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def a(lowercase__ , lowercase__ = False ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
snake_case_ = f"""Expected string as input, found {type(lowercase__ )}"""
raise ValueError(lowercase__ )
if not isinstance(lowercase__ , lowercase__ ):
snake_case_ = f"""Expected boolean as use_pascal parameter, found {type(lowercase__ )}"""
raise ValueError(lowercase__ )
snake_case_ = input_str.split('_' )
snake_case_ = 0 if use_pascal else 1
snake_case_ = words[start_index:]
snake_case_ = [word[0].upper() + word[1:] for word in words_to_capitalize]
snake_case_ = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 46
|
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ = int(factor * num_class_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 )
os.makedirs(f"""{class_data_dir}/images""" , exist_ok=lowercase__ )
if len(list(Path(f"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
snake_case_ = client.query(text=lowercase__ )
if len(lowercase__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
snake_case_ = int(factor * num_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 , )
snake_case_ = 0
snake_case_ = 0
snake_case_ = tqdm(desc='downloading real regularization images' , total=lowercase__ )
with open(f"""{class_data_dir}/caption.txt""" , 'w' ) as fa, open(f"""{class_data_dir}/urls.txt""" , 'w' ) as fa, open(
f"""{class_data_dir}/images.txt""" , 'w' ) as fa:
while total < num_class_images:
snake_case_ = class_images[count]
count += 1
try:
snake_case_ = requests.get(images['url'] )
if img.status_code == 200:
snake_case_ = Image.open(BytesIO(img.content ) )
with open(f"""{class_data_dir}/images/{total}.jpg""" , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def a():
'''simple docstring'''
snake_case_ = argparse.ArgumentParser('' , add_help=lowercase__ )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--class_data_dir' , help='path to save images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--num_class_images' , help='number of images to download' , default=200 , type=lowercase__ )
return parser.parse_args()
if __name__ == "__main__":
A = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 46
| 1
|
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Initialise PyTorch model
snake_case_ = LxmertConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
snake_case_ = LxmertForPreTraining(lowercase__ )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowercase__ )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 46
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
A = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 46
| 1
|
import re
def a(lowercase__ ):
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
|
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__A = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(default=__snake_case , metadata={"""help""": """The input training data file (a text file)."""} )
__A = field(
default=__snake_case , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__A = field(
default=__snake_case , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
if self.train_file is not None:
snake_case_ = self.train_file.split('.' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
snake_case_ = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = 42
__A = True
__A = None
__A = None
def __call__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 'label' if 'label' in features[0].keys() else 'labels'
snake_case_ = [feature.pop(__UpperCamelCase ) for feature in features]
snake_case_ = len(__UpperCamelCase )
snake_case_ = len(features[0]['input_ids'] )
snake_case_ = [
[{k: v[i] for k, v in feature.items()} for i in range(__UpperCamelCase )] for feature in features
]
snake_case_ = list(chain(*__UpperCamelCase ) )
snake_case_ = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
# Un-flatten
snake_case_ = {k: v.view(__UpperCamelCase , __UpperCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
snake_case_ = torch.tensor(__UpperCamelCase , dtype=torch.intaa )
return batch
def a():
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_ , snake_case_ , snake_case_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ , snake_case_ , snake_case_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_swag' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
datasets.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
snake_case_ = {}
if data_args.train_file is not None:
snake_case_ = data_args.train_file
if data_args.validation_file is not None:
snake_case_ = data_args.validation_file
snake_case_ = data_args.train_file.split('.' )[-1]
snake_case_ = load_dataset(
lowercase__ , data_files=lowercase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
snake_case_ = load_dataset(
'swag' , 'regular' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
snake_case_ = [f"""ending{i}""" for i in range(4 )]
snake_case_ = 'sent1'
snake_case_ = 'sent2'
if data_args.max_seq_length is None:
snake_case_ = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'
' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'
' override this default with `--block_size xxx`.' )
snake_case_ = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case_ = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowercase__ ):
snake_case_ = [[context] * 4 for context in examples[context_name]]
snake_case_ = examples[question_header_name]
snake_case_ = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase__ )
]
# Flatten out
snake_case_ = list(chain(*lowercase__ ) )
snake_case_ = list(chain(*lowercase__ ) )
# Tokenize
snake_case_ = tokenizer(
lowercase__ , lowercase__ , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowercase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case_ = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_train_samples )
snake_case_ = train_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
snake_case_ = train_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case_ = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_eval_samples )
snake_case_ = eval_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
snake_case_ = eval_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
snake_case_ = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowercase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowercase__ ):
snake_case_ , snake_case_ = eval_predictions
snake_case_ = np.argmax(lowercase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
snake_case_ = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , compute_metrics=lowercase__ , )
# Training
if training_args.do_train:
snake_case_ = None
if training_args.resume_from_checkpoint is not None:
snake_case_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ = last_checkpoint
snake_case_ = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case_ = train_result.metrics
snake_case_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase__ )
)
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('train' , lowercase__ )
trainer.save_metrics('train' , lowercase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case_ = trainer.evaluate()
snake_case_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase__ )
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
snake_case_ = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'multiple-choice',
'dataset_tags': 'swag',
'dataset_args': 'regular',
'dataset': 'SWAG',
'language': 'en',
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 46
| 1
|
from __future__ import annotations
def a(lowercase__ ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(lowercase__ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(lowercase__ ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ASTForAudioClassification',
'ASTModel',
'ASTPreTrainedModel',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['ASTFeatureExtractor']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 1
|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
|
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
else:
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
A = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 46
| 1
|
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A = [ord(letter) for letter in string.ascii_lowercase]
A = {ord(char) for char in VALID_CHARS}
A = ["the", "be", "to", "of", "and", "in", "that", "have"]
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = ""
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
for keychar, cipherchar in zip(cycle(lowercase__ ) , lowercase__ ):
snake_case_ = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(lowercase__ )
return decoded
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
for key in product(lowercase__ , repeat=3 ):
snake_case_ = try_key(lowercase__ , lowercase__ )
if encoded is not None:
possibles.append(lowercase__ )
return possibles
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
return [possible for possible in possibles if common_word in possible.lower()]
def a(lowercase__ = "p059_cipher.txt" ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = Path(lowercase__ ).parent.joinpath(lowercase__ ).read_text(encoding='utf-8' )
snake_case_ = [int(lowercase__ ) for number in data.strip().split(',' )]
snake_case_ = filter_valid_chars(lowercase__ )
for common_word in COMMON_WORDS:
snake_case_ = filter_common_word(lowercase__ , lowercase__ )
if len(lowercase__ ) == 1:
break
snake_case_ = possibles[0]
return sum(ord(lowercase__ ) for char in decoded_text )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 46
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
__A = """bit"""
__A = ["""preactivation""", """bottleneck"""]
__A = ["""SAME""", """VALID"""]
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=64 , __UpperCamelCase=[2_56, 5_12, 10_24, 20_48] , __UpperCamelCase=[3, 4, 6, 3] , __UpperCamelCase="preactivation" , __UpperCamelCase="relu" , __UpperCamelCase=None , __UpperCamelCase=32 , __UpperCamelCase=0.0 , __UpperCamelCase=False , __UpperCamelCase=32 , __UpperCamelCase=1 , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
snake_case_ = global_padding.upper()
else:
raise ValueError(f"""Padding strategy {global_padding} not supported""" )
snake_case_ = num_channels
snake_case_ = embedding_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = layer_type
snake_case_ = hidden_act
snake_case_ = global_padding
snake_case_ = num_groups
snake_case_ = drop_path_rate
snake_case_ = embedding_dynamic_padding
snake_case_ = output_stride
snake_case_ = width_factor
snake_case_ = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(__UpperCamelCase ) + 1 )]
snake_case_ , snake_case_ = get_aligned_output_features_output_indices(
out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
| 46
| 1
|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
snake_case_ = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = DDIMScheduler()
snake_case_ = self.dummy_vq_model
snake_case_ = LDMPipeline(unet=__UpperCamelCase , vqvae=__UpperCamelCase , scheduler=__UpperCamelCase )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' , return_dict=__UpperCamelCase )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=5 , output_type='numpy' ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 46
| 1
|
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
f"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
f"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
f"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
f"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = []
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token') )
return token
def a():
'''simple docstring'''
snake_case_ = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 'imagenet-1k-id2label.json'
snake_case_ = 1000
snake_case_ = 'huggingface/label-files'
snake_case_ = num_labels
snake_case_ = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' ) ) , 'r' ) )
snake_case_ = {int(lowercase__ ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = snake_case_ = CvtConfig(num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
snake_case_ = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
snake_case_ = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
snake_case_ = [2, 2, 20]
snake_case_ = [3, 12, 16]
snake_case_ = [192, 768, 1024]
snake_case_ = CvtForImageClassification(lowercase__ )
snake_case_ = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
snake_case_ = image_size
snake_case_ = torch.load(lowercase__ , map_location=torch.device('cpu' ) )
snake_case_ = OrderedDict()
snake_case_ = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
snake_case_ = list_of_state_dict + cls_token(lowercase__ )
snake_case_ = list_of_state_dict + embeddings(lowercase__ )
for cnt in range(config.depth[idx] ):
snake_case_ = list_of_state_dict + attention(lowercase__ , lowercase__ )
snake_case_ = list_of_state_dict + final()
for gg in list_of_state_dict:
print(lowercase__ )
for i in range(len(lowercase__ ) ):
snake_case_ = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(lowercase__ )
model.save_pretrained(lowercase__ )
image_processor.save_pretrained(lowercase__ )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=384,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
A = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 46
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = LEDConfig
__A = {}
__A = """gelu"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=20 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=4 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
snake_case_ = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
snake_case_ = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
snake_case_ = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
snake_case_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
snake_case_ = tf.concat([input_ids, eos_tensor] , axis=1 )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
snake_case_ = prepare_led_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = tf.concat(
[tf.zeros_like(__UpperCamelCase )[:, :-1], tf.ones_like(__UpperCamelCase )[:, -1:]] , axis=-1 , )
snake_case_ = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFLEDModel(config=__UpperCamelCase ).get_decoder()
snake_case_ = inputs_dict['input_ids']
snake_case_ = input_ids[:1, :]
snake_case_ = inputs_dict['attention_mask'][:1, :]
snake_case_ = 1
# first forward pass
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
snake_case_ = tf.concat([input_ids, next_tokens] , axis=-1 )
snake_case_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
snake_case_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
snake_case_ = output_from_no_past[:, -3:, random_slice_idx]
snake_case_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-3 )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
__A = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
__A = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A = True
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = tf.zeros_like(inputs_dict['attention_mask'] )
snake_case_ = 2
snake_case_ = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['global_attention_mask'] , )
snake_case_ = True
snake_case_ = self.model_tester.seq_length
snake_case_ = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__UpperCamelCase ):
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__UpperCamelCase ):
snake_case_ = [t.numpy() for t in outputs.encoder_attentions]
snake_case_ = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = len(__UpperCamelCase )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_decoder_attentions_output(__UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def a(lowercase__ ):
'''simple docstring'''
return tf.constant(lowercase__ , dtype=tf.intaa )
A = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, 7_68)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 , rtol=1E-3 )
| 46
| 1
|
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = ScoreSdeVeScheduler()
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase , return_dict=__UpperCamelCase )[
0
]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'google/ncsnpp-church-256'
snake_case_ = UNetaDModel.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 46
|
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replace(' ' , '' )
# Strings of different lengths are not anagrams
if len(lowercase__ ) != len(lowercase__ ):
return False
# Default values for count should be 0
snake_case_ = defaultdict(lowercase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowercase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
A = input('Enter the first string ').strip()
A = input('Enter the second string ').strip()
A = check_anagrams(input_a, input_b)
print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 46
| 1
|
import os
import sys
import unittest
A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
A = os.path.join(git_repo_path, 'src', 'diffusers')
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = find_backend(' if not is_torch_available():' )
self.assertEqual(__UpperCamelCase , 'torch' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
snake_case_ = find_backend(' if not (is_torch_available() and is_transformers_available()):' )
self.assertEqual(__UpperCamelCase , 'torch_and_transformers' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
snake_case_ = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):' )
self.assertEqual(__UpperCamelCase , 'torch_and_transformers_and_onnx' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , __UpperCamelCase )
self.assertIn('torch_and_transformers' , __UpperCamelCase )
self.assertIn('flax_and_transformers' , __UpperCamelCase )
self.assertIn('torch_and_transformers_and_onnx' , __UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'] )
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'] )
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'] )
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'] )
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'] )
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'] )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(__UpperCamelCase , '\nCONSTANT = None\n' )
snake_case_ = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
__UpperCamelCase , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
snake_case_ = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
snake_case_ = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
snake_case_ = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , __UpperCamelCase )
| 46
|
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = ScoreSdeVeScheduler()
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase , return_dict=__UpperCamelCase )[
0
]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'google/ncsnpp-church-256'
snake_case_ = UNetaDModel.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 46
| 1
|
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {'vocab_file': 'spiece.model'}
A = {
'vocab_file': {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
}
}
A = {
'albert-base-v1': 512,
'albert-large-v1': 512,
'albert-xlarge-v1': 512,
'albert-xxlarge-v1': 512,
'albert-base-v2': 512,
'albert-large-v2': 512,
'albert-xlarge-v2': 512,
'albert-xxlarge-v2': 512,
}
A = '▁'
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , __UpperCamelCase , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase="[CLS]" , __UpperCamelCase="[SEP]" , __UpperCamelCase="<unk>" , __UpperCamelCase="[SEP]" , __UpperCamelCase="<pad>" , __UpperCamelCase="[CLS]" , __UpperCamelCase="[MASK]" , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
snake_case_ = (
AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase , normalized=__UpperCamelCase )
if isinstance(__UpperCamelCase , __UpperCamelCase )
else mask_token
)
snake_case_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCamelCase , remove_space=__UpperCamelCase , keep_accents=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , pad_token=__UpperCamelCase , cls_token=__UpperCamelCase , mask_token=__UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCamelCase , )
snake_case_ = do_lower_case
snake_case_ = remove_space
snake_case_ = keep_accents
snake_case_ = vocab_file
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCamelCase )
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.sp_model )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = {self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
snake_case_ = self.__dict__.copy()
snake_case_ = None
return state
def __setstate__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
snake_case_ = {}
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
if self.remove_space:
snake_case_ = ' '.join(inputs.strip().split() )
else:
snake_case_ = inputs
snake_case_ = outputs.replace('``' , '"' ).replace('\'\'' , '"' )
if not self.keep_accents:
snake_case_ = unicodedata.normalize('NFKD' , __UpperCamelCase )
snake_case_ = ''.join([c for c in outputs if not unicodedata.combining(__UpperCamelCase )] )
if self.do_lower_case:
snake_case_ = outputs.lower()
return outputs
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.preprocess_text(__UpperCamelCase )
snake_case_ = self.sp_model.encode(__UpperCamelCase , out_type=__UpperCamelCase )
snake_case_ = []
for piece in pieces:
if len(__UpperCamelCase ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit():
snake_case_ = self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCamelCase , '' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
snake_case_ = cur_pieces[1:]
else:
snake_case_ = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__UpperCamelCase )
else:
new_pieces.append(__UpperCamelCase )
return new_pieces
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return self.sp_model.PieceToId(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return self.sp_model.IdToPiece(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = []
snake_case_ = ''
snake_case_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__UpperCamelCase ) + token
snake_case_ = True
snake_case_ = []
else:
current_sub_tokens.append(__UpperCamelCase )
snake_case_ = False
out_string += self.sp_model.decode(__UpperCamelCase )
return out_string.strip()
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
snake_case_ = [self.sep_token_id]
snake_case_ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase )
if token_ids_a is not None:
return [1] + ([0] * len(__UpperCamelCase )) + [1] + ([0] * len(__UpperCamelCase )) + [1]
return [1] + ([0] * len(__UpperCamelCase )) + [1]
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
snake_case_ = [self.sep_token_id]
snake_case_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
if not os.path.isdir(__UpperCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case_ = os.path.join(
__UpperCamelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCamelCase , 'wb' ) as fi:
snake_case_ = self.sp_model.serialized_model_proto()
fi.write(__UpperCamelCase )
return (out_vocab_file,)
| 46
|
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
@register_to_config
def __init__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None ):
"""simple docstring"""
super().__init__()
snake_case_ = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
snake_case_ = torch.zeros(__UpperCamelCase , __UpperCamelCase )
else:
snake_case_ = None
snake_case_ = torch.nn.Parameter(__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
super().__init__()
self.register_modules(
vqvae=__UpperCamelCase , transformer=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , scheduler=__UpperCamelCase , learned_classifier_free_sampling_embeddings=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else 1
# get prompt text embeddings
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
snake_case_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
snake_case_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
snake_case_ = text_input_ids[:, : self.tokenizer.model_max_length]
snake_case_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
snake_case_ = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate text embeddings for each generation per prompt
snake_case_ = prompt_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
snake_case_ = self.learned_classifier_free_sampling_embeddings.embeddings
snake_case_ = negative_prompt_embeds.unsqueeze(0 ).repeat(__UpperCamelCase , 1 , 1 )
else:
snake_case_ = [''] * batch_size
snake_case_ = text_input_ids.shape[-1]
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors='pt' , )
snake_case_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
snake_case_ = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case_ = negative_prompt_embeds.shape[1]
snake_case_ = negative_prompt_embeds.repeat(1 , __UpperCamelCase , 1 )
snake_case_ = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __UpperCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case_ = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , __UpperCamelCase , __UpperCamelCase = 1_00 , __UpperCamelCase = 5.0 , __UpperCamelCase = 1.0 , __UpperCamelCase = 1 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = "pil" , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = 1 , ):
"""simple docstring"""
if isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = 1
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = len(__UpperCamelCase )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase )}""" )
snake_case_ = batch_size * num_images_per_prompt
snake_case_ = guidance_scale > 1.0
snake_case_ = self._encode_prompt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__UpperCamelCase , __UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__UpperCamelCase )}.""" )
# get the initial completely masked latents unless the user supplied it
snake_case_ = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
snake_case_ = self.transformer.num_vector_embeds - 1
snake_case_ = torch.full(__UpperCamelCase , __UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
f""" {self.transformer.num_vector_embeds - 1} (inclusive).""" )
snake_case_ = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__UpperCamelCase , device=self.device )
snake_case_ = self.scheduler.timesteps.to(self.device )
snake_case_ = latents
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
snake_case_ = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
snake_case_ = self.transformer(__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , timestep=__UpperCamelCase ).sample
if do_classifier_free_guidance:
snake_case_ , snake_case_ = model_output.chunk(2 )
snake_case_ = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__UpperCamelCase , dim=1 , keepdim=__UpperCamelCase )
snake_case_ = self.truncate(__UpperCamelCase , __UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
snake_case_ = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(__UpperCamelCase , timestep=__UpperCamelCase , sample=__UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = self.vqvae.config.vq_embed_dim
snake_case_ = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
snake_case_ = self.vqvae.quantize.get_codebook_entry(__UpperCamelCase , shape=__UpperCamelCase )
snake_case_ = self.vqvae.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase ).sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ , snake_case_ = torch.sort(__UpperCamelCase , 1 , descending=__UpperCamelCase )
snake_case_ = torch.exp(__UpperCamelCase )
snake_case_ = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
snake_case_ = torch.full_like(keep_mask[:, 0:1, :] , __UpperCamelCase )
snake_case_ = torch.cat((all_true, keep_mask) , dim=1 )
snake_case_ = keep_mask[:, :-1, :]
snake_case_ = keep_mask.gather(1 , indices.argsort(1 ) )
snake_case_ = log_p_x_0.clone()
snake_case_ = -torch.inf # -inf = log(0)
return rv
| 46
| 1
|
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
A = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = SpeechTaTokenizer
__A = False
__A = True
def __lowerCAmelCase ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ = SpeechTaTokenizer(__UpperCamelCase )
snake_case_ = AddedToken('<mask>' , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase )
snake_case_ = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token} )
tokenizer.add_tokens(['<ctc_blank>'] )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 'this is a test'
snake_case_ = 'this is a test'
return input_text, output_text
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=False , __UpperCamelCase=20 , __UpperCamelCase=5 ):
"""simple docstring"""
snake_case_ , snake_case_ = self.get_input_output_texts(__UpperCamelCase )
snake_case_ = tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
snake_case_ = tokenizer.decode(__UpperCamelCase , clean_up_tokenization_spaces=__UpperCamelCase )
return text, ids
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = '<pad>'
snake_case_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase ) , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-4] , 'œ' )
self.assertEqual(vocab_keys[-2] , '<mask>' )
self.assertEqual(vocab_keys[-1] , '<ctc_blank>' )
self.assertEqual(len(__UpperCamelCase ) , 81 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_tokenizers(do_lower_case=__UpperCamelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
snake_case_ = tokenizer.vocab_size
snake_case_ = len(__UpperCamelCase )
self.assertNotEqual(__UpperCamelCase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
snake_case_ = ['aaaaa bbbbbb', 'cccccccccdddddddd']
snake_case_ = tokenizer.add_tokens(__UpperCamelCase )
snake_case_ = tokenizer.vocab_size
snake_case_ = len(__UpperCamelCase )
self.assertNotEqual(__UpperCamelCase , 0 )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
self.assertEqual(__UpperCamelCase , len(__UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , all_size + len(__UpperCamelCase ) )
snake_case_ = tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l' , add_special_tokens=__UpperCamelCase )
self.assertGreaterEqual(len(__UpperCamelCase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
snake_case_ = {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
snake_case_ = tokenizer.add_special_tokens(__UpperCamelCase )
snake_case_ = tokenizer.vocab_size
snake_case_ = len(__UpperCamelCase )
self.assertNotEqual(__UpperCamelCase , 0 )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
self.assertEqual(__UpperCamelCase , len(__UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , all_size_a + len(__UpperCamelCase ) )
snake_case_ = tokenizer.encode(
'>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l' , add_special_tokens=__UpperCamelCase )
self.assertGreaterEqual(len(__UpperCamelCase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = tokenizer.tokenize('This is a test' )
# fmt: off
self.assertListEqual(__UpperCamelCase , [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't'] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
snake_case_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__UpperCamelCase , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] )
snake_case_ = tokenizer.convert_tokens_to_ids(__UpperCamelCase )
# fmt: off
self.assertListEqual(__UpperCamelCase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
snake_case_ = tokenizer.convert_ids_to_tokens(__UpperCamelCase )
self.assertListEqual(
__UpperCamelCase , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [
'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
'general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
'Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '
'models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.',
'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
'conditioning on both left and right context in all layers.',
'The quick brown fox jumps over the lazy dog.',
]
# fmt: off
snake_case_ = {
'input_ids': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'attention_mask': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCamelCase , model_name='microsoft/speecht5_asr' , revision='c5ef64c71905caeccde0e4462ef3f9077224c524' , sequences=__UpperCamelCase , )
| 46
|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'neck_hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'num_attention_heads' ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=6_40 , __UpperCamelCase=4 , __UpperCamelCase="silu" , __UpperCamelCase=3 , __UpperCamelCase=32 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = last_hidden_size
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = conv_kernel_size
snake_case_ = output_stride
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = classifier_dropout_prob
snake_case_ = use_labels
snake_case_ = is_training
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MobileViTModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForSemanticSegmentation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__A = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTModelTester(self )
snake_case_ = MobileViTConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = 5
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case_ = 2
for i in range(len(__UpperCamelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MobileViTModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits
# verify the logits
snake_case_ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=__UpperCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits.detach().cpu()
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase , target_sizes=[(50, 60)] )
snake_case_ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase )
snake_case_ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
| 46
| 1
|
import math
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = [True] * n
snake_case_ = False
snake_case_ = False
snake_case_ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
snake_case_ = i * 2
while index < n:
snake_case_ = False
snake_case_ = index + i
snake_case_ = [2]
for i in range(3 , lowercase__ , 2 ):
if is_prime[i]:
primes.append(lowercase__ )
return primes
def a(lowercase__ = 999966663333 ):
'''simple docstring'''
snake_case_ = math.floor(math.sqrt(lowercase__ ) ) + 100
snake_case_ = prime_sieve(lowercase__ )
snake_case_ = 0
snake_case_ = 0
snake_case_ = primes[prime_index]
while (last_prime**2) <= limit:
snake_case_ = primes[prime_index + 1]
snake_case_ = last_prime**2
snake_case_ = next_prime**2
# Get numbers divisible by lps(current)
snake_case_ = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
snake_case_ = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
snake_case_ = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
snake_case_ = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 46
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 46
| 1
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 46
|
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
snake_case_ = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
snake_case_ = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
snake_case_ = tf_top_k_top_p_filtering(__UpperCamelCase , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
snake_case_ = output[output != -float('inf' )]
snake_case_ = tf.cast(
tf.where(tf.not_equal(__UpperCamelCase , tf.constant(-float('inf' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-12 )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase , __snake_case ):
"""simple docstring"""
if is_tf_available():
__A = {
"""AutoModelForCausalLM""": TFAutoModelForCausalLM,
"""AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
"""AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
"""AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
"""LogitsProcessorList""": TFLogitsProcessorList,
"""MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
"""create_tensor_fn""": tf.convert_to_tensor,
"""floats_tensor""": floats_tensor,
"""return_tensors""": """tf""",
}
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 2
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='input_ids' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2, 0], [1_02, 1_03]]
snake_case_ = [[1, 0], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for batch_size in range(1 , len(__UpperCamelCase ) + 1 ):
snake_case_ = {
'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 1
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='input_ids' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2], [1_02, 1_03]]
snake_case_ = [[1], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for input_row in range(len(__UpperCamelCase ) ):
snake_case_ = {
'input_ids': tf.constant([dummy_input_ids[input_row]] ),
'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
@require_tensorflow_text
def __lowerCAmelCase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='google/flan-t5-small' , filename='spiece.model' , local_dir=__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
super().__init__()
snake_case_ = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(__UpperCamelCase , 'spiece.model' ) , 'rb' ).read() )
snake_case_ = TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )
def __lowerCAmelCase ( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.tokenizer.tokenize(__UpperCamelCase )
snake_case_ , snake_case_ = text.pad_model_inputs(
__UpperCamelCase , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
snake_case_ = self.model.generate(input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase )
return self.tokenizer.detokenize(__UpperCamelCase )
snake_case_ = CompleteSentenceTransformer()
snake_case_ = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='inputs' )
snake_case_ = complete_model(__UpperCamelCase )
snake_case_ = tf.keras.Model(__UpperCamelCase , __UpperCamelCase )
keras_model.save(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = {
'do_sample': True,
'num_beams': 1,
'top_p': 0.7,
'top_k': 10,
'temperature': 0.7,
}
snake_case_ = 14
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 'Hello, my dog is cute and'
snake_case_ = tokenizer(__UpperCamelCase , return_tensors='tf' )
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
snake_case_ = [6_38, 1_98]
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = 'Hugging Face is a technology company based in New York and Paris.'
snake_case_ = bart_tokenizer(__UpperCamelCase , return_tensors='tf' ).input_ids
snake_case_ = TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=None , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase , foo='bar' ).numpy()
self.assertTrue(np.array_equal(__UpperCamelCase , __UpperCamelCase ) )
class SCREAMING_SNAKE_CASE ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeEncoder(bart_model.config , bart_model.model.shared )
snake_case_ = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
with self.assertRaises(__UpperCamelCase ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(__UpperCamelCase , foo='bar' )
| 46
| 1
|
import os
def a():
'''simple docstring'''
with open(os.path.dirname(lowercase__ ) + '/p022_names.txt' ) as file:
snake_case_ = str(file.readlines()[0] )
snake_case_ = names.replace('"' , '' ).split(',' )
names.sort()
snake_case_ = 0
snake_case_ = 0
for i, name in enumerate(lowercase__ ):
for letter in name:
name_score += ord(lowercase__ ) - 64
total_score += (i + 1) * name_score
snake_case_ = 0
return total_score
if __name__ == "__main__":
print(solution())
| 46
|
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=2 , __UpperCamelCase=8 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=16 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=36 , __UpperCamelCase="gelu" , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_config()
snake_case_ = 3_00
return config
def __lowerCAmelCase ( self ):
"""simple docstring"""
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = self.prepare_config_and_inputs()
snake_case_ = True
snake_case_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = MraModel(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , )
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , start_positions=__UpperCamelCase , end_positions=__UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_choices
snake_case_ = MraForMultipleChoice(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
__A = False
__A = False
__A = False
__A = False
__A = ()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MraModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@unittest.skip(reason='MRA does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModel.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = torch.Size((1, 2_56, 7_68) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 2_56, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
snake_case_ = torch.arange(40_96 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 40_96, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
| 1
|
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __init__( self , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['bs4'] )
super().__init__(**__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = []
snake_case_ = []
snake_case_ = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
snake_case_ = parent.find_all(child.name , recursive=__UpperCamelCase )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(__UpperCamelCase ) else next(i for i, s in enumerate(__UpperCamelCase , 1 ) if s is child ) )
snake_case_ = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = BeautifulSoup(__UpperCamelCase , 'html.parser' )
snake_case_ = []
snake_case_ = []
snake_case_ = []
for element in html_code.descendants:
if type(__UpperCamelCase ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
snake_case_ = html.unescape(__UpperCamelCase ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(__UpperCamelCase )
snake_case_ , snake_case_ = self.xpath_soup(__UpperCamelCase )
stringaxtag_seq.append(__UpperCamelCase )
stringaxsubs_seq.append(__UpperCamelCase )
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError('Number of doc strings and xtags does not correspond' )
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError('Number of doc strings and xsubs does not correspond' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = ''
for tagname, subs in zip(__UpperCamelCase , __UpperCamelCase ):
xpath += f"""/{tagname}"""
if subs != 0:
xpath += f"""[{subs}]"""
return xpath
def __call__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = False
# Check that strings has a valid type
if isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = True
elif isinstance(__UpperCamelCase , (list, tuple) ):
if len(__UpperCamelCase ) == 0 or isinstance(html_strings[0] , __UpperCamelCase ):
snake_case_ = True
if not valid_strings:
raise ValueError(
'HTML strings must of type `str`, `List[str]` (batch of examples), '
f"""but is of type {type(__UpperCamelCase )}.""" )
snake_case_ = bool(isinstance(__UpperCamelCase , (list, tuple) ) and (isinstance(html_strings[0] , __UpperCamelCase )) )
if not is_batched:
snake_case_ = [html_strings]
# Get nodes + xpaths
snake_case_ = []
snake_case_ = []
for html_string in html_strings:
snake_case_ , snake_case_ , snake_case_ = self.get_three_from_single(__UpperCamelCase )
nodes.append(__UpperCamelCase )
snake_case_ = []
for node, tag_list, sub_list in zip(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = self.construct_xpath(__UpperCamelCase , __UpperCamelCase )
xpath_strings.append(__UpperCamelCase )
xpaths.append(__UpperCamelCase )
# return as Dict
snake_case_ = {'nodes': nodes, 'xpaths': xpaths}
snake_case_ = BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
return encoded_inputs
| 46
|
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
snake_case_ = TapasConfig.from_json_file(lowercase__ )
# set absolute/relative position embeddings parameter
snake_case_ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WTQ":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = True
# hparam_utils.py hparams
snake_case_ = 0.66_4694
snake_case_ = 0.20_7951
snake_case_ = 0.12_1194
snake_case_ = True
snake_case_ = True
snake_case_ = False
snake_case_ = 0.035_2513
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = False
# hparam_utils.py hparams
snake_case_ = 36.4519
snake_case_ = 0.90_3421
snake_case_ = 222.088
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 0.76_3141
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "TABFACT":
snake_case_ = TapasForSequenceClassification(config=lowercase__ )
elif task == "MLM":
snake_case_ = TapasForMaskedLM(config=lowercase__ )
elif task == "INTERMEDIATE_PRETRAINING":
snake_case_ = TapasModel(config=lowercase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
snake_case_ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' , model_max_length=512 )
tokenizer.save_pretrained(lowercase__ )
print('Used relative position embeddings:' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
)
parser.add_argument(
'--reset_position_index_per_cell',
default=False,
action='store_true',
help='Whether to use relative position embeddings or not. Defaults to True.',
)
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--tapas_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained TAPAS model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 46
| 1
|
from typing import Dict, Optional
import numpy as np
import datasets
A = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
A = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
A = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = False , ):
'''simple docstring'''
if label_map is not None:
for old_id, new_id in label_map.items():
snake_case_ = new_id
# turn into Numpy arrays
snake_case_ = np.array(lowercase__ )
snake_case_ = np.array(lowercase__ )
if reduce_labels:
snake_case_ = 255
snake_case_ = label - 1
snake_case_ = 255
snake_case_ = label != ignore_index
snake_case_ = np.not_equal(lowercase__ , lowercase__ )
snake_case_ = pred_label[mask]
snake_case_ = np.array(lowercase__ )[mask]
snake_case_ = pred_label[pred_label == label]
snake_case_ = np.histogram(lowercase__ , bins=lowercase__ , range=(0, num_labels - 1) )[0]
snake_case_ = np.histogram(lowercase__ , bins=lowercase__ , range=(0, num_labels - 1) )[0]
snake_case_ = np.histogram(lowercase__ , bins=lowercase__ , range=(0, num_labels - 1) )[0]
snake_case_ = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = False , ):
'''simple docstring'''
snake_case_ = np.zeros((num_labels,) , dtype=np.floataa )
snake_case_ = np.zeros((num_labels,) , dtype=np.floataa )
snake_case_ = np.zeros((num_labels,) , dtype=np.floataa )
snake_case_ = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(lowercase__ , lowercase__ ):
snake_case_ , snake_case_ , snake_case_ , snake_case_ = intersect_and_union(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = False , ):
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ , snake_case_ = total_intersect_and_union(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# compute metrics
snake_case_ = {}
snake_case_ = total_area_intersect.sum() / total_area_label.sum()
snake_case_ = total_area_intersect / total_area_union
snake_case_ = total_area_intersect / total_area_label
snake_case_ = np.nanmean(lowercase__ )
snake_case_ = np.nanmean(lowercase__ )
snake_case_ = all_acc
snake_case_ = iou
snake_case_ = acc
if nan_to_num is not None:
snake_case_ = {metric: np.nan_to_num(lowercase__ , nan=lowercase__ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
} ) , reference_urls=[
'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
] , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , ):
"""simple docstring"""
snake_case_ = mean_iou(
results=__UpperCamelCase , gt_seg_maps=__UpperCamelCase , num_labels=__UpperCamelCase , ignore_index=__UpperCamelCase , nan_to_num=__UpperCamelCase , label_map=__UpperCamelCase , reduce_labels=__UpperCamelCase , )
return iou_result
| 46
|
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=16 , __UpperCamelCase=[1, 2, 1] , __UpperCamelCase=[2, 2, 4] , __UpperCamelCase=2 , __UpperCamelCase=2.0 , __UpperCamelCase=True , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase="gelu" , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=8 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = embed_dim
snake_case_ = depths
snake_case_ = num_heads
snake_case_ = window_size
snake_case_ = mlp_ratio
snake_case_ = qkv_bias
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = drop_path_rate
snake_case_ = hidden_act
snake_case_ = use_absolute_embeddings
snake_case_ = patch_norm
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = is_training
snake_case_ = scope
snake_case_ = use_labels
snake_case_ = type_sequence_label_size
snake_case_ = encoder_stride
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
snake_case_ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaForMaskedImageModeling(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case_ = 1
snake_case_ = SwinvaForMaskedImageModeling(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.type_sequence_label_size
snake_case_ = SwinvaForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
__A = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , embed_dim=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
snake_case_ = len(self.model_tester.depths )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = config.window_size**2
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
snake_case_ = len(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
if hasattr(self.model_tester , 'num_hidden_states_types' ):
snake_case_ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case_ = 2
self.assertEqual(out_len + added_hidden_states , len(__UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# Swinv2 has a different seq_length
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
snake_case_ = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = reshaped_hidden_states[0].shape
snake_case_ = (
reshaped_hidden_states[0].view(__UpperCamelCase , __UpperCamelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = SwinvaModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = _config_zero_init(__UpperCamelCase )
for model_class in self.all_model_classes:
snake_case_ = model_class(config=__UpperCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
| 1
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
'''simple docstring'''
snake_case_ = {
'7z': (seven_zip_file, SevenZipExtractor),
'bz2': (bza_file, BzipaExtractor),
'gzip': (gz_file, GzipExtractor),
'lz4': (lza_file, LzaExtractor),
'tar': (tar_file, TarExtractor),
'xz': (xz_file, XzExtractor),
'zip': (zip_file, ZipExtractor),
'zstd': (zstd_file, ZstdExtractor),
}
snake_case_ , snake_case_ = input_paths_and_base_extractors[compression_format]
if input_path is None:
snake_case_ = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(lowercase__ )
assert base_extractor.is_extractable(lowercase__ )
snake_case_ = tmp_path / ('extracted' if is_archive else 'extracted.txt')
base_extractor.extract(lowercase__ , lowercase__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case_ = file_path.read_text(encoding='utf-8' )
else:
snake_case_ = output_path.read_text(encoding='utf-8' )
snake_case_ = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
'''simple docstring'''
snake_case_ = {
'7z': seven_zip_file,
'bz2': bza_file,
'gzip': gz_file,
'lz4': lza_file,
'tar': tar_file,
'xz': xz_file,
'zip': zip_file,
'zstd': zstd_file,
}
snake_case_ = input_paths[compression_format]
if input_path is None:
snake_case_ = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(lowercase__ )
snake_case_ = Extractor.infer_extractor_format(lowercase__ )
assert extractor_format is not None
snake_case_ = tmp_path / ('extracted' if is_archive else 'extracted.txt')
Extractor.extract(lowercase__ , lowercase__ , lowercase__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case_ = file_path.read_text(encoding='utf-8' )
else:
snake_case_ = output_path.read_text(encoding='utf-8' )
snake_case_ = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
import tarfile
snake_case_ = tmp_path / 'data_dot_dot'
directory.mkdir()
snake_case_ = directory / 'tar_file_with_dot_dot.tar'
with tarfile.TarFile(lowercase__ , 'w' ) as f:
f.add(lowercase__ , arcname=os.path.join('..' , text_file.name ) )
return path
@pytest.fixture
def a(lowercase__ ):
'''simple docstring'''
import tarfile
snake_case_ = tmp_path / 'data_sym_link'
directory.mkdir()
snake_case_ = directory / 'tar_file_with_sym_link.tar'
os.symlink('..' , directory / 'subdir' , target_is_directory=lowercase__ )
with tarfile.TarFile(lowercase__ , 'w' ) as f:
f.add(str(directory / 'subdir' ) , arcname='subdir' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'insecure_tar_file, error_log' , [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')] , )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = {
'tar_file_with_dot_dot': tar_file_with_dot_dot,
'tar_file_with_sym_link': tar_file_with_sym_link,
}
snake_case_ = insecure_tar_files[insecure_tar_file]
snake_case_ = tmp_path / 'extracted'
TarExtractor.extract(lowercase__ , lowercase__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a(lowercase__ ):
'''simple docstring'''
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
snake_case_ = tmpdir / 'not_a_zip_file'
# From: https://github.com/python/cpython/pull/5053
snake_case_ = (
B'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
B'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
B'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
B'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
)
with not_a_zip_file.open('wb' ) as f:
f.write(lowercase__ )
assert zipfile.is_zipfile(str(lowercase__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(lowercase__ ) # but we're right
| 46
|
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A = parser.parse_args()
A = 'cpu'
A = 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'
A = 'path-to-your-trained-model'
A = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
A = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A = pipe.to(device)
# to channels last
A = pipe.unet.to(memory_format=torch.channels_last)
A = pipe.vae.to(memory_format=torch.channels_last)
A = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
A = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
A = torch.randn(2, 4, 64, 64)
A = torch.rand(1) * 999
A = torch.randn(2, 77, 768)
A = (sample, timestep, encoder_hidden_status)
try:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
A = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
A = 666
A = torch.Generator(device).manual_seed(seed)
A = {'generator': generator}
if args.steps is not None:
A = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
A = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('generated.png')
| 46
| 1
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = (
"""This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
"""It takes two arguments named `image` which should be the original image, and `label` which should be a text """
"""describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
)
__A = """CIDAS/clipseg-rd64-refined"""
__A = """image_segmenter"""
__A = CLIPSegForImageSegmentation
__A = ["""image""", """text"""]
__A = ["""image"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['vision'] )
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=__UpperCamelCase , return_tensors='pt' )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
with torch.no_grad():
snake_case_ = self.model(**__UpperCamelCase ).logits
return logits
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = outputs.cpu().detach().numpy()
snake_case_ = 0
snake_case_ = 1
return Image.fromarray((array * 2_55).astype(np.uinta ) )
| 46
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """unispeech-sat"""
def __init__( self , __UpperCamelCase=32 , __UpperCamelCase=7_68 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=30_72 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase="group" , __UpperCamelCase="gelu" , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) , __UpperCamelCase=False , __UpperCamelCase=1_28 , __UpperCamelCase=16 , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.05 , __UpperCamelCase=10 , __UpperCamelCase=2 , __UpperCamelCase=0.0 , __UpperCamelCase=10 , __UpperCamelCase=0 , __UpperCamelCase=3_20 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , __UpperCamelCase=1_00 , __UpperCamelCase=2_56 , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase="mean" , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=2_56 , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) , __UpperCamelCase=(5, 3, 3, 1, 1) , __UpperCamelCase=(1, 2, 3, 1, 1) , __UpperCamelCase=5_12 , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=2 , __UpperCamelCase=5_04 , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase , pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase )
snake_case_ = hidden_size
snake_case_ = feat_extract_norm
snake_case_ = feat_extract_activation
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = conv_bias
snake_case_ = num_conv_pos_embeddings
snake_case_ = num_conv_pos_embedding_groups
snake_case_ = len(self.conv_dim )
snake_case_ = num_hidden_layers
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = feat_proj_dropout
snake_case_ = final_dropout
snake_case_ = layerdrop
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = vocab_size
snake_case_ = num_clusters
snake_case_ = do_stable_layer_norm
snake_case_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = apply_spec_augment
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
snake_case_ = num_codevectors_per_group
snake_case_ = num_codevector_groups
snake_case_ = contrastive_logits_temperature
snake_case_ = feat_quantizer_dropout
snake_case_ = num_negatives
snake_case_ = codevector_dim
snake_case_ = proj_codevector_dim
snake_case_ = diversity_loss_weight
# ctc loss
snake_case_ = ctc_loss_reduction
snake_case_ = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = xvector_output_dim
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 46
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = ShapEImgaImgPipeline
__A = ["""image"""]
__A = ["""image"""]
__A = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
__A = False
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return 32
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return 32
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return 8
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
snake_case_ = CLIPVisionModel(__UpperCamelCase )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = CLIPImageProcessor(
crop_size=2_24 , do_center_crop=__UpperCamelCase , do_normalize=__UpperCamelCase , do_resize=__UpperCamelCase , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] , resample=3 , size=2_24 , )
return image_processor
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
snake_case_ = PriorTransformer(**__UpperCamelCase )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
snake_case_ = ShapERenderer(**__UpperCamelCase )
return model
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_prior
snake_case_ = self.dummy_image_encoder
snake_case_ = self.dummy_image_processor
snake_case_ = self.dummy_renderer
snake_case_ = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=10_24 , prediction_type='sample' , use_karras_sigmas=__UpperCamelCase , clip_sample=__UpperCamelCase , clip_sample_range=1.0 , )
snake_case_ = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=0 ):
"""simple docstring"""
snake_case_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
if str(__UpperCamelCase ).startswith('mps' ):
snake_case_ = torch.manual_seed(__UpperCamelCase )
else:
snake_case_ = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
snake_case_ = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'cpu'
snake_case_ = self.get_dummy_components()
snake_case_ = self.pipeline_class(**__UpperCamelCase )
snake_case_ = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = pipe(**self.get_dummy_inputs(__UpperCamelCase ) )
snake_case_ = output.images[0]
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
snake_case_ = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = torch_device == 'cpu'
snake_case_ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__UpperCamelCase , relax_max_difference=__UpperCamelCase , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_dummy_components()
snake_case_ = self.pipeline_class(**__UpperCamelCase )
snake_case_ = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = 1
snake_case_ = 2
snake_case_ = self.get_dummy_inputs(__UpperCamelCase )
for key in inputs.keys():
if key in self.batch_params:
snake_case_ = batch_size * [inputs[key]]
snake_case_ = pipe(**__UpperCamelCase , num_images_per_prompt=__UpperCamelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
snake_case_ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
snake_case_ = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
snake_case_ = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.Generator(device=__UpperCamelCase ).manual_seed(0 )
snake_case_ = pipe(
__UpperCamelCase , generator=__UpperCamelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__UpperCamelCase , __UpperCamelCase )
| 46
|
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self.__class__.__name__}({self.name}, {self.val})"""
def __lt__( self , __UpperCamelCase ):
"""simple docstring"""
return self.val < other.val
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = {}
snake_case_ = {}
snake_case_ = self.build_heap(__UpperCamelCase )
def __getitem__( self , __UpperCamelCase ):
"""simple docstring"""
return self.get_value(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return (idx - 1) // 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 1
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return self.heap_dict[key]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) - 1
snake_case_ = self.get_parent_idx(__UpperCamelCase )
for idx, i in enumerate(__UpperCamelCase ):
snake_case_ = idx
snake_case_ = i.val
for i in range(__UpperCamelCase , -1 , -1 ):
self.sift_down(__UpperCamelCase , __UpperCamelCase )
return array
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
while True:
snake_case_ = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741
snake_case_ = self.get_right_child_idx(__UpperCamelCase )
snake_case_ = idx
if l < len(__UpperCamelCase ) and array[l] < array[idx]:
snake_case_ = l
if r < len(__UpperCamelCase ) and array[r] < array[smallest]:
snake_case_ = r
if smallest != idx:
snake_case_ , snake_case_ = array[smallest], array[idx]
(
(
snake_case_
) , (
snake_case_
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
snake_case_ = smallest
else:
break
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.get_parent_idx(__UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
snake_case_ , snake_case_ = self.heap[idx], self.heap[p]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
snake_case_ = p
snake_case_ = self.get_parent_idx(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return self.heap[0]
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.heap[-1], self.heap[0]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
snake_case_ = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
self.heap.append(__UpperCamelCase )
snake_case_ = len(self.heap ) - 1
snake_case_ = node.val
self.sift_up(len(self.heap ) - 1 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.heap ) == 0
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
snake_case_ = new_value
snake_case_ = new_value
self.sift_up(self.idx_of_element[node] )
A = Node('R', -1)
A = Node('B', 6)
A = Node('A', 3)
A = Node('X', 1)
A = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
A = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 1
|
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """autoformer"""
__A = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__( self , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = "student_t" , __UpperCamelCase = "nll" , __UpperCamelCase = 1 , __UpperCamelCase = [1, 2, 3, 4, 5, 6, 7] , __UpperCamelCase = True , __UpperCamelCase = 0 , __UpperCamelCase = 0 , __UpperCamelCase = 0 , __UpperCamelCase = 0 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = 64 , __UpperCamelCase = 2 , __UpperCamelCase = 2 , __UpperCamelCase = 2 , __UpperCamelCase = 2 , __UpperCamelCase = 32 , __UpperCamelCase = 32 , __UpperCamelCase = "gelu" , __UpperCamelCase = 0.1 , __UpperCamelCase = 0.1 , __UpperCamelCase = 0.1 , __UpperCamelCase = 0.1 , __UpperCamelCase = 0.1 , __UpperCamelCase = 1_00 , __UpperCamelCase = 0.02 , __UpperCamelCase = True , __UpperCamelCase=True , __UpperCamelCase = 10 , __UpperCamelCase = 25 , __UpperCamelCase = 3 , **__UpperCamelCase , ):
"""simple docstring"""
snake_case_ = prediction_length
snake_case_ = context_length if context_length is not None else prediction_length
snake_case_ = distribution_output
snake_case_ = loss
snake_case_ = input_size
snake_case_ = num_time_features
snake_case_ = lags_sequence
snake_case_ = scaling
snake_case_ = num_dynamic_real_features
snake_case_ = num_static_real_features
snake_case_ = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(__UpperCamelCase ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
snake_case_ = cardinality
else:
snake_case_ = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(__UpperCamelCase ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
snake_case_ = embedding_dimension
else:
snake_case_ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
snake_case_ = num_parallel_samples
# Transformer architecture configuration
snake_case_ = input_size * len(self.lags_sequence ) + self._number_of_features
snake_case_ = d_model
snake_case_ = encoder_attention_heads
snake_case_ = decoder_attention_heads
snake_case_ = encoder_ffn_dim
snake_case_ = decoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = decoder_layers
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = use_cache
# Autoformer
snake_case_ = label_length
snake_case_ = moving_average
snake_case_ = autocorrelation_factor
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 46
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['PerceiverFeatureExtractor']
A = ['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 1
|
from typing import Union
import fire
import torch
from tqdm import tqdm
def a(lowercase__ , lowercase__ = "cpu" , lowercase__ = None ):
'''simple docstring'''
snake_case_ = torch.load(lowercase__ , map_location=lowercase__ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowercase__ , torch.Tensor ):
raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin' )
snake_case_ = v.half()
if save_path is None: # overwrite src_path
snake_case_ = src_path
torch.save(lowercase__ , lowercase__ )
if __name__ == "__main__":
fire.Fire(convert)
| 46
|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
snake_case_ = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 1
|
from __future__ import annotations
from math import gcd
def a(lowercase__ , lowercase__ = 2 , lowercase__ = 1 , lowercase__ = 3 , ):
'''simple docstring'''
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('The input value cannot be less than 2' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowercase__ , lowercase__ , lowercase__ ) -> int:
return (pow(lowercase__ , 2 ) + step) % modulus
for _ in range(lowercase__ ):
# These track the position within the cycle detection logic.
snake_case_ = seed
snake_case_ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
snake_case_ = rand_fn(lowercase__ , lowercase__ , lowercase__ )
snake_case_ = rand_fn(lowercase__ , lowercase__ , lowercase__ )
snake_case_ = rand_fn(lowercase__ , lowercase__ , lowercase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
snake_case_ = gcd(hare - tortoise , lowercase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
snake_case_ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
A = argparse.ArgumentParser()
parser.add_argument(
'num',
type=int,
help='The value to find a divisor of',
)
parser.add_argument(
'--attempts',
type=int,
default=3,
help='The number of attempts before giving up',
)
A = parser.parse_args()
A = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(f"""{args.num} is probably prime""")
else:
A = args.num // divisor
print(f"""{args.num} = {divisor} * {quotient}""")
| 46
|
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ = int(factor * num_class_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 )
os.makedirs(f"""{class_data_dir}/images""" , exist_ok=lowercase__ )
if len(list(Path(f"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
snake_case_ = client.query(text=lowercase__ )
if len(lowercase__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
snake_case_ = int(factor * num_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 , )
snake_case_ = 0
snake_case_ = 0
snake_case_ = tqdm(desc='downloading real regularization images' , total=lowercase__ )
with open(f"""{class_data_dir}/caption.txt""" , 'w' ) as fa, open(f"""{class_data_dir}/urls.txt""" , 'w' ) as fa, open(
f"""{class_data_dir}/images.txt""" , 'w' ) as fa:
while total < num_class_images:
snake_case_ = class_images[count]
count += 1
try:
snake_case_ = requests.get(images['url'] )
if img.status_code == 200:
snake_case_ = Image.open(BytesIO(img.content ) )
with open(f"""{class_data_dir}/images/{total}.jpg""" , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def a():
'''simple docstring'''
snake_case_ = argparse.ArgumentParser('' , add_help=lowercase__ )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--class_data_dir' , help='path to save images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--num_class_images' , help='number of images to download' , default=200 , type=lowercase__ )
return parser.parse_args()
if __name__ == "__main__":
A = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 46
| 1
|
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=30 , __UpperCamelCase=4_00 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=True , __UpperCamelCase=1 / 2_55 , __UpperCamelCase=True , ):
"""simple docstring"""
snake_case_ = size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = num_channels
snake_case_ = min_resolution
snake_case_ = max_resolution
snake_case_ = do_resize
snake_case_ = size
snake_case_ = do_normalize
snake_case_ = image_mean
snake_case_ = image_std
snake_case_ = do_rescale
snake_case_ = rescale_factor
snake_case_ = do_pad
def __lowerCAmelCase ( self ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=False ):
"""simple docstring"""
if not batched:
snake_case_ = image_inputs[0]
if isinstance(__UpperCamelCase , Image.Image ):
snake_case_ , snake_case_ = image.size
else:
snake_case_ , snake_case_ = image.shape[1], image.shape[2]
if w < h:
snake_case_ = int(self.size['shortest_edge'] * h / w )
snake_case_ = self.size['shortest_edge']
elif w > h:
snake_case_ = self.size['shortest_edge']
snake_case_ = int(self.size['shortest_edge'] * w / h )
else:
snake_case_ = self.size['shortest_edge']
snake_case_ = self.size['shortest_edge']
else:
snake_case_ = []
for image in image_inputs:
snake_case_ , snake_case_ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ = max(__UpperCamelCase , key=lambda __UpperCamelCase : item[0] )[0]
snake_case_ = max(__UpperCamelCase , key=lambda __UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = YolosImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = YolosImageProcessingTester(self )
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCamelCase , 'image_mean' ) )
self.assertTrue(hasattr(__UpperCamelCase , 'image_std' ) )
self.assertTrue(hasattr(__UpperCamelCase , 'do_normalize' ) )
self.assertTrue(hasattr(__UpperCamelCase , 'do_resize' ) )
self.assertTrue(hasattr(__UpperCamelCase , 'size' ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 13_33} )
self.assertEqual(image_processor.do_pad , __UpperCamelCase )
snake_case_ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__UpperCamelCase )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , Image.Image )
# Test not batched input
snake_case_ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
snake_case_ , snake_case_ = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ , snake_case_ = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
snake_case_ = image_processing(__UpperCamelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , numpify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , np.ndarray )
# Test not batched input
snake_case_ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
snake_case_ , snake_case_ = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ = image_processing(__UpperCamelCase , return_tensors='pt' ).pixel_values
snake_case_ , snake_case_ = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , torch.Tensor )
# Test not batched input
snake_case_ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
snake_case_ , snake_case_ = self.image_processor_tester.get_expected_values(__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ = image_processing(__UpperCamelCase , return_tensors='pt' ).pixel_values
snake_case_ , snake_case_ = self.image_processor_tester.get_expected_values(__UpperCamelCase , batched=__UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.image_processing_class(**self.image_processor_dict )
snake_case_ = self.image_processing_class(do_resize=__UpperCamelCase , do_normalize=__UpperCamelCase , do_rescale=__UpperCamelCase )
# create random PyTorch tensors
snake_case_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
snake_case_ = image_processing_a.pad(__UpperCamelCase , return_tensors='pt' )
snake_case_ = image_processing_a(__UpperCamelCase , return_tensors='pt' )
self.assertTrue(
torch.allclose(encoded_images_with_method['pixel_values'] , encoded_images['pixel_values'] , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
snake_case_ = json.loads(f.read() )
snake_case_ = {'image_id': 3_97_69, 'annotations': target}
# encode them
snake_case_ = YolosImageProcessor.from_pretrained('hustvl/yolos-small' )
snake_case_ = image_processing(images=__UpperCamelCase , annotations=__UpperCamelCase , return_tensors='pt' )
# verify pixel values
snake_case_ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['pixel_values'].shape , __UpperCamelCase )
snake_case_ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , __UpperCamelCase , atol=1E-4 ) )
# verify area
snake_case_ = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , __UpperCamelCase ) )
# verify boxes
snake_case_ = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , __UpperCamelCase )
snake_case_ = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , __UpperCamelCase , atol=1E-3 ) )
# verify image_id
snake_case_ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , __UpperCamelCase ) )
# verify is_crowd
snake_case_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , __UpperCamelCase ) )
# verify class_labels
snake_case_ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , __UpperCamelCase ) )
# verify orig_size
snake_case_ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , __UpperCamelCase ) )
# verify size
snake_case_ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , __UpperCamelCase ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
snake_case_ = json.loads(f.read() )
snake_case_ = {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}
snake_case_ = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
snake_case_ = YolosImageProcessor(format='coco_panoptic' )
snake_case_ = image_processing(images=__UpperCamelCase , annotations=__UpperCamelCase , masks_path=__UpperCamelCase , return_tensors='pt' )
# verify pixel values
snake_case_ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['pixel_values'].shape , __UpperCamelCase )
snake_case_ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , __UpperCamelCase , atol=1E-4 ) )
# verify area
snake_case_ = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , __UpperCamelCase ) )
# verify boxes
snake_case_ = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , __UpperCamelCase )
snake_case_ = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , __UpperCamelCase , atol=1E-3 ) )
# verify image_id
snake_case_ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , __UpperCamelCase ) )
# verify is_crowd
snake_case_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , __UpperCamelCase ) )
# verify class_labels
snake_case_ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , __UpperCamelCase ) )
# verify masks
snake_case_ = 82_28_73
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , __UpperCamelCase )
# verify orig_size
snake_case_ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , __UpperCamelCase ) )
# verify size
snake_case_ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , __UpperCamelCase ) )
| 46
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
A = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 46
| 1
|
def a(lowercase__ = 100 ):
'''simple docstring'''
snake_case_ = set()
snake_case_ = 0
snake_case_ = n + 1 # maximum limit
for a in range(2 , lowercase__ ):
for b in range(2 , lowercase__ ):
snake_case_ = a**b # calculates the current power
collect_powers.add(lowercase__ ) # adds the result to the set
return len(lowercase__ )
if __name__ == "__main__":
print('Number of terms ', solution(int(str(input()).strip())))
| 46
|
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__A = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(default=__snake_case , metadata={"""help""": """The input training data file (a text file)."""} )
__A = field(
default=__snake_case , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__A = field(
default=__snake_case , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
if self.train_file is not None:
snake_case_ = self.train_file.split('.' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
snake_case_ = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = 42
__A = True
__A = None
__A = None
def __call__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 'label' if 'label' in features[0].keys() else 'labels'
snake_case_ = [feature.pop(__UpperCamelCase ) for feature in features]
snake_case_ = len(__UpperCamelCase )
snake_case_ = len(features[0]['input_ids'] )
snake_case_ = [
[{k: v[i] for k, v in feature.items()} for i in range(__UpperCamelCase )] for feature in features
]
snake_case_ = list(chain(*__UpperCamelCase ) )
snake_case_ = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
# Un-flatten
snake_case_ = {k: v.view(__UpperCamelCase , __UpperCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
snake_case_ = torch.tensor(__UpperCamelCase , dtype=torch.intaa )
return batch
def a():
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_ , snake_case_ , snake_case_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ , snake_case_ , snake_case_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_swag' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
datasets.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
snake_case_ = {}
if data_args.train_file is not None:
snake_case_ = data_args.train_file
if data_args.validation_file is not None:
snake_case_ = data_args.validation_file
snake_case_ = data_args.train_file.split('.' )[-1]
snake_case_ = load_dataset(
lowercase__ , data_files=lowercase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
snake_case_ = load_dataset(
'swag' , 'regular' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
snake_case_ = [f"""ending{i}""" for i in range(4 )]
snake_case_ = 'sent1'
snake_case_ = 'sent2'
if data_args.max_seq_length is None:
snake_case_ = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'
' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'
' override this default with `--block_size xxx`.' )
snake_case_ = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case_ = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowercase__ ):
snake_case_ = [[context] * 4 for context in examples[context_name]]
snake_case_ = examples[question_header_name]
snake_case_ = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase__ )
]
# Flatten out
snake_case_ = list(chain(*lowercase__ ) )
snake_case_ = list(chain(*lowercase__ ) )
# Tokenize
snake_case_ = tokenizer(
lowercase__ , lowercase__ , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowercase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case_ = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_train_samples )
snake_case_ = train_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
snake_case_ = train_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case_ = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_eval_samples )
snake_case_ = eval_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
snake_case_ = eval_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
snake_case_ = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowercase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowercase__ ):
snake_case_ , snake_case_ = eval_predictions
snake_case_ = np.argmax(lowercase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
snake_case_ = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , compute_metrics=lowercase__ , )
# Training
if training_args.do_train:
snake_case_ = None
if training_args.resume_from_checkpoint is not None:
snake_case_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ = last_checkpoint
snake_case_ = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case_ = train_result.metrics
snake_case_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase__ )
)
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('train' , lowercase__ )
trainer.save_metrics('train' , lowercase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case_ = trainer.evaluate()
snake_case_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase__ )
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
snake_case_ = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'multiple-choice',
'dataset_tags': 'swag',
'dataset_args': 'regular',
'dataset': 'SWAG',
'language': 'en',
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 46
| 1
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
A = logging.get_logger(__name__)
A = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions',
'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
A = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case_ = getattr(lowercase__ , lowercase__ )
if weight_type is not None:
snake_case_ = getattr(lowercase__ , lowercase__ ).shape
else:
snake_case_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
snake_case_ = value
elif weight_type == "weight_g":
snake_case_ = value
elif weight_type == "weight_v":
snake_case_ = value
elif weight_type == "bias":
snake_case_ = value
elif weight_type == "running_mean":
snake_case_ = value
elif weight_type == "running_var":
snake_case_ = value
elif weight_type == "num_batches_tracked":
snake_case_ = value
elif weight_type == "inv_freq":
snake_case_ = value
else:
snake_case_ = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = fairseq_model.state_dict()
snake_case_ = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
snake_case_ = False
if "conv_layers" in name:
load_conv_layer(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , hf_model.config.feat_extract_norm == 'group' , )
snake_case_ = True
else:
for key, mapped_key in MAPPING.items():
snake_case_ = 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case_ = True
if "*" in mapped_key:
snake_case_ = name.split(lowercase__ )[0].split('.' )[-2]
snake_case_ = mapped_key.replace('*' , lowercase__ )
if "pos_bias_u" in name:
snake_case_ = None
elif "pos_bias_v" in name:
snake_case_ = None
elif "weight_g" in name:
snake_case_ = 'weight_g'
elif "weight_v" in name:
snake_case_ = 'weight_v'
elif "bias" in name:
snake_case_ = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case_ = 'weight'
elif "running_mean" in name:
snake_case_ = 'running_mean'
elif "inv_freq" in name:
snake_case_ = 'inv_freq'
elif "running_var" in name:
snake_case_ = 'running_var'
elif "num_batches_tracked" in name:
snake_case_ = 'num_batches_tracked'
else:
snake_case_ = None
set_recursively(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
continue
if not is_used:
unused_weights.append(lowercase__ )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = full_name.split('conv_layers.' )[-1]
snake_case_ = name.split('.' )
snake_case_ = int(items[0] )
snake_case_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowercase__ )
@torch.no_grad()
def a(lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=True ):
'''simple docstring'''
if config_path is not None:
snake_case_ = WavaVecaConformerConfig.from_pretrained(lowercase__ , hidden_act='swish' )
else:
snake_case_ = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
snake_case_ = 'rotary'
if is_finetuned:
if dict_path:
snake_case_ = Dictionary.load(lowercase__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case_ = target_dict.pad_index
snake_case_ = target_dict.bos_index
snake_case_ = target_dict.eos_index
snake_case_ = len(target_dict.symbols )
snake_case_ = os.path.join(lowercase__ , 'vocab.json' )
if not os.path.isdir(lowercase__ ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase__ ) )
return
os.makedirs(lowercase__ , exist_ok=lowercase__ )
snake_case_ = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case_ = 0
snake_case_ = 1
with open(lowercase__ , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(lowercase__ , lowercase__ )
snake_case_ = WavaVecaCTCTokenizer(
lowercase__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=lowercase__ , )
snake_case_ = True if config.feat_extract_norm == 'layer' else False
snake_case_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowercase__ , return_attention_mask=lowercase__ , )
snake_case_ = WavaVecaProcessor(feature_extractor=lowercase__ , tokenizer=lowercase__ )
processor.save_pretrained(lowercase__ )
snake_case_ = WavaVecaConformerForCTC(lowercase__ )
else:
snake_case_ = WavaVecaConformerForPreTraining(lowercase__ )
if is_finetuned:
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
snake_case_ = argparse.Namespace(task='audio_pretraining' )
snake_case_ = fairseq.tasks.setup_task(lowercase__ )
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=lowercase__ )
snake_case_ = model[0].eval()
recursively_load_weights(lowercase__ , lowercase__ , not is_finetuned )
hf_wavavec.save_pretrained(lowercase__ )
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
A = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 46
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ASTForAudioClassification',
'ASTModel',
'ASTPreTrainedModel',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['ASTFeatureExtractor']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 1
|
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = multiprocessing.Manager()
snake_case_ = manager.list()
snake_case_ = multiprocessing.Process(target=lowercase__ , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append('timed out' )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
snake_case_ = shutil.rmtree
snake_case_ = os.rmdir
snake_case_ = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
snake_case_ = {}
with swallow_io():
with time_limit(lowercase__ ):
exec(lowercase__ , lowercase__ )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(f"""failed: {e}""" )
# Needed for cleaning up.
snake_case_ = rmtree
snake_case_ = rmdir
snake_case_ = chdir
@contextlib.contextmanager
def a(lowercase__ ):
'''simple docstring'''
def signal_handler(lowercase__ , lowercase__ ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , lowercase__ )
signal.signal(signal.SIGALRM , lowercase__ )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def a():
'''simple docstring'''
snake_case_ = WriteOnlyStringIO()
with contextlib.redirect_stdout(lowercase__ ):
with contextlib.redirect_stderr(lowercase__ ):
with redirect_stdin(lowercase__ ):
yield
@contextlib.contextmanager
def a():
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(lowercase__ ):
yield dirname
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
pass
class SCREAMING_SNAKE_CASE ( io.StringIO ):
"""simple docstring"""
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
raise OSError
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
raise OSError
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
raise OSError
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return False
class SCREAMING_SNAKE_CASE ( contextlib._RedirectStream ): # type: ignore
"""simple docstring"""
__A = """stdin"""
@contextlib.contextmanager
def a(lowercase__ ):
'''simple docstring'''
if root == ".":
yield
return
snake_case_ = os.getcwd()
os.chdir(lowercase__ )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(lowercase__ )
def a(lowercase__=None ):
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
snake_case_ = None
snake_case_ = None
import os
snake_case_ = '1'
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
import shutil
snake_case_ = None
snake_case_ = None
snake_case_ = None
import subprocess
snake_case_ = None # type: ignore
snake_case_ = None
import sys
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
snake_case_ = None
| 46
|
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
else:
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
A = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 46
| 1
|
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'configuration_cpmant': ['CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CpmAntConfig'],
'tokenization_cpmant': ['CpmAntTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST',
'CpmAntForCausalLM',
'CpmAntModel',
'CpmAntPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
__A = """bit"""
__A = ["""preactivation""", """bottleneck"""]
__A = ["""SAME""", """VALID"""]
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=64 , __UpperCamelCase=[2_56, 5_12, 10_24, 20_48] , __UpperCamelCase=[3, 4, 6, 3] , __UpperCamelCase="preactivation" , __UpperCamelCase="relu" , __UpperCamelCase=None , __UpperCamelCase=32 , __UpperCamelCase=0.0 , __UpperCamelCase=False , __UpperCamelCase=32 , __UpperCamelCase=1 , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
snake_case_ = global_padding.upper()
else:
raise ValueError(f"""Padding strategy {global_padding} not supported""" )
snake_case_ = num_channels
snake_case_ = embedding_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = layer_type
snake_case_ = hidden_act
snake_case_ = global_padding
snake_case_ = num_groups
snake_case_ = drop_path_rate
snake_case_ = embedding_dynamic_padding
snake_case_ = output_stride
snake_case_ = width_factor
snake_case_ = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(__UpperCamelCase ) + 1 )]
snake_case_ , snake_case_ = get_aligned_output_features_output_indices(
out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
| 46
| 1
|
from __future__ import annotations
from typing import Generic, TypeVar
A = TypeVar('T')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = data
snake_case_ = self
snake_case_ = 0
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
snake_case_ = {}
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = DisjointSetTreeNode(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.map[data]
if elem_ref != elem_ref.parent:
snake_case_ = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
if nodea.rank > nodea.rank:
snake_case_ = nodea
else:
snake_case_ = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
self.link(self.find_set(__UpperCamelCase ) , self.find_set(__UpperCamelCase ) )
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
snake_case_ = {}
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
if node not in self.connections:
snake_case_ = {}
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
self.add_node(__UpperCamelCase )
self.add_node(__UpperCamelCase )
snake_case_ = weight
snake_case_ = weight
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = []
snake_case_ = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __UpperCamelCase : x[2] )
# creating the disjoint set
snake_case_ = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__UpperCamelCase )
# MST generation
snake_case_ = 0
snake_case_ = 0
snake_case_ = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
snake_case_ , snake_case_ , snake_case_ = edges[index]
index += 1
snake_case_ = disjoint_set.find_set(__UpperCamelCase )
snake_case_ = disjoint_set.find_set(__UpperCamelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
disjoint_set.union(__UpperCamelCase , __UpperCamelCase )
return graph
| 46
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = DDIMScheduler()
snake_case_ = self.dummy_vq_model
snake_case_ = LDMPipeline(unet=__UpperCamelCase , vqvae=__UpperCamelCase , scheduler=__UpperCamelCase )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' , return_dict=__UpperCamelCase )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=5 , output_type='numpy' ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 46
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
'CanineForMultipleChoice',
'CanineForQuestionAnswering',
'CanineForSequenceClassification',
'CanineForTokenClassification',
'CanineLayer',
'CanineModel',
'CaninePreTrainedModel',
'load_tf_weights_in_canine',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = LEDConfig
__A = {}
__A = """gelu"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=20 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=4 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
snake_case_ = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
snake_case_ = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
snake_case_ = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
snake_case_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
snake_case_ = tf.concat([input_ids, eos_tensor] , axis=1 )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
snake_case_ = prepare_led_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = tf.concat(
[tf.zeros_like(__UpperCamelCase )[:, :-1], tf.ones_like(__UpperCamelCase )[:, -1:]] , axis=-1 , )
snake_case_ = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFLEDModel(config=__UpperCamelCase ).get_decoder()
snake_case_ = inputs_dict['input_ids']
snake_case_ = input_ids[:1, :]
snake_case_ = inputs_dict['attention_mask'][:1, :]
snake_case_ = 1
# first forward pass
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
snake_case_ = tf.concat([input_ids, next_tokens] , axis=-1 )
snake_case_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
snake_case_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
snake_case_ = output_from_no_past[:, -3:, random_slice_idx]
snake_case_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-3 )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
__A = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
__A = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A = True
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = tf.zeros_like(inputs_dict['attention_mask'] )
snake_case_ = 2
snake_case_ = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['global_attention_mask'] , )
snake_case_ = True
snake_case_ = self.model_tester.seq_length
snake_case_ = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__UpperCamelCase ):
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__UpperCamelCase ):
snake_case_ = [t.numpy() for t in outputs.encoder_attentions]
snake_case_ = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = len(__UpperCamelCase )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_decoder_attentions_output(__UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def a(lowercase__ ):
'''simple docstring'''
return tf.constant(lowercase__ , dtype=tf.intaa )
A = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, 7_68)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 , rtol=1E-3 )
| 46
| 1
|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'neck_hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'num_attention_heads' ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=6_40 , __UpperCamelCase=4 , __UpperCamelCase="silu" , __UpperCamelCase=3 , __UpperCamelCase=32 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = last_hidden_size
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = conv_kernel_size
snake_case_ = output_stride
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = classifier_dropout_prob
snake_case_ = use_labels
snake_case_ = is_training
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MobileViTModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForSemanticSegmentation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__A = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTModelTester(self )
snake_case_ = MobileViTConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = 5
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case_ = 2
for i in range(len(__UpperCamelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MobileViTModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits
# verify the logits
snake_case_ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=__UpperCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits.detach().cpu()
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase , target_sizes=[(50, 60)] )
snake_case_ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase )
snake_case_ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
| 46
|
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replace(' ' , '' )
# Strings of different lengths are not anagrams
if len(lowercase__ ) != len(lowercase__ ):
return False
# Default values for count should be 0
snake_case_ = defaultdict(lowercase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowercase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
A = input('Enter the first string ').strip()
A = input('Enter the second string ').strip()
A = check_anagrams(input_a, input_b)
print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 46
| 1
|
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=0.0 , __UpperCamelCase = None , __UpperCamelCase = "geglu" , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = True , __UpperCamelCase = "layer_norm" , __UpperCamelCase = False , ):
"""simple docstring"""
super().__init__()
snake_case_ = only_cross_attention
snake_case_ = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
snake_case_ = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case_ = AdaLayerNorm(__UpperCamelCase , __UpperCamelCase )
elif self.use_ada_layer_norm_zero:
snake_case_ = AdaLayerNormZero(__UpperCamelCase , __UpperCamelCase )
else:
snake_case_ = nn.LayerNorm(__UpperCamelCase , elementwise_affine=__UpperCamelCase )
snake_case_ = Attention(
query_dim=__UpperCamelCase , heads=__UpperCamelCase , dim_head=__UpperCamelCase , dropout=__UpperCamelCase , bias=__UpperCamelCase , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=__UpperCamelCase , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case_ = (
AdaLayerNorm(__UpperCamelCase , __UpperCamelCase )
if self.use_ada_layer_norm
else nn.LayerNorm(__UpperCamelCase , elementwise_affine=__UpperCamelCase )
)
snake_case_ = Attention(
query_dim=__UpperCamelCase , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=__UpperCamelCase , dim_head=__UpperCamelCase , dropout=__UpperCamelCase , bias=__UpperCamelCase , upcast_attention=__UpperCamelCase , ) # is self-attn if encoder_hidden_states is none
else:
snake_case_ = None
snake_case_ = None
# 3. Feed-forward
snake_case_ = nn.LayerNorm(__UpperCamelCase , elementwise_affine=__UpperCamelCase )
snake_case_ = FeedForward(__UpperCamelCase , dropout=__UpperCamelCase , activation_fn=__UpperCamelCase , final_dropout=__UpperCamelCase )
# let chunk size default to None
snake_case_ = None
snake_case_ = 0
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = chunk_size
snake_case_ = dim
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , ):
"""simple docstring"""
if self.use_ada_layer_norm:
snake_case_ = self.norma(__UpperCamelCase , __UpperCamelCase )
elif self.use_ada_layer_norm_zero:
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.norma(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , hidden_dtype=hidden_states.dtype )
else:
snake_case_ = self.norma(__UpperCamelCase )
snake_case_ = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case_ = self.attna(
__UpperCamelCase , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=__UpperCamelCase , **__UpperCamelCase , )
if self.use_ada_layer_norm_zero:
snake_case_ = gate_msa.unsqueeze(1 ) * attn_output
snake_case_ = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case_ = (
self.norma(__UpperCamelCase , __UpperCamelCase ) if self.use_ada_layer_norm else self.norma(__UpperCamelCase )
)
snake_case_ = self.attna(
__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , attention_mask=__UpperCamelCase , **__UpperCamelCase , )
snake_case_ = attn_output + hidden_states
# 3. Feed-forward
snake_case_ = self.norma(__UpperCamelCase )
if self.use_ada_layer_norm_zero:
snake_case_ = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
snake_case_ = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case_ = torch.cat(
[self.ff(__UpperCamelCase ) for hid_slice in norm_hidden_states.chunk(__UpperCamelCase , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
snake_case_ = self.ff(__UpperCamelCase )
if self.use_ada_layer_norm_zero:
snake_case_ = gate_mlp.unsqueeze(1 ) * ff_output
snake_case_ = ff_output + hidden_states
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = 4 , __UpperCamelCase = 0.0 , __UpperCamelCase = "geglu" , __UpperCamelCase = False , ):
"""simple docstring"""
super().__init__()
snake_case_ = int(dim * mult )
snake_case_ = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case_ = GELU(__UpperCamelCase , __UpperCamelCase )
if activation_fn == "gelu-approximate":
snake_case_ = GELU(__UpperCamelCase , __UpperCamelCase , approximate='tanh' )
elif activation_fn == "geglu":
snake_case_ = GEGLU(__UpperCamelCase , __UpperCamelCase )
elif activation_fn == "geglu-approximate":
snake_case_ = ApproximateGELU(__UpperCamelCase , __UpperCamelCase )
snake_case_ = nn.ModuleList([] )
# project in
self.net.append(__UpperCamelCase )
# project dropout
self.net.append(nn.Dropout(__UpperCamelCase ) )
# project out
self.net.append(nn.Linear(__UpperCamelCase , __UpperCamelCase ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(__UpperCamelCase ) )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
for module in self.net:
snake_case_ = module(__UpperCamelCase )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = "none" ):
"""simple docstring"""
super().__init__()
snake_case_ = nn.Linear(__UpperCamelCase , __UpperCamelCase )
snake_case_ = approximate
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(__UpperCamelCase , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.proj(__UpperCamelCase )
snake_case_ = self.gelu(__UpperCamelCase )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
super().__init__()
snake_case_ = nn.Linear(__UpperCamelCase , dim_out * 2 )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(__UpperCamelCase )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ , snake_case_ = self.proj(__UpperCamelCase ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
super().__init__()
snake_case_ = nn.Linear(__UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.proj(__UpperCamelCase )
return x * torch.sigmoid(1.702 * x )
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
super().__init__()
snake_case_ = nn.Embedding(__UpperCamelCase , __UpperCamelCase )
snake_case_ = nn.SiLU()
snake_case_ = nn.Linear(__UpperCamelCase , embedding_dim * 2 )
snake_case_ = nn.LayerNorm(__UpperCamelCase , elementwise_affine=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.linear(self.silu(self.emb(__UpperCamelCase ) ) )
snake_case_ , snake_case_ = torch.chunk(__UpperCamelCase , 2 )
snake_case_ = self.norm(__UpperCamelCase ) * (1 + scale) + shift
return x
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
super().__init__()
snake_case_ = CombinedTimestepLabelEmbeddings(__UpperCamelCase , __UpperCamelCase )
snake_case_ = nn.SiLU()
snake_case_ = nn.Linear(__UpperCamelCase , 6 * embedding_dim , bias=__UpperCamelCase )
snake_case_ = nn.LayerNorm(__UpperCamelCase , elementwise_affine=__UpperCamelCase , eps=1E-6 )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None ):
"""simple docstring"""
snake_case_ = self.linear(self.silu(self.emb(__UpperCamelCase , __UpperCamelCase , hidden_dtype=__UpperCamelCase ) ) )
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = emb.chunk(6 , dim=1 )
snake_case_ = self.norm(__UpperCamelCase ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = 1E-5 ):
"""simple docstring"""
super().__init__()
snake_case_ = num_groups
snake_case_ = eps
if act_fn is None:
snake_case_ = None
else:
snake_case_ = get_activation(__UpperCamelCase )
snake_case_ = nn.Linear(__UpperCamelCase , out_dim * 2 )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
if self.act:
snake_case_ = self.act(__UpperCamelCase )
snake_case_ = self.linear(__UpperCamelCase )
snake_case_ = emb[:, :, None, None]
snake_case_ , snake_case_ = emb.chunk(2 , dim=1 )
snake_case_ = F.group_norm(__UpperCamelCase , self.num_groups , eps=self.eps )
snake_case_ = x * (1 + scale) + shift
return x
| 46
|
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = ScoreSdeVeScheduler()
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase , return_dict=__UpperCamelCase )[
0
]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'google/ncsnpp-church-256'
snake_case_ = UNetaDModel.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 46
| 1
|
A = 'Alexander Joslin'
import operator as op
from .stack import Stack
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
snake_case_ = Stack()
snake_case_ = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowercase__ ) )
elif i in operators:
# RULE 2
operator_stack.push(lowercase__ )
elif i == ")":
# RULE 4
snake_case_ = operator_stack.peek()
operator_stack.pop()
snake_case_ = operand_stack.peek()
operand_stack.pop()
snake_case_ = operand_stack.peek()
operand_stack.pop()
snake_case_ = operators[opr](lowercase__ , lowercase__ )
operand_stack.push(lowercase__ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
A = '(5 + ((4 * 2) * (2 + 3)))'
# answer = 45
print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")
| 46
|
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
@register_to_config
def __init__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None ):
"""simple docstring"""
super().__init__()
snake_case_ = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
snake_case_ = torch.zeros(__UpperCamelCase , __UpperCamelCase )
else:
snake_case_ = None
snake_case_ = torch.nn.Parameter(__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
super().__init__()
self.register_modules(
vqvae=__UpperCamelCase , transformer=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , scheduler=__UpperCamelCase , learned_classifier_free_sampling_embeddings=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else 1
# get prompt text embeddings
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
snake_case_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
snake_case_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
snake_case_ = text_input_ids[:, : self.tokenizer.model_max_length]
snake_case_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
snake_case_ = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate text embeddings for each generation per prompt
snake_case_ = prompt_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
snake_case_ = self.learned_classifier_free_sampling_embeddings.embeddings
snake_case_ = negative_prompt_embeds.unsqueeze(0 ).repeat(__UpperCamelCase , 1 , 1 )
else:
snake_case_ = [''] * batch_size
snake_case_ = text_input_ids.shape[-1]
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors='pt' , )
snake_case_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
snake_case_ = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case_ = negative_prompt_embeds.shape[1]
snake_case_ = negative_prompt_embeds.repeat(1 , __UpperCamelCase , 1 )
snake_case_ = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __UpperCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case_ = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , __UpperCamelCase , __UpperCamelCase = 1_00 , __UpperCamelCase = 5.0 , __UpperCamelCase = 1.0 , __UpperCamelCase = 1 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = "pil" , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = 1 , ):
"""simple docstring"""
if isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = 1
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = len(__UpperCamelCase )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase )}""" )
snake_case_ = batch_size * num_images_per_prompt
snake_case_ = guidance_scale > 1.0
snake_case_ = self._encode_prompt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__UpperCamelCase , __UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__UpperCamelCase )}.""" )
# get the initial completely masked latents unless the user supplied it
snake_case_ = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
snake_case_ = self.transformer.num_vector_embeds - 1
snake_case_ = torch.full(__UpperCamelCase , __UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
f""" {self.transformer.num_vector_embeds - 1} (inclusive).""" )
snake_case_ = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__UpperCamelCase , device=self.device )
snake_case_ = self.scheduler.timesteps.to(self.device )
snake_case_ = latents
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
snake_case_ = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
snake_case_ = self.transformer(__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , timestep=__UpperCamelCase ).sample
if do_classifier_free_guidance:
snake_case_ , snake_case_ = model_output.chunk(2 )
snake_case_ = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__UpperCamelCase , dim=1 , keepdim=__UpperCamelCase )
snake_case_ = self.truncate(__UpperCamelCase , __UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
snake_case_ = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(__UpperCamelCase , timestep=__UpperCamelCase , sample=__UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = self.vqvae.config.vq_embed_dim
snake_case_ = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
snake_case_ = self.vqvae.quantize.get_codebook_entry(__UpperCamelCase , shape=__UpperCamelCase )
snake_case_ = self.vqvae.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase ).sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ , snake_case_ = torch.sort(__UpperCamelCase , 1 , descending=__UpperCamelCase )
snake_case_ = torch.exp(__UpperCamelCase )
snake_case_ = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
snake_case_ = torch.full_like(keep_mask[:, 0:1, :] , __UpperCamelCase )
snake_case_ = torch.cat((all_true, keep_mask) , dim=1 )
snake_case_ = keep_mask[:, :-1, :]
snake_case_ = keep_mask.gather(1 , indices.argsort(1 ) )
snake_case_ = log_p_x_0.clone()
snake_case_ = -torch.inf # -inf = log(0)
return rv
| 46
| 1
|
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = filter(lambda lowercase__ : p.requires_grad , model.parameters() )
snake_case_ = sum([np.prod(p.size() ) for p in model_parameters] )
return params
A = logging.getLogger(__name__)
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if metric == "rouge2":
snake_case_ = '{val_avg_rouge2:.4f}-{step_count}'
elif metric == "bleu":
snake_case_ = '{val_avg_bleu:.4f}-{step_count}'
elif metric == "em":
snake_case_ = '{val_avg_em:.4f}-{step_count}'
elif metric == "loss":
snake_case_ = '{val_avg_loss:.4f}-{step_count}'
else:
raise NotImplementedError(
f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
' function.' )
snake_case_ = ModelCheckpoint(
dirpath=lowercase__ , filename=lowercase__ , monitor=f"""val_{metric}""" , mode='max' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
return EarlyStopping(
monitor=f"""val_{metric}""" , mode='min' if 'loss' in metric else 'max' , patience=lowercase__ , verbose=lowercase__ , )
class SCREAMING_SNAKE_CASE ( pl.Callback ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__UpperCamelCase )
@rank_zero_only
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
snake_case_ = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']} )
# Log results
snake_case_ = Path(pl_module.hparams.output_dir )
if type_path == "test":
snake_case_ = od / 'test_results.txt'
snake_case_ = od / 'test_generations.txt'
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
snake_case_ = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
snake_case_ = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=__UpperCamelCase )
generations_file.parent.mkdir(exist_ok=__UpperCamelCase )
with open(__UpperCamelCase , 'a+' ) as writer:
for key in sorted(__UpperCamelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
snake_case_ = metrics[key]
if isinstance(__UpperCamelCase , torch.Tensor ):
snake_case_ = val.item()
snake_case_ = f"""{key}: {val:.6f}\n"""
writer.write(__UpperCamelCase )
if not save_generations:
return
if "preds" in metrics:
snake_case_ = '\n'.join(metrics['preds'] )
generations_file.open('w+' ).write(__UpperCamelCase )
@rank_zero_only
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
try:
snake_case_ = pl_module.model.model.num_parameters()
except AttributeError:
snake_case_ = pl_module.model.num_parameters()
snake_case_ = count_trainable_parameters(__UpperCamelCase )
# mp stands for million parameters
trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6} )
@rank_zero_only
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__UpperCamelCase , __UpperCamelCase , 'test' )
@rank_zero_only
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 46
|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'neck_hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'num_attention_heads' ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=6_40 , __UpperCamelCase=4 , __UpperCamelCase="silu" , __UpperCamelCase=3 , __UpperCamelCase=32 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = last_hidden_size
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = conv_kernel_size
snake_case_ = output_stride
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = classifier_dropout_prob
snake_case_ = use_labels
snake_case_ = is_training
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MobileViTModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForSemanticSegmentation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__A = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTModelTester(self )
snake_case_ = MobileViTConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = 5
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case_ = 2
for i in range(len(__UpperCamelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MobileViTModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits
# verify the logits
snake_case_ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=__UpperCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits.detach().cpu()
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase , target_sizes=[(50, 60)] )
snake_case_ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase )
snake_case_ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
| 46
| 1
|
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A = logging.getLogger()
def a():
'''simple docstring'''
snake_case_ = argparse.ArgumentParser()
parser.add_argument('-f' )
snake_case_ = parser.parse_args()
return args.f
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {}
snake_case_ = os.path.join(lowercase__ , 'all_results.json' )
if os.path.exists(lowercase__ ):
with open(lowercase__ , 'r' ) as f:
snake_case_ = json.load(lowercase__ )
else:
raise ValueError(f"""can't find {path}""" )
return results
def a():
'''simple docstring'''
snake_case_ = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
@classmethod
def __lowerCAmelCase ( cls ):
"""simple docstring"""
snake_case_ = tempfile.mkdtemp()
snake_case_ = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
snake_case_ = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def __lowerCAmelCase ( cls ):
"""simple docstring"""
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertLess(result['perplexity'] , 1_00 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 7 if get_gpu_count() > 1 else 2
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'translation_no_trainer' ) ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = logging.StreamHandler(sys.stdout )
logger.addHandler(__UpperCamelCase )
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.10 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = f"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
snake_case_ = get_results(__UpperCamelCase )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'image_classification_no_trainer' ) ) )
| 46
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 46
| 1
|
from math import factorial
def a(lowercase__ = 100 ):
'''simple docstring'''
return sum(map(lowercase__ , str(factorial(lowercase__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 46
|
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
snake_case_ = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
snake_case_ = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
snake_case_ = tf_top_k_top_p_filtering(__UpperCamelCase , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
snake_case_ = output[output != -float('inf' )]
snake_case_ = tf.cast(
tf.where(tf.not_equal(__UpperCamelCase , tf.constant(-float('inf' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-12 )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase , __snake_case ):
"""simple docstring"""
if is_tf_available():
__A = {
"""AutoModelForCausalLM""": TFAutoModelForCausalLM,
"""AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
"""AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
"""AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
"""LogitsProcessorList""": TFLogitsProcessorList,
"""MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
"""create_tensor_fn""": tf.convert_to_tensor,
"""floats_tensor""": floats_tensor,
"""return_tensors""": """tf""",
}
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 2
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='input_ids' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2, 0], [1_02, 1_03]]
snake_case_ = [[1, 0], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for batch_size in range(1 , len(__UpperCamelCase ) + 1 ):
snake_case_ = {
'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 1
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='input_ids' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2], [1_02, 1_03]]
snake_case_ = [[1], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for input_row in range(len(__UpperCamelCase ) ):
snake_case_ = {
'input_ids': tf.constant([dummy_input_ids[input_row]] ),
'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
@require_tensorflow_text
def __lowerCAmelCase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='google/flan-t5-small' , filename='spiece.model' , local_dir=__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
super().__init__()
snake_case_ = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(__UpperCamelCase , 'spiece.model' ) , 'rb' ).read() )
snake_case_ = TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )
def __lowerCAmelCase ( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.tokenizer.tokenize(__UpperCamelCase )
snake_case_ , snake_case_ = text.pad_model_inputs(
__UpperCamelCase , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
snake_case_ = self.model.generate(input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase )
return self.tokenizer.detokenize(__UpperCamelCase )
snake_case_ = CompleteSentenceTransformer()
snake_case_ = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='inputs' )
snake_case_ = complete_model(__UpperCamelCase )
snake_case_ = tf.keras.Model(__UpperCamelCase , __UpperCamelCase )
keras_model.save(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = {
'do_sample': True,
'num_beams': 1,
'top_p': 0.7,
'top_k': 10,
'temperature': 0.7,
}
snake_case_ = 14
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 'Hello, my dog is cute and'
snake_case_ = tokenizer(__UpperCamelCase , return_tensors='tf' )
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
snake_case_ = [6_38, 1_98]
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = 'Hugging Face is a technology company based in New York and Paris.'
snake_case_ = bart_tokenizer(__UpperCamelCase , return_tensors='tf' ).input_ids
snake_case_ = TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=None , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase , foo='bar' ).numpy()
self.assertTrue(np.array_equal(__UpperCamelCase , __UpperCamelCase ) )
class SCREAMING_SNAKE_CASE ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeEncoder(bart_model.config , bart_model.model.shared )
snake_case_ = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
with self.assertRaises(__UpperCamelCase ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(__UpperCamelCase , foo='bar' )
| 46
| 1
|
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
A = '0.12' # assumed parallelism: 8
if is_torch_available():
import torch
def a(lowercase__ , lowercase__ , lowercase__=None ):
'''simple docstring'''
if rng is None:
snake_case_ = random.Random()
snake_case_ = 1
for dim in shape:
total_dims *= dim
snake_case_ = []
for _ in range(lowercase__ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
snake_case_ = np.array(lowercase__ , dtype=jnp.intaa ).reshape(lowercase__ )
return output
def a(lowercase__ , lowercase__=None ):
'''simple docstring'''
snake_case_ = ids_tensor(lowercase__ , vocab_size=2 , rng=lowercase__ )
# make sure that at least one token is attended to for each batch
snake_case_ = 1
return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = None
__A = ()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
snake_case_ = 2
snake_case_ = inputs['input_ids'].shape[-1] // 2
snake_case_ = inputs['input_ids'][:max_batch_size, :sequence_length]
snake_case_ = jnp.ones_like(__UpperCamelCase )
snake_case_ = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
snake_case_ = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
snake_case_ = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = False
snake_case_ = max_length
snake_case_ = 0
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model_class.__name__[4:] # Skip the "Flax" at the beginning
snake_case_ = getattr(__UpperCamelCase , __UpperCamelCase )
snake_case_ = pt_model_class(__UpperCamelCase ).eval()
snake_case_ = load_flax_weights_in_pytorch_model(__UpperCamelCase , flax_model.params )
snake_case_ = flax_model.generate(__UpperCamelCase ).sequences
snake_case_ = pt_model.generate(torch.tensor(__UpperCamelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
snake_case_ = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = False
snake_case_ = max_length
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = True
snake_case_ = max_length
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = False
snake_case_ = max_length
snake_case_ = 2
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = False
snake_case_ = max_length
snake_case_ = 2
snake_case_ = 2
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = True
snake_case_ = max_length
snake_case_ = 0.8
snake_case_ = 10
snake_case_ = 0.3
snake_case_ = 1
snake_case_ = 8
snake_case_ = 9
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = max_length
snake_case_ = 1
snake_case_ = 8
snake_case_ = 9
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
snake_case_ = max_length
snake_case_ = 2
snake_case_ = 1
snake_case_ = 8
snake_case_ = 9
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
# pad attention mask on the left
snake_case_ = attention_mask.at[(0, 0)].set(0 )
snake_case_ = False
snake_case_ = max_length
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase , attention_mask=__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase , attention_mask=__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
# pad attention mask on the left
snake_case_ = attention_mask.at[(0, 0)].set(0 )
snake_case_ = True
snake_case_ = max_length
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase , attention_mask=__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase , attention_mask=__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self._get_input_ids_and_config()
# pad attention mask on the left
snake_case_ = attention_mask.at[(0, 0)].set(0 )
snake_case_ = 2
snake_case_ = max_length
for model_class in self.all_generative_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model.generate(__UpperCamelCase , attention_mask=__UpperCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __UpperCamelCase )
snake_case_ = jit(model.generate )
snake_case_ = jit_generate(__UpperCamelCase , attention_mask=__UpperCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert' )
snake_case_ = FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only' )
snake_case_ = 'Hello world'
snake_case_ = tokenizer(__UpperCamelCase , return_tensors='np' ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(__UpperCamelCase , 'do_samples' ):
model.generate(__UpperCamelCase , do_samples=__UpperCamelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(__UpperCamelCase , 'foo' ):
snake_case_ = {'foo': 'bar'}
model.generate(__UpperCamelCase , **__UpperCamelCase )
| 46
|
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=2 , __UpperCamelCase=8 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=16 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=36 , __UpperCamelCase="gelu" , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_config()
snake_case_ = 3_00
return config
def __lowerCAmelCase ( self ):
"""simple docstring"""
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = self.prepare_config_and_inputs()
snake_case_ = True
snake_case_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = MraModel(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , )
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , start_positions=__UpperCamelCase , end_positions=__UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_choices
snake_case_ = MraForMultipleChoice(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
__A = False
__A = False
__A = False
__A = False
__A = ()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MraModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@unittest.skip(reason='MRA does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModel.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = torch.Size((1, 2_56, 7_68) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 2_56, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
snake_case_ = torch.arange(40_96 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 40_96, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
| 1
|
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def a(lowercase__ , lowercase__=() , lowercase__=None , lowercase__="no" , lowercase__="29500" ):
'''simple docstring'''
snake_case_ = False
snake_case_ = False
if any(key.startswith('KAGGLE' ) for key in os.environ.keys() ):
snake_case_ = True
elif "IPython" in sys.modules:
snake_case_ = 'google.colab' in str(sys.modules['IPython'].get_ipython() )
try:
snake_case_ = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.""" )
if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME' , lowercase__ ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
'your training function. Restart your notebook and make sure no cells initializes an '
'`Accelerator`.' )
if num_processes is None:
snake_case_ = 8
snake_case_ = PrepareForLaunch(lowercase__ , distributed_type='TPU' )
print(f"""Launching a training on {num_processes} TPU cores.""" )
xmp.spawn(lowercase__ , args=lowercase__ , nprocs=lowercase__ , start_method='fork' )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print('Launching training on one GPU.' )
else:
print('Launching training on one CPU.' )
function(*lowercase__ )
else:
if num_processes is None:
raise ValueError(
'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.' )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
'inside your training function. Restart your notebook and make sure no cells initializes an '
'`Accelerator`.' )
if torch.cuda.is_initialized():
raise ValueError(
'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
'function.' )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=lowercase__ , master_addr='127.0.01' , master_port=lowercase__ , mixed_precision=lowercase__ ):
snake_case_ = PrepareForLaunch(lowercase__ , distributed_type='MULTI_GPU' )
print(f"""Launching training on {num_processes} GPUs.""" )
try:
start_processes(lowercase__ , args=lowercase__ , nprocs=lowercase__ , start_method='fork' )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
'Please review your imports and test them when running the `notebook_launcher()` to identify '
'which one is problematic.' ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
snake_case_ = '1'
print('Launching training on MPS.' )
elif torch.cuda.is_available():
print('Launching training on one GPU.' )
else:
print('Launching training on CPU.' )
function(*lowercase__ )
def a(lowercase__ , lowercase__=() , lowercase__=2 ):
'''simple docstring'''
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=lowercase__ , master_addr='127.0.01' , master_port='29500' , accelerate_mixed_precision='no' , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu='yes' , ):
snake_case_ = PrepareForLaunch(lowercase__ , debug=lowercase__ )
start_processes(lowercase__ , args=lowercase__ , nprocs=lowercase__ , start_method='fork' )
| 46
|
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
snake_case_ = TapasConfig.from_json_file(lowercase__ )
# set absolute/relative position embeddings parameter
snake_case_ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WTQ":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = True
# hparam_utils.py hparams
snake_case_ = 0.66_4694
snake_case_ = 0.20_7951
snake_case_ = 0.12_1194
snake_case_ = True
snake_case_ = True
snake_case_ = False
snake_case_ = 0.035_2513
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = False
# hparam_utils.py hparams
snake_case_ = 36.4519
snake_case_ = 0.90_3421
snake_case_ = 222.088
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 0.76_3141
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "TABFACT":
snake_case_ = TapasForSequenceClassification(config=lowercase__ )
elif task == "MLM":
snake_case_ = TapasForMaskedLM(config=lowercase__ )
elif task == "INTERMEDIATE_PRETRAINING":
snake_case_ = TapasModel(config=lowercase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
snake_case_ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' , model_max_length=512 )
tokenizer.save_pretrained(lowercase__ )
print('Used relative position embeddings:' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
)
parser.add_argument(
'--reset_position_index_per_cell',
default=False,
action='store_true',
help='Whether to use relative position embeddings or not. Defaults to True.',
)
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--tapas_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained TAPAS model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 46
| 1
|
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
|
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=16 , __UpperCamelCase=[1, 2, 1] , __UpperCamelCase=[2, 2, 4] , __UpperCamelCase=2 , __UpperCamelCase=2.0 , __UpperCamelCase=True , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase="gelu" , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=8 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = embed_dim
snake_case_ = depths
snake_case_ = num_heads
snake_case_ = window_size
snake_case_ = mlp_ratio
snake_case_ = qkv_bias
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = drop_path_rate
snake_case_ = hidden_act
snake_case_ = use_absolute_embeddings
snake_case_ = patch_norm
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = is_training
snake_case_ = scope
snake_case_ = use_labels
snake_case_ = type_sequence_label_size
snake_case_ = encoder_stride
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
snake_case_ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaForMaskedImageModeling(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case_ = 1
snake_case_ = SwinvaForMaskedImageModeling(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.type_sequence_label_size
snake_case_ = SwinvaForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
__A = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , embed_dim=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
snake_case_ = len(self.model_tester.depths )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = config.window_size**2
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
snake_case_ = len(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
if hasattr(self.model_tester , 'num_hidden_states_types' ):
snake_case_ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case_ = 2
self.assertEqual(out_len + added_hidden_states , len(__UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# Swinv2 has a different seq_length
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
snake_case_ = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = reshaped_hidden_states[0].shape
snake_case_ = (
reshaped_hidden_states[0].view(__UpperCamelCase , __UpperCamelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = SwinvaModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = _config_zero_init(__UpperCamelCase )
for model_class in self.all_model_classes:
snake_case_ = model_class(config=__UpperCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
| 1
|
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
snake_case_ = ''
snake_case_ = ''
snake_case_ = []
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
snake_case_ = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
snake_case_ = self.__min_dist_top_down_dp(__UpperCamelCase , n - 1 )
snake_case_ = self.__min_dist_top_down_dp(m - 1 , __UpperCamelCase )
snake_case_ = self.__min_dist_top_down_dp(m - 1 , n - 1 )
snake_case_ = 1 + min(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self.dp[m][n]
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = worda
snake_case_ = worda
snake_case_ = [[-1 for _ in range(len(__UpperCamelCase ) )] for _ in range(len(__UpperCamelCase ) )]
return self.__min_dist_top_down_dp(len(__UpperCamelCase ) - 1 , len(__UpperCamelCase ) - 1 )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = worda
snake_case_ = worda
snake_case_ = len(__UpperCamelCase )
snake_case_ = len(__UpperCamelCase )
snake_case_ = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
snake_case_ = j
elif j == 0: # second string is empty
snake_case_ = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
snake_case_ = self.dp[i - 1][j - 1]
else:
snake_case_ = self.dp[i][j - 1]
snake_case_ = self.dp[i - 1][j]
snake_case_ = self.dp[i - 1][j - 1]
snake_case_ = 1 + min(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return self.dp[m][n]
if __name__ == "__main__":
A = EditDistance()
print('****************** Testing Edit Distance DP Algorithm ******************')
print()
A = input('Enter the first string: ').strip()
A = input('Enter the second string: ').strip()
print()
print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
print()
print('*************** End of Testing Edit Distance DP Algorithm ***************')
| 46
|
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A = parser.parse_args()
A = 'cpu'
A = 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'
A = 'path-to-your-trained-model'
A = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
A = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A = pipe.to(device)
# to channels last
A = pipe.unet.to(memory_format=torch.channels_last)
A = pipe.vae.to(memory_format=torch.channels_last)
A = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
A = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
A = torch.randn(2, 4, 64, 64)
A = torch.rand(1) * 999
A = torch.randn(2, 77, 768)
A = (sample, timestep, encoder_hidden_status)
try:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
A = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
A = 666
A = torch.Generator(device).manual_seed(seed)
A = {'generator': generator}
if args.steps is not None:
A = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
A = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('generated.png')
| 46
| 1
|
def a(lowercase__ ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if num == 0:
return "0b0"
snake_case_ = False
if num < 0:
snake_case_ = True
snake_case_ = -num
snake_case_ = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(lowercase__ ) for e in binary )
return "0b" + "".join(str(lowercase__ ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """unispeech-sat"""
def __init__( self , __UpperCamelCase=32 , __UpperCamelCase=7_68 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=30_72 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase="group" , __UpperCamelCase="gelu" , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) , __UpperCamelCase=False , __UpperCamelCase=1_28 , __UpperCamelCase=16 , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.05 , __UpperCamelCase=10 , __UpperCamelCase=2 , __UpperCamelCase=0.0 , __UpperCamelCase=10 , __UpperCamelCase=0 , __UpperCamelCase=3_20 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , __UpperCamelCase=1_00 , __UpperCamelCase=2_56 , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase="mean" , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=2_56 , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) , __UpperCamelCase=(5, 3, 3, 1, 1) , __UpperCamelCase=(1, 2, 3, 1, 1) , __UpperCamelCase=5_12 , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=2 , __UpperCamelCase=5_04 , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase , pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase )
snake_case_ = hidden_size
snake_case_ = feat_extract_norm
snake_case_ = feat_extract_activation
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = conv_bias
snake_case_ = num_conv_pos_embeddings
snake_case_ = num_conv_pos_embedding_groups
snake_case_ = len(self.conv_dim )
snake_case_ = num_hidden_layers
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = feat_proj_dropout
snake_case_ = final_dropout
snake_case_ = layerdrop
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = vocab_size
snake_case_ = num_clusters
snake_case_ = do_stable_layer_norm
snake_case_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = apply_spec_augment
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
snake_case_ = num_codevectors_per_group
snake_case_ = num_codevector_groups
snake_case_ = contrastive_logits_temperature
snake_case_ = feat_quantizer_dropout
snake_case_ = num_negatives
snake_case_ = codevector_dim
snake_case_ = proj_codevector_dim
snake_case_ = diversity_loss_weight
# ctc loss
snake_case_ = ctc_loss_reduction
snake_case_ = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = xvector_output_dim
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 46
| 1
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
A = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 46
|
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self.__class__.__name__}({self.name}, {self.val})"""
def __lt__( self , __UpperCamelCase ):
"""simple docstring"""
return self.val < other.val
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = {}
snake_case_ = {}
snake_case_ = self.build_heap(__UpperCamelCase )
def __getitem__( self , __UpperCamelCase ):
"""simple docstring"""
return self.get_value(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return (idx - 1) // 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 1
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return self.heap_dict[key]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) - 1
snake_case_ = self.get_parent_idx(__UpperCamelCase )
for idx, i in enumerate(__UpperCamelCase ):
snake_case_ = idx
snake_case_ = i.val
for i in range(__UpperCamelCase , -1 , -1 ):
self.sift_down(__UpperCamelCase , __UpperCamelCase )
return array
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
while True:
snake_case_ = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741
snake_case_ = self.get_right_child_idx(__UpperCamelCase )
snake_case_ = idx
if l < len(__UpperCamelCase ) and array[l] < array[idx]:
snake_case_ = l
if r < len(__UpperCamelCase ) and array[r] < array[smallest]:
snake_case_ = r
if smallest != idx:
snake_case_ , snake_case_ = array[smallest], array[idx]
(
(
snake_case_
) , (
snake_case_
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
snake_case_ = smallest
else:
break
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.get_parent_idx(__UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
snake_case_ , snake_case_ = self.heap[idx], self.heap[p]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
snake_case_ = p
snake_case_ = self.get_parent_idx(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return self.heap[0]
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.heap[-1], self.heap[0]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
snake_case_ = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
self.heap.append(__UpperCamelCase )
snake_case_ = len(self.heap ) - 1
snake_case_ = node.val
self.sift_up(len(self.heap ) - 1 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.heap ) == 0
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
snake_case_ = new_value
snake_case_ = new_value
self.sift_up(self.idx_of_element[node] )
A = Node('R', -1)
A = Node('B', 6)
A = Node('A', 3)
A = Node('X', 1)
A = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
A = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 1
|
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
A = direct_transformers_import(PATH_TO_TRANSFORMERS)
A = transformers.models.auto.configuration_auto.CONFIG_MAPPING
A = {
# used to compute the property `self.chunk_length`
'EncodecConfig': ['overlap'],
# used as `self.bert_model = BertModel(config, ...)`
'DPRConfig': True,
# not used in modeling files, but it's an important information
'FSMTConfig': ['langs'],
# used internally in the configuration class file
'GPTNeoConfig': ['attention_types'],
# used internally in the configuration class file
'EsmConfig': ['is_folding_model'],
# used during training (despite we don't have training script for these models yet)
'Mask2FormerConfig': ['ignore_value'],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
'OneFormerConfig': ['ignore_value', 'norm'],
# used during preprocessing and collation, see `collating_graphormer.py`
'GraphormerConfig': ['spatial_pos_max'],
# used internally in the configuration class file
'T5Config': ['feed_forward_proj'],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
# used internally in the configuration class file
'LongT5Config': ['feed_forward_proj'],
# used internally in the configuration class file
'SwitchTransformersConfig': ['feed_forward_proj'],
# having default values other than `1e-5` - we can't fix them without breaking
'BioGptConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'GLPNConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'SegformerConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'CvtConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'PerceiverConfig': ['layer_norm_eps'],
# used internally to calculate the feature size
'InformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate the feature size
'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate the feature size
'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate `mlp_dim`
'SamVisionConfig': ['mlp_ratio'],
# For (head) training, but so far not implemented
'ClapAudioConfig': ['num_classes'],
# Not used, but providing useful information to users
'SpeechT5HifiGanConfig': ['sampling_rate'],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
'CLIPSegConfig': True,
'DeformableDetrConfig': True,
'DetaConfig': True,
'DinatConfig': True,
'DonutSwinConfig': True,
'EfficientFormerConfig': True,
'FSMTConfig': True,
'JukeboxConfig': True,
'LayoutLMv2Config': True,
'MaskFormerSwinConfig': True,
'MT5Config': True,
'NatConfig': True,
'OneFormerConfig': True,
'PerceiverConfig': True,
'RagConfig': True,
'SpeechT5Config': True,
'SwinConfig': True,
'Swin2SRConfig': True,
'Swinv2Config': True,
'SwitchTransformersConfig': True,
'TableTransformerConfig': True,
'TapasConfig': True,
'TransfoXLConfig': True,
'UniSpeechConfig': True,
'UniSpeechSatConfig': True,
'WavLMConfig': True,
'WhisperConfig': True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
'JukeboxPriorConfig': True,
# TODO: @Younes (for `is_decoder`)
'Pix2StructTextConfig': True,
}
)
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f"""config.{attribute}""" in modeling_source
or f"""getattr(config, \"{attribute}\"""" in modeling_source
or f"""getattr(self.config, \"{attribute}\"""" in modeling_source
):
snake_case_ = True
# Deal with multi-line cases
elif (
re.search(
Rf"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"""" , lowercase__ , )
is not None
):
snake_case_ = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
snake_case_ = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
snake_case_ = [
'bos_index',
'eos_index',
'pad_index',
'unk_index',
'mask_index',
'image_size',
'use_cache',
'out_features',
'out_indices',
]
snake_case_ = ['encoder_no_repeat_ngram_size']
# Special cases to be allowed
snake_case_ = True
if not attribute_used:
snake_case_ = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
snake_case_ = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
snake_case_ = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
snake_case_ = True
elif attribute.endswith('_token_id' ):
snake_case_ = True
# configuration class specific cases
if not case_allowed:
snake_case_ = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
snake_case_ = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = dict(inspect.signature(config_class.__init__ ).parameters )
snake_case_ = [x for x in list(signature.keys() ) if x not in ['self', 'kwargs']]
snake_case_ = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
snake_case_ = {}
if len(config_class.attribute_map ) > 0:
snake_case_ = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
snake_case_ = inspect.getsourcefile(lowercase__ )
snake_case_ = os.path.dirname(lowercase__ )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
snake_case_ = [os.path.join(lowercase__ , lowercase__ ) for fn in os.listdir(lowercase__ ) if fn.startswith('modeling_' )]
# Get the source code strings
snake_case_ = []
for path in modeling_paths:
if os.path.isfile(lowercase__ ):
with open(lowercase__ ) as fp:
modeling_sources.append(fp.read() )
snake_case_ = []
for config_param, default_value in zip(lowercase__ , lowercase__ ):
# `attributes` here is all the variant names for `config_param`
snake_case_ = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
unused_attributes.append(attributes[0] )
return sorted(lowercase__ )
def a():
'''simple docstring'''
snake_case_ = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
snake_case_ = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda lowercase__ : inspect.isclass(lowercase__ )
and issubclass(lowercase__ , lowercase__ )
and inspect.getmodule(lowercase__ ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
snake_case_ = check_config_attributes_being_used(lowercase__ )
if len(lowercase__ ) > 0:
snake_case_ = unused_attributes
if len(lowercase__ ) > 0:
snake_case_ = 'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
for name, attributes in configs_with_unused_attributes.items():
error += f"""{name}: {attributes}\n"""
raise ValueError(lowercase__ )
if __name__ == "__main__":
check_config_attributes()
| 46
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['PerceiverFeatureExtractor']
A = ['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 1
|
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=3 , __UpperCamelCase=32 , __UpperCamelCase=3 , __UpperCamelCase=10 , __UpperCamelCase=[8, 16, 32, 64] , __UpperCamelCase=[1, 1, 2, 1] , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase="relu" , __UpperCamelCase=3 , __UpperCamelCase=None , __UpperCamelCase=["stage2", "stage3", "stage4"] , __UpperCamelCase=[2, 3, 4] , __UpperCamelCase=1 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = num_channels
snake_case_ = embeddings_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = hidden_act
snake_case_ = num_labels
snake_case_ = scope
snake_case_ = len(__UpperCamelCase )
snake_case_ = out_features
snake_case_ = out_indices
snake_case_ = num_groups
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = BitModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = BitForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = BitBackbone(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
snake_case_ = None
snake_case_ = BitBackbone(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
__A = (
{"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = BitModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCAmelCase ( self ):
"""simple docstring"""
return
@unittest.skip(reason='Bit does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Bit does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Bit does not support input and output embeddings' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(config=__UpperCamelCase )
for name, module in model.named_modules():
if isinstance(__UpperCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case_ = self.model_tester.num_stages
self.assertEqual(len(__UpperCamelCase ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = ['preactivation', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
snake_case_ = layer_type
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@unittest.skip(reason='Bit does not use feedforward chunking' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = BitModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (BitBackbone,) if is_torch_available() else ()
__A = BitConfig
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = BitModelTester(self )
| 46
|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
snake_case_ = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 1
|
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
warnings.warn(
'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use LayoutLMv2ImageProcessor instead.' , __UpperCamelCase , )
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
| 46
|
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ = int(factor * num_class_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 )
os.makedirs(f"""{class_data_dir}/images""" , exist_ok=lowercase__ )
if len(list(Path(f"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
snake_case_ = client.query(text=lowercase__ )
if len(lowercase__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
snake_case_ = int(factor * num_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 , )
snake_case_ = 0
snake_case_ = 0
snake_case_ = tqdm(desc='downloading real regularization images' , total=lowercase__ )
with open(f"""{class_data_dir}/caption.txt""" , 'w' ) as fa, open(f"""{class_data_dir}/urls.txt""" , 'w' ) as fa, open(
f"""{class_data_dir}/images.txt""" , 'w' ) as fa:
while total < num_class_images:
snake_case_ = class_images[count]
count += 1
try:
snake_case_ = requests.get(images['url'] )
if img.status_code == 200:
snake_case_ = Image.open(BytesIO(img.content ) )
with open(f"""{class_data_dir}/images/{total}.jpg""" , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def a():
'''simple docstring'''
snake_case_ = argparse.ArgumentParser('' , add_help=lowercase__ )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--class_data_dir' , help='path to save images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--num_class_images' , help='number of images to download' , default=200 , type=lowercase__ )
return parser.parse_args()
if __name__ == "__main__":
A = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 46
| 1
|
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 46
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
A = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 46
| 1
|
def a(lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('check_bouncy() accepts only integer arguments' )
snake_case_ = str(lowercase__ )
snake_case_ = ''.join(sorted(lowercase__ ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def a(lowercase__ = 99 ):
'''simple docstring'''
if not 0 < percent < 100:
raise ValueError('solution() only accepts values from 0 to 100' )
snake_case_ = 0
snake_case_ = 1
while True:
if check_bouncy(lowercase__ ):
bouncy_num += 1
if (bouncy_num / num) * 100 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"""{solution(99)}""")
| 46
|
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__A = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(default=__snake_case , metadata={"""help""": """The input training data file (a text file)."""} )
__A = field(
default=__snake_case , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__A = field(
default=__snake_case , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
if self.train_file is not None:
snake_case_ = self.train_file.split('.' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
snake_case_ = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = 42
__A = True
__A = None
__A = None
def __call__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 'label' if 'label' in features[0].keys() else 'labels'
snake_case_ = [feature.pop(__UpperCamelCase ) for feature in features]
snake_case_ = len(__UpperCamelCase )
snake_case_ = len(features[0]['input_ids'] )
snake_case_ = [
[{k: v[i] for k, v in feature.items()} for i in range(__UpperCamelCase )] for feature in features
]
snake_case_ = list(chain(*__UpperCamelCase ) )
snake_case_ = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
# Un-flatten
snake_case_ = {k: v.view(__UpperCamelCase , __UpperCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
snake_case_ = torch.tensor(__UpperCamelCase , dtype=torch.intaa )
return batch
def a():
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_ , snake_case_ , snake_case_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ , snake_case_ , snake_case_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_swag' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
datasets.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
snake_case_ = {}
if data_args.train_file is not None:
snake_case_ = data_args.train_file
if data_args.validation_file is not None:
snake_case_ = data_args.validation_file
snake_case_ = data_args.train_file.split('.' )[-1]
snake_case_ = load_dataset(
lowercase__ , data_files=lowercase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
snake_case_ = load_dataset(
'swag' , 'regular' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
snake_case_ = [f"""ending{i}""" for i in range(4 )]
snake_case_ = 'sent1'
snake_case_ = 'sent2'
if data_args.max_seq_length is None:
snake_case_ = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'
' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'
' override this default with `--block_size xxx`.' )
snake_case_ = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case_ = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowercase__ ):
snake_case_ = [[context] * 4 for context in examples[context_name]]
snake_case_ = examples[question_header_name]
snake_case_ = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase__ )
]
# Flatten out
snake_case_ = list(chain(*lowercase__ ) )
snake_case_ = list(chain(*lowercase__ ) )
# Tokenize
snake_case_ = tokenizer(
lowercase__ , lowercase__ , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowercase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case_ = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_train_samples )
snake_case_ = train_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
snake_case_ = train_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case_ = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_eval_samples )
snake_case_ = eval_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
snake_case_ = eval_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
snake_case_ = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowercase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowercase__ ):
snake_case_ , snake_case_ = eval_predictions
snake_case_ = np.argmax(lowercase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
snake_case_ = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , compute_metrics=lowercase__ , )
# Training
if training_args.do_train:
snake_case_ = None
if training_args.resume_from_checkpoint is not None:
snake_case_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ = last_checkpoint
snake_case_ = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case_ = train_result.metrics
snake_case_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase__ )
)
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('train' , lowercase__ )
trainer.save_metrics('train' , lowercase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case_ = trainer.evaluate()
snake_case_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase__ )
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
snake_case_ = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'multiple-choice',
'dataset_tags': 'swag',
'dataset_args': 'regular',
'dataset': 'SWAG',
'language': 'en',
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 46
| 1
|
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
A = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0.bias', 'time_embedding.linear_1.bias'),
('time_embed.2.weight', 'time_embedding.linear_2.weight'),
('time_embed.2.bias', 'time_embedding.linear_2.bias'),
('input_blocks.0.0.weight', 'conv_in.weight'),
('input_blocks.0.0.bias', 'conv_in.bias'),
('out.0.weight', 'conv_norm_out.weight'),
('out.0.bias', 'conv_norm_out.bias'),
('out.2.weight', 'conv_out.weight'),
('out.2.bias', 'conv_out.bias'),
]
A = [
# (stable-diffusion, HF Diffusers)
('in_layers.0', 'norm1'),
('in_layers.2', 'conv1'),
('out_layers.0', 'norm2'),
('out_layers.3', 'conv2'),
('emb_layers.1', 'time_emb_proj'),
('skip_connection', 'conv_shortcut'),
]
A = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
A = f"""down_blocks.{i}.resnets.{j}."""
A = f"""input_blocks.{3*i + j + 1}.0."""
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
A = f"""down_blocks.{i}.attentions.{j}."""
A = f"""input_blocks.{3*i + j + 1}.1."""
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
A = f"""up_blocks.{i}.resnets.{j}."""
A = f"""output_blocks.{3*i + j}.0."""
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
A = f"""up_blocks.{i}.attentions.{j}."""
A = f"""output_blocks.{3*i + j}.1."""
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
A = f"""down_blocks.{i}.downsamplers.0.conv."""
A = f"""input_blocks.{3*(i+1)}.0.op."""
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
A = f"""up_blocks.{i}.upsamplers.0."""
A = f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
A = 'mid_block.attentions.0.'
A = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
A = f"""mid_block.resnets.{j}."""
A = f"""middle_block.{2*j}."""
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def a(lowercase__ ):
'''simple docstring'''
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
snake_case_ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
snake_case_ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
snake_case_ = v.replace(lowercase__ , lowercase__ )
snake_case_ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
snake_case_ = v.replace(lowercase__ , lowercase__ )
snake_case_ = v
snake_case_ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
A = [
# (stable-diffusion, HF Diffusers)
('nin_shortcut', 'conv_shortcut'),
('norm_out', 'conv_norm_out'),
('mid.attn_1.', 'mid_block.attentions.0.'),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
A = f"""encoder.down_blocks.{i}.resnets.{j}."""
A = f"""encoder.down.{i}.block.{j}."""
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
A = f"""down_blocks.{i}.downsamplers.0."""
A = f"""down.{i}.downsample."""
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
A = f"""up_blocks.{i}.upsamplers.0."""
A = f"""up.{3-i}.upsample."""
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
A = f"""decoder.up_blocks.{i}.resnets.{j}."""
A = f"""decoder.up.{3-i}.block.{j}."""
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
A = f"""mid_block.resnets.{i}."""
A = f"""mid.block_{i+1}."""
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
A = [
# (stable-diffusion, HF Diffusers)
('norm.', 'group_norm.'),
('q.', 'query.'),
('k.', 'key.'),
('v.', 'value.'),
('proj_out.', 'proj_attn.'),
]
def a(lowercase__ ):
'''simple docstring'''
# convert HF linear weights to SD conv2d weights
return w.reshape(*w.shape , 1 , 1 )
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
snake_case_ = v.replace(lowercase__ , lowercase__ )
snake_case_ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
snake_case_ = v.replace(lowercase__ , lowercase__ )
snake_case_ = v
snake_case_ = {v: vae_state_dict[k] for k, v in mapping.items()}
snake_case_ = ['q', 'k', 'v', 'proj_out']
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f"""mid.attn_1.{weight_name}.weight""" in k:
print(f"""Reshaping {k} for SD format""" )
snake_case_ = reshape_weight_for_sd(lowercase__ )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
A = [
# (stable-diffusion, HF Diffusers)
('resblocks.', 'text_model.encoder.layers.'),
('ln_1', 'layer_norm1'),
('ln_2', 'layer_norm2'),
('.c_fc.', '.fc1.'),
('.c_proj.', '.fc2.'),
('.attn', '.self_attn'),
('ln_final.', 'transformer.text_model.final_layer_norm.'),
('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
A = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A = re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A = {'q': 0, 'k': 1, 'v': 2}
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {}
snake_case_ = {}
snake_case_ = {}
for k, v in text_enc_dict.items():
if (
k.endswith('.self_attn.q_proj.weight' )
or k.endswith('.self_attn.k_proj.weight' )
or k.endswith('.self_attn.v_proj.weight' )
):
snake_case_ = k[: -len('.q_proj.weight' )]
snake_case_ = k[-len('q_proj.weight' )]
if k_pre not in capture_qkv_weight:
snake_case_ = [None, None, None]
snake_case_ = v
continue
if (
k.endswith('.self_attn.q_proj.bias' )
or k.endswith('.self_attn.k_proj.bias' )
or k.endswith('.self_attn.v_proj.bias' )
):
snake_case_ = k[: -len('.q_proj.bias' )]
snake_case_ = k[-len('q_proj.bias' )]
if k_pre not in capture_qkv_bias:
snake_case_ = [None, None, None]
snake_case_ = v
continue
snake_case_ = textenc_pattern.sub(lambda lowercase__ : protected[re.escape(m.group(0 ) )] , lowercase__ )
snake_case_ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing' )
snake_case_ = textenc_pattern.sub(lambda lowercase__ : protected[re.escape(m.group(0 ) )] , lowercase__ )
snake_case_ = torch.cat(lowercase__ )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing' )
snake_case_ = textenc_pattern.sub(lambda lowercase__ : protected[re.escape(m.group(0 ) )] , lowercase__ )
snake_case_ = torch.cat(lowercase__ )
return new_state_dict
def a(lowercase__ ):
'''simple docstring'''
return text_enc_dict
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
A = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
A = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
A = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
A = osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
A = load_file(unet_path, device='cpu')
else:
A = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
A = torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
A = load_file(vae_path, device='cpu')
else:
A = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
A = torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
A = load_file(text_enc_path, device='cpu')
else:
A = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
A = torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
A = convert_unet_state_dict(unet_state_dict)
A = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
A = convert_vae_state_dict(vae_state_dict)
A = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
A = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
A = {'transformer.' + k: v for k, v in text_enc_dict.items()}
A = convert_text_enc_state_dict_vaa(text_enc_dict)
A = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
else:
A = convert_text_enc_state_dict(text_enc_dict)
A = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
A = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
A = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
A = {'state_dict': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 46
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ASTForAudioClassification',
'ASTModel',
'ASTPreTrainedModel',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['ASTFeatureExtractor']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 1
|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = ["""image_processor""", """tokenizer"""]
__A = """AutoImageProcessor"""
__A = """AutoTokenizer"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
super().__init__(__UpperCamelCase , __UpperCamelCase )
snake_case_ = self.image_processor
def __call__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase ):
"""simple docstring"""
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
snake_case_ = self.tokenizer(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
if images is not None:
snake_case_ = self.image_processor(__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase )
if text is not None and images is not None:
snake_case_ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCamelCase ) , tensor_type=__UpperCamelCase )
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return ["input_ids", "attention_mask", "pixel_values"]
| 46
|
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
else:
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
A = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 46
| 1
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """unispeech-sat"""
def __init__( self , __UpperCamelCase=32 , __UpperCamelCase=7_68 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=30_72 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase="group" , __UpperCamelCase="gelu" , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) , __UpperCamelCase=False , __UpperCamelCase=1_28 , __UpperCamelCase=16 , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.05 , __UpperCamelCase=10 , __UpperCamelCase=2 , __UpperCamelCase=0.0 , __UpperCamelCase=10 , __UpperCamelCase=0 , __UpperCamelCase=3_20 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , __UpperCamelCase=1_00 , __UpperCamelCase=2_56 , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase="mean" , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=2_56 , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) , __UpperCamelCase=(5, 3, 3, 1, 1) , __UpperCamelCase=(1, 2, 3, 1, 1) , __UpperCamelCase=5_12 , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=2 , __UpperCamelCase=5_04 , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase , pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase )
snake_case_ = hidden_size
snake_case_ = feat_extract_norm
snake_case_ = feat_extract_activation
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = conv_bias
snake_case_ = num_conv_pos_embeddings
snake_case_ = num_conv_pos_embedding_groups
snake_case_ = len(self.conv_dim )
snake_case_ = num_hidden_layers
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = feat_proj_dropout
snake_case_ = final_dropout
snake_case_ = layerdrop
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = vocab_size
snake_case_ = num_clusters
snake_case_ = do_stable_layer_norm
snake_case_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = apply_spec_augment
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
snake_case_ = num_codevectors_per_group
snake_case_ = num_codevector_groups
snake_case_ = contrastive_logits_temperature
snake_case_ = feat_quantizer_dropout
snake_case_ = num_negatives
snake_case_ = codevector_dim
snake_case_ = proj_codevector_dim
snake_case_ = diversity_loss_weight
# ctc loss
snake_case_ = ctc_loss_reduction
snake_case_ = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = xvector_output_dim
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 46
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
__A = """bit"""
__A = ["""preactivation""", """bottleneck"""]
__A = ["""SAME""", """VALID"""]
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=64 , __UpperCamelCase=[2_56, 5_12, 10_24, 20_48] , __UpperCamelCase=[3, 4, 6, 3] , __UpperCamelCase="preactivation" , __UpperCamelCase="relu" , __UpperCamelCase=None , __UpperCamelCase=32 , __UpperCamelCase=0.0 , __UpperCamelCase=False , __UpperCamelCase=32 , __UpperCamelCase=1 , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
snake_case_ = global_padding.upper()
else:
raise ValueError(f"""Padding strategy {global_padding} not supported""" )
snake_case_ = num_channels
snake_case_ = embedding_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = layer_type
snake_case_ = hidden_act
snake_case_ = global_padding
snake_case_ = num_groups
snake_case_ = drop_path_rate
snake_case_ = embedding_dynamic_padding
snake_case_ = output_stride
snake_case_ = width_factor
snake_case_ = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(__UpperCamelCase ) + 1 )]
snake_case_ , snake_case_ = get_aligned_output_features_output_indices(
out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
| 46
| 1
|
from jiwer import compute_measures
import datasets
A = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
A = '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'
A = '\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> wer = datasets.load_metric("wer")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/jitsi/jiwer/'] , reference_urls=[
'https://en.wikipedia.org/wiki/Word_error_rate',
] , )
def __lowerCAmelCase ( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=False ):
"""simple docstring"""
if concatenate_texts:
return compute_measures(__UpperCamelCase , __UpperCamelCase )["wer"]
else:
snake_case_ = 0
snake_case_ = 0
for prediction, reference in zip(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = compute_measures(__UpperCamelCase , __UpperCamelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 46
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = DDIMScheduler()
snake_case_ = self.dummy_vq_model
snake_case_ = LDMPipeline(unet=__UpperCamelCase , vqvae=__UpperCamelCase , scheduler=__UpperCamelCase )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' , return_dict=__UpperCamelCase )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=5 , output_type='numpy' ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 46
| 1
|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
def get_matched_characters(lowercase__ , lowercase__ ) -> str:
snake_case_ = []
snake_case_ = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ = int(max(0 , i - limit ) )
snake_case_ = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase__ )
snake_case_ = f"""{_stra[0:_stra.index(lowercase__ )]} {_stra[_stra.index(lowercase__ ) + 1:]}"""
return "".join(lowercase__ )
# matching characters
snake_case_ = get_matched_characters(lowercase__ , lowercase__ )
snake_case_ = get_matched_characters(lowercase__ , lowercase__ )
snake_case_ = len(lowercase__ )
# transposition
snake_case_ = (
len([(ca, ca) for ca, ca in zip(lowercase__ , lowercase__ ) if ca != ca] ) // 2
)
if not match_count:
snake_case_ = 0.0
else:
snake_case_ = (
1
/ 3
* (
match_count / len(lowercase__ )
+ match_count / len(lowercase__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
snake_case_ = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 46
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = LEDConfig
__A = {}
__A = """gelu"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=20 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=4 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
snake_case_ = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
snake_case_ = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
snake_case_ = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
snake_case_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
snake_case_ = tf.concat([input_ids, eos_tensor] , axis=1 )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
snake_case_ = prepare_led_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = tf.concat(
[tf.zeros_like(__UpperCamelCase )[:, :-1], tf.ones_like(__UpperCamelCase )[:, -1:]] , axis=-1 , )
snake_case_ = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFLEDModel(config=__UpperCamelCase ).get_decoder()
snake_case_ = inputs_dict['input_ids']
snake_case_ = input_ids[:1, :]
snake_case_ = inputs_dict['attention_mask'][:1, :]
snake_case_ = 1
# first forward pass
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
snake_case_ = tf.concat([input_ids, next_tokens] , axis=-1 )
snake_case_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
snake_case_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
snake_case_ = output_from_no_past[:, -3:, random_slice_idx]
snake_case_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-3 )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
__A = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
__A = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A = True
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = tf.zeros_like(inputs_dict['attention_mask'] )
snake_case_ = 2
snake_case_ = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['global_attention_mask'] , )
snake_case_ = True
snake_case_ = self.model_tester.seq_length
snake_case_ = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__UpperCamelCase ):
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__UpperCamelCase ):
snake_case_ = [t.numpy() for t in outputs.encoder_attentions]
snake_case_ = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = len(__UpperCamelCase )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_decoder_attentions_output(__UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def a(lowercase__ ):
'''simple docstring'''
return tf.constant(lowercase__ , dtype=tf.intaa )
A = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, 7_68)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 , rtol=1E-3 )
| 46
| 1
|
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__A = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(default=__snake_case , metadata={"""help""": """The input training data file (a text file)."""} )
__A = field(
default=__snake_case , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__A = field(
default=__snake_case , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
if self.train_file is not None:
snake_case_ = self.train_file.split('.' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
snake_case_ = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = 42
__A = True
__A = None
__A = None
def __call__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 'label' if 'label' in features[0].keys() else 'labels'
snake_case_ = [feature.pop(__UpperCamelCase ) for feature in features]
snake_case_ = len(__UpperCamelCase )
snake_case_ = len(features[0]['input_ids'] )
snake_case_ = [
[{k: v[i] for k, v in feature.items()} for i in range(__UpperCamelCase )] for feature in features
]
snake_case_ = list(chain(*__UpperCamelCase ) )
snake_case_ = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
# Un-flatten
snake_case_ = {k: v.view(__UpperCamelCase , __UpperCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
snake_case_ = torch.tensor(__UpperCamelCase , dtype=torch.intaa )
return batch
def a():
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_ , snake_case_ , snake_case_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ , snake_case_ , snake_case_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_swag' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
datasets.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
snake_case_ = {}
if data_args.train_file is not None:
snake_case_ = data_args.train_file
if data_args.validation_file is not None:
snake_case_ = data_args.validation_file
snake_case_ = data_args.train_file.split('.' )[-1]
snake_case_ = load_dataset(
lowercase__ , data_files=lowercase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
snake_case_ = load_dataset(
'swag' , 'regular' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
snake_case_ = [f"""ending{i}""" for i in range(4 )]
snake_case_ = 'sent1'
snake_case_ = 'sent2'
if data_args.max_seq_length is None:
snake_case_ = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'
' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'
' override this default with `--block_size xxx`.' )
snake_case_ = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case_ = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowercase__ ):
snake_case_ = [[context] * 4 for context in examples[context_name]]
snake_case_ = examples[question_header_name]
snake_case_ = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase__ )
]
# Flatten out
snake_case_ = list(chain(*lowercase__ ) )
snake_case_ = list(chain(*lowercase__ ) )
# Tokenize
snake_case_ = tokenizer(
lowercase__ , lowercase__ , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowercase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case_ = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_train_samples )
snake_case_ = train_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
snake_case_ = train_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case_ = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_eval_samples )
snake_case_ = eval_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
snake_case_ = eval_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
snake_case_ = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowercase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowercase__ ):
snake_case_ , snake_case_ = eval_predictions
snake_case_ = np.argmax(lowercase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
snake_case_ = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , compute_metrics=lowercase__ , )
# Training
if training_args.do_train:
snake_case_ = None
if training_args.resume_from_checkpoint is not None:
snake_case_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ = last_checkpoint
snake_case_ = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case_ = train_result.metrics
snake_case_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase__ )
)
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('train' , lowercase__ )
trainer.save_metrics('train' , lowercase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case_ = trainer.evaluate()
snake_case_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase__ )
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
snake_case_ = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'multiple-choice',
'dataset_tags': 'swag',
'dataset_args': 'regular',
'dataset': 'SWAG',
'language': 'en',
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 46
|
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replace(' ' , '' )
# Strings of different lengths are not anagrams
if len(lowercase__ ) != len(lowercase__ ):
return False
# Default values for count should be 0
snake_case_ = defaultdict(lowercase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowercase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
A = input('Enter the first string ').strip()
A = input('Enter the second string ').strip()
A = check_anagrams(input_a, input_b)
print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 46
| 1
|
import os
from distutils.util import strtobool
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
for e in env_keys:
snake_case_ = int(os.environ.get(lowercase__ , -1 ) )
if val >= 0:
return val
return default
def a(lowercase__ , lowercase__=False ):
'''simple docstring'''
snake_case_ = os.environ.get(lowercase__ , str(lowercase__ ) )
return strtobool(lowercase__ ) == 1 # As its name indicates `strtobool` actually returns an int...
def a(lowercase__ , lowercase__="no" ):
'''simple docstring'''
snake_case_ = os.environ.get(lowercase__ , str(lowercase__ ) )
return value
| 46
|
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = ScoreSdeVeScheduler()
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase , return_dict=__UpperCamelCase )[
0
]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'google/ncsnpp-church-256'
snake_case_ = UNetaDModel.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 46
| 1
|
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=12 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=0.02 , __UpperCamelCase=0 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = projection_dim
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = max_position_embeddings
snake_case_ = initializer_range
snake_case_ = scope
snake_case_ = bos_token_id
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
snake_case_ = input_mask.numpy()
snake_case_ , snake_case_ = input_mask.shape
snake_case_ = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__UpperCamelCase ):
snake_case_ = 1
snake_case_ = 0
snake_case_ = self.get_config()
return config, input_ids, tf.convert_to_tensor(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFBlipTextModel(config=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , training=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase , training=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (TFBlipTextModel,) if is_tf_available() else ()
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = BlipTextModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Blip does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = TFBlipTextModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase=True ):
"""simple docstring"""
super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCamelCase )
| 46
|
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
@register_to_config
def __init__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None ):
"""simple docstring"""
super().__init__()
snake_case_ = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
snake_case_ = torch.zeros(__UpperCamelCase , __UpperCamelCase )
else:
snake_case_ = None
snake_case_ = torch.nn.Parameter(__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
super().__init__()
self.register_modules(
vqvae=__UpperCamelCase , transformer=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , scheduler=__UpperCamelCase , learned_classifier_free_sampling_embeddings=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else 1
# get prompt text embeddings
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
snake_case_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
snake_case_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
snake_case_ = text_input_ids[:, : self.tokenizer.model_max_length]
snake_case_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
snake_case_ = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate text embeddings for each generation per prompt
snake_case_ = prompt_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
snake_case_ = self.learned_classifier_free_sampling_embeddings.embeddings
snake_case_ = negative_prompt_embeds.unsqueeze(0 ).repeat(__UpperCamelCase , 1 , 1 )
else:
snake_case_ = [''] * batch_size
snake_case_ = text_input_ids.shape[-1]
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors='pt' , )
snake_case_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
snake_case_ = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case_ = negative_prompt_embeds.shape[1]
snake_case_ = negative_prompt_embeds.repeat(1 , __UpperCamelCase , 1 )
snake_case_ = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __UpperCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case_ = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , __UpperCamelCase , __UpperCamelCase = 1_00 , __UpperCamelCase = 5.0 , __UpperCamelCase = 1.0 , __UpperCamelCase = 1 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = "pil" , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = 1 , ):
"""simple docstring"""
if isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = 1
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = len(__UpperCamelCase )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase )}""" )
snake_case_ = batch_size * num_images_per_prompt
snake_case_ = guidance_scale > 1.0
snake_case_ = self._encode_prompt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__UpperCamelCase , __UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__UpperCamelCase )}.""" )
# get the initial completely masked latents unless the user supplied it
snake_case_ = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
snake_case_ = self.transformer.num_vector_embeds - 1
snake_case_ = torch.full(__UpperCamelCase , __UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
f""" {self.transformer.num_vector_embeds - 1} (inclusive).""" )
snake_case_ = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__UpperCamelCase , device=self.device )
snake_case_ = self.scheduler.timesteps.to(self.device )
snake_case_ = latents
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
snake_case_ = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
snake_case_ = self.transformer(__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , timestep=__UpperCamelCase ).sample
if do_classifier_free_guidance:
snake_case_ , snake_case_ = model_output.chunk(2 )
snake_case_ = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__UpperCamelCase , dim=1 , keepdim=__UpperCamelCase )
snake_case_ = self.truncate(__UpperCamelCase , __UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
snake_case_ = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(__UpperCamelCase , timestep=__UpperCamelCase , sample=__UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = self.vqvae.config.vq_embed_dim
snake_case_ = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
snake_case_ = self.vqvae.quantize.get_codebook_entry(__UpperCamelCase , shape=__UpperCamelCase )
snake_case_ = self.vqvae.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase ).sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ , snake_case_ = torch.sort(__UpperCamelCase , 1 , descending=__UpperCamelCase )
snake_case_ = torch.exp(__UpperCamelCase )
snake_case_ = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
snake_case_ = torch.full_like(keep_mask[:, 0:1, :] , __UpperCamelCase )
snake_case_ = torch.cat((all_true, keep_mask) , dim=1 )
snake_case_ = keep_mask[:, :-1, :]
snake_case_ = keep_mask.gather(1 , indices.argsort(1 ) )
snake_case_ = log_p_x_0.clone()
snake_case_ = -torch.inf # -inf = log(0)
return rv
| 46
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'ResNetForImageClassification',
'ResNetModel',
'ResNetPreTrainedModel',
'ResNetBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFResNetForImageClassification',
'TFResNetModel',
'TFResNetPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'FlaxResNetForImageClassification',
'FlaxResNetModel',
'FlaxResNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 46
|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'neck_hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'num_attention_heads' ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=6_40 , __UpperCamelCase=4 , __UpperCamelCase="silu" , __UpperCamelCase=3 , __UpperCamelCase=32 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = last_hidden_size
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = conv_kernel_size
snake_case_ = output_stride
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = classifier_dropout_prob
snake_case_ = use_labels
snake_case_ = is_training
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MobileViTModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForSemanticSegmentation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__A = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTModelTester(self )
snake_case_ = MobileViTConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = 5
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case_ = 2
for i in range(len(__UpperCamelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MobileViTModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits
# verify the logits
snake_case_ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=__UpperCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits.detach().cpu()
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase , target_sizes=[(50, 60)] )
snake_case_ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase )
snake_case_ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
| 46
| 1
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A = logging.get_logger(__name__)
A = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
A = {
'vocab_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
},
'merges_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
},
}
A = {'allegro/herbert-base-cased': 514}
A = {}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_INIT_CONFIGURATION
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = HerbertTokenizer
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase="<s>" , __UpperCamelCase="<unk>" , __UpperCamelCase="<pad>" , __UpperCamelCase="<mask>" , __UpperCamelCase="</s>" , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(
__UpperCamelCase , __UpperCamelCase , tokenizer_file=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , sep_token=__UpperCamelCase , **__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
snake_case_ = [self.cls_token_id]
snake_case_ = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__UpperCamelCase )) + [1]
return [1] + ([0] * len(__UpperCamelCase )) + [1] + ([0] * len(__UpperCamelCase )) + [1]
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
snake_case_ = [self.sep_token_id]
snake_case_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
snake_case_ = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
| 46
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 46
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegatronBertForCausalLM',
'MegatronBertForMaskedLM',
'MegatronBertForMultipleChoice',
'MegatronBertForNextSentencePrediction',
'MegatronBertForPreTraining',
'MegatronBertForQuestionAnswering',
'MegatronBertForSequenceClassification',
'MegatronBertForTokenClassification',
'MegatronBertModel',
'MegatronBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
|
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
snake_case_ = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
snake_case_ = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
snake_case_ = tf_top_k_top_p_filtering(__UpperCamelCase , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
snake_case_ = output[output != -float('inf' )]
snake_case_ = tf.cast(
tf.where(tf.not_equal(__UpperCamelCase , tf.constant(-float('inf' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-12 )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase , __snake_case ):
"""simple docstring"""
if is_tf_available():
__A = {
"""AutoModelForCausalLM""": TFAutoModelForCausalLM,
"""AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
"""AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
"""AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
"""LogitsProcessorList""": TFLogitsProcessorList,
"""MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
"""create_tensor_fn""": tf.convert_to_tensor,
"""floats_tensor""": floats_tensor,
"""return_tensors""": """tf""",
}
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 2
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='input_ids' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2, 0], [1_02, 1_03]]
snake_case_ = [[1, 0], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for batch_size in range(1 , len(__UpperCamelCase ) + 1 ):
snake_case_ = {
'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 1
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='input_ids' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2], [1_02, 1_03]]
snake_case_ = [[1], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for input_row in range(len(__UpperCamelCase ) ):
snake_case_ = {
'input_ids': tf.constant([dummy_input_ids[input_row]] ),
'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
@require_tensorflow_text
def __lowerCAmelCase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='google/flan-t5-small' , filename='spiece.model' , local_dir=__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
super().__init__()
snake_case_ = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(__UpperCamelCase , 'spiece.model' ) , 'rb' ).read() )
snake_case_ = TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )
def __lowerCAmelCase ( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.tokenizer.tokenize(__UpperCamelCase )
snake_case_ , snake_case_ = text.pad_model_inputs(
__UpperCamelCase , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
snake_case_ = self.model.generate(input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase )
return self.tokenizer.detokenize(__UpperCamelCase )
snake_case_ = CompleteSentenceTransformer()
snake_case_ = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='inputs' )
snake_case_ = complete_model(__UpperCamelCase )
snake_case_ = tf.keras.Model(__UpperCamelCase , __UpperCamelCase )
keras_model.save(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = {
'do_sample': True,
'num_beams': 1,
'top_p': 0.7,
'top_k': 10,
'temperature': 0.7,
}
snake_case_ = 14
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 'Hello, my dog is cute and'
snake_case_ = tokenizer(__UpperCamelCase , return_tensors='tf' )
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
snake_case_ = [6_38, 1_98]
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = 'Hugging Face is a technology company based in New York and Paris.'
snake_case_ = bart_tokenizer(__UpperCamelCase , return_tensors='tf' ).input_ids
snake_case_ = TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=None , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase , foo='bar' ).numpy()
self.assertTrue(np.array_equal(__UpperCamelCase , __UpperCamelCase ) )
class SCREAMING_SNAKE_CASE ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeEncoder(bart_model.config , bart_model.model.shared )
snake_case_ = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
with self.assertRaises(__UpperCamelCase ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(__UpperCamelCase , foo='bar' )
| 46
| 1
|
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
A = logging.get_logger(__name__)
A = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A = {
'vocab_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
A = {
'vocab_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
A = {
'vocab_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
),
},
}
A = {
'facebook/dpr-ctx_encoder-single-nq-base': 512,
'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A = {
'facebook/dpr-question_encoder-single-nq-base': 512,
'facebook/dpr-question_encoder-multiset-base': 512,
}
A = {
'facebook/dpr-reader-single-nq-base': 512,
'facebook/dpr-reader-multiset-base': 512,
}
A = {
'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A = {
'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A = {
'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = VOCAB_FILES_NAMES
__A = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__A = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = VOCAB_FILES_NAMES
__A = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__A = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
A = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
A = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
A = R'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n '
@add_start_docstrings(__snake_case )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __call__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors=__UpperCamelCase , return_attention_mask=__UpperCamelCase , **__UpperCamelCase , )
elif titles is None or texts is None:
snake_case_ = titles if texts is None else texts
return super().__call__(
__UpperCamelCase , __UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors=__UpperCamelCase , return_attention_mask=__UpperCamelCase , **__UpperCamelCase , )
snake_case_ = titles if not isinstance(__UpperCamelCase , __UpperCamelCase ) else [titles]
snake_case_ = texts if not isinstance(__UpperCamelCase , __UpperCamelCase ) else [texts]
snake_case_ = len(__UpperCamelCase )
snake_case_ = questions if not isinstance(__UpperCamelCase , __UpperCamelCase ) else [questions] * n_passages
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError(
f"""There should be as many titles than texts but got {len(__UpperCamelCase )} titles and {len(__UpperCamelCase )} texts.""" )
snake_case_ = super().__call__(__UpperCamelCase , __UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase )['input_ids']
snake_case_ = super().__call__(__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase )['input_ids']
snake_case_ = {
'input_ids': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(__UpperCamelCase , __UpperCamelCase )
]
}
if return_attention_mask is not False:
snake_case_ = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
snake_case_ = attention_mask
return self.pad(__UpperCamelCase , padding=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 16 , __UpperCamelCase = 64 , __UpperCamelCase = 4 , ):
"""simple docstring"""
snake_case_ = reader_input['input_ids']
snake_case_ , snake_case_ , snake_case_ = reader_output[:3]
snake_case_ = len(__UpperCamelCase )
snake_case_ = sorted(range(__UpperCamelCase ) , reverse=__UpperCamelCase , key=relevance_logits.__getitem__ )
snake_case_ = []
for doc_id in sorted_docs:
snake_case_ = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
snake_case_ = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
snake_case_ = sequence_ids.index(self.pad_token_id )
else:
snake_case_ = len(__UpperCamelCase )
snake_case_ = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=__UpperCamelCase , top_spans=__UpperCamelCase , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=__UpperCamelCase , start_index=__UpperCamelCase , end_index=__UpperCamelCase , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(__UpperCamelCase ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = []
for start_index, start_score in enumerate(__UpperCamelCase ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
snake_case_ = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : x[1] , reverse=__UpperCamelCase )
snake_case_ = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""" )
snake_case_ = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f"""Span is too long: {length} > {max_answer_length}""" )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(__UpperCamelCase ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__snake_case )
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
__A = VOCAB_FILES_NAMES
__A = READER_PRETRAINED_VOCAB_FILES_MAP
__A = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = READER_PRETRAINED_INIT_CONFIGURATION
__A = ["""input_ids""", """attention_mask"""]
| 46
|
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=2 , __UpperCamelCase=8 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=16 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=36 , __UpperCamelCase="gelu" , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_config()
snake_case_ = 3_00
return config
def __lowerCAmelCase ( self ):
"""simple docstring"""
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = self.prepare_config_and_inputs()
snake_case_ = True
snake_case_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = MraModel(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , )
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , start_positions=__UpperCamelCase , end_positions=__UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_choices
snake_case_ = MraForMultipleChoice(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
__A = False
__A = False
__A = False
__A = False
__A = ()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MraModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@unittest.skip(reason='MRA does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModel.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = torch.Size((1, 2_56, 7_68) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 2_56, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
snake_case_ = torch.arange(40_96 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 40_96, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
| 1
|
from __future__ import annotations
from collections.abc import Callable
A = list[list[float | int]]
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = len(SCREAMING_SNAKE_CASE_ )
snake_case_ = [[0 for _ in range(size + 1 )] for _ in range(SCREAMING_SNAKE_CASE_ )]
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
for row in range(SCREAMING_SNAKE_CASE_ ):
for col in range(SCREAMING_SNAKE_CASE_ ):
snake_case_ = matrix[row][col]
snake_case_ = vector[row][0]
snake_case_ = 0
snake_case_ = 0
while row < size and col < size:
# pivoting
snake_case_ = max((abs(augmented[rowa][col] ), rowa) for rowa in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
snake_case_ , snake_case_ = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , SCREAMING_SNAKE_CASE_ ):
snake_case_ = augmented[rowa][col] / augmented[row][col]
snake_case_ = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , SCREAMING_SNAKE_CASE_ ):
for row in range(SCREAMING_SNAKE_CASE_ ):
snake_case_ = augmented[row][col] / augmented[col][col]
for cola in range(SCREAMING_SNAKE_CASE_ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(SCREAMING_SNAKE_CASE_ )
]
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = len(SCREAMING_SNAKE_CASE_ )
snake_case_ = [[0 for _ in range(SCREAMING_SNAKE_CASE_ )] for _ in range(SCREAMING_SNAKE_CASE_ )]
snake_case_ = [[0] for _ in range(SCREAMING_SNAKE_CASE_ )]
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
for x_val, y_val in enumerate(SCREAMING_SNAKE_CASE_ ):
for col in range(SCREAMING_SNAKE_CASE_ ):
snake_case_ = (x_val + 1) ** (size - col - 1)
snake_case_ = y_val
snake_case_ = solve(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def interpolated_func(lowercase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(SCREAMING_SNAKE_CASE_ ) )
return interpolated_func
def a(lowercase__ ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def a(lowercase__ = question_function , lowercase__ = 10 ):
'''simple docstring'''
snake_case_ = [func(SCREAMING_SNAKE_CASE_ ) for x_val in range(1 , order + 1 )]
snake_case_ = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
snake_case_ = 0
snake_case_ = 42
snake_case_ = 42
for poly in polynomials:
snake_case_ = 1
while func(SCREAMING_SNAKE_CASE_ ) == poly(SCREAMING_SNAKE_CASE_ ):
x_val += 1
ret += poly(SCREAMING_SNAKE_CASE_ )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 700
|
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
snake_case_ = TapasConfig.from_json_file(lowercase__ )
# set absolute/relative position embeddings parameter
snake_case_ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WTQ":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = True
# hparam_utils.py hparams
snake_case_ = 0.66_4694
snake_case_ = 0.20_7951
snake_case_ = 0.12_1194
snake_case_ = True
snake_case_ = True
snake_case_ = False
snake_case_ = 0.035_2513
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = False
# hparam_utils.py hparams
snake_case_ = 36.4519
snake_case_ = 0.90_3421
snake_case_ = 222.088
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 0.76_3141
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "TABFACT":
snake_case_ = TapasForSequenceClassification(config=lowercase__ )
elif task == "MLM":
snake_case_ = TapasForMaskedLM(config=lowercase__ )
elif task == "INTERMEDIATE_PRETRAINING":
snake_case_ = TapasModel(config=lowercase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
snake_case_ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' , model_max_length=512 )
tokenizer.save_pretrained(lowercase__ )
print('Used relative position embeddings:' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
)
parser.add_argument(
'--reset_position_index_per_cell',
default=False,
action='store_true',
help='Whether to use relative position embeddings or not. Defaults to True.',
)
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--tapas_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained TAPAS model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 46
| 0
|
import math
import sys
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = ''
try:
with open(__A , 'rb' ) as binary_file:
snake_case_ = binary_file.read()
for dat in data:
snake_case_ = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {'0': '0', '1': '1'}
snake_case_ , snake_case_ = '', ''
snake_case_ = len(__A )
for i in range(len(__A ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
snake_case_ = lexicon[curr_string]
result += last_match_id
snake_case_ = last_match_id + '0'
if math.loga(__A ).is_integer():
snake_case_ = {}
for curr_key in list(__A ):
snake_case_ = lexicon.pop(__A )
snake_case_ = new_lex
snake_case_ = last_match_id + '1'
index += 1
snake_case_ = ''
return result
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 8
try:
with open(__A , 'wb' ) as opened_file:
snake_case_ = [
to_write[i : i + byte_length]
for i in range(0 , len(__A ) , __A )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(__A , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
snake_case_ = data_bits[counter:]
snake_case_ = data_bits[counter + 1 :]
return data_bits
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = read_file_binary(__A )
snake_case_ = remove_prefix(__A )
snake_case_ = decompress_data(__A )
write_file_binary(__A , __A )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 701
|
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=16 , __UpperCamelCase=[1, 2, 1] , __UpperCamelCase=[2, 2, 4] , __UpperCamelCase=2 , __UpperCamelCase=2.0 , __UpperCamelCase=True , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase="gelu" , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=8 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = embed_dim
snake_case_ = depths
snake_case_ = num_heads
snake_case_ = window_size
snake_case_ = mlp_ratio
snake_case_ = qkv_bias
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = drop_path_rate
snake_case_ = hidden_act
snake_case_ = use_absolute_embeddings
snake_case_ = patch_norm
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = is_training
snake_case_ = scope
snake_case_ = use_labels
snake_case_ = type_sequence_label_size
snake_case_ = encoder_stride
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
snake_case_ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaForMaskedImageModeling(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case_ = 1
snake_case_ = SwinvaForMaskedImageModeling(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.type_sequence_label_size
snake_case_ = SwinvaForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
__A = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , embed_dim=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
snake_case_ = len(self.model_tester.depths )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = config.window_size**2
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
snake_case_ = len(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
if hasattr(self.model_tester , 'num_hidden_states_types' ):
snake_case_ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case_ = 2
self.assertEqual(out_len + added_hidden_states , len(__UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# Swinv2 has a different seq_length
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
snake_case_ = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = reshaped_hidden_states[0].shape
snake_case_ = (
reshaped_hidden_states[0].view(__UpperCamelCase , __UpperCamelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = SwinvaModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = _config_zero_init(__UpperCamelCase )
for model_class in self.all_model_classes:
snake_case_ = model_class(config=__UpperCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
| 0
|
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE ( snake_case__ ):
"""simple docstring"""
__A = """"""
__A = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(self , **UpperCAmelCase_ )
snake_case_ = repo_info
snake_case_ = token
snake_case_ = None
def __lowerCAmelCase ( self ):
"""simple docstring"""
if self.dir_cache is None:
snake_case_ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
snake_case_ = {
'name': hf_file.rfilename,
'size': None,
'type': 'file',
}
self.dir_cache.update(
{
str(UpperCAmelCase_ ): {'name': str(UpperCAmelCase_ ), 'size': None, 'type': 'directory'}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = "rb" , **__UpperCamelCase , ):
"""simple docstring"""
if not isinstance(self.repo_info , UpperCAmelCase_ ):
raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
snake_case_ = hf_hub_url(self.repo_info.id , UpperCAmelCase_ , revision=self.repo_info.sha )
return fsspec.open(
UpperCAmelCase_ , mode=UpperCAmelCase_ , headers=get_authentication_headers_for_url(UpperCAmelCase_ , use_auth_token=self.token ) , client_kwargs={'trust_env': True} , ).open()
def __lowerCAmelCase ( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
self._get_dirs()
snake_case_ = self._strip_protocol(UpperCAmelCase_ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCAmelCase_ )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=False , **__UpperCamelCase ):
"""simple docstring"""
self._get_dirs()
snake_case_ = PurePosixPath(path.strip('/' ) )
snake_case_ = {}
for p, f in self.dir_cache.items():
snake_case_ = PurePosixPath(p.strip('/' ) )
snake_case_ = p.parent
if root == path:
snake_case_ = f
snake_case_ = list(paths.values() )
if detail:
return out
else:
return sorted(f['name'] for f in out )
| 702
|
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A = parser.parse_args()
A = 'cpu'
A = 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'
A = 'path-to-your-trained-model'
A = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
A = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A = pipe.to(device)
# to channels last
A = pipe.unet.to(memory_format=torch.channels_last)
A = pipe.vae.to(memory_format=torch.channels_last)
A = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
A = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
A = torch.randn(2, 4, 64, 64)
A = torch.rand(1) * 999
A = torch.randn(2, 77, 768)
A = (sample, timestep, encoder_hidden_status)
try:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
A = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
A = 666
A = torch.Generator(device).manual_seed(seed)
A = {'generator': generator}
if args.steps is not None:
A = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
A = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('generated.png')
| 46
| 0
|
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
__A = AudioLDMPipeline
__A = TEXT_TO_AUDIO_PARAMS
__A = TEXT_TO_AUDIO_BATCH_PARAMS
__A = frozenset(
[
"""num_inference_steps""",
"""num_waveforms_per_prompt""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=(32, 64) , class_embed_type='simple_projection' , projection_class_embeddings_input_dim=32 , class_embeddings_concat=A_ , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=A_ , set_alpha_to_one=A_ , )
torch.manual_seed(0 )
snake_case_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case_ = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , projection_dim=32 , )
snake_case_ = ClapTextModelWithProjection(A_ )
snake_case_ = RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta' , model_max_length=77 )
snake_case_ = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_60_00 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=A_ , )
snake_case_ = SpeechTaHifiGan(A_ )
snake_case_ = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'vocoder': vocoder,
}
return components
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=0 ):
"""simple docstring"""
if str(A_ ).startswith('mps' ):
snake_case_ = torch.manual_seed(A_ )
else:
snake_case_ = torch.Generator(device=A_ ).manual_seed(A_ )
snake_case_ = {
'prompt': 'A hammer hitting a wooden surface',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
}
return inputs
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = AudioLDMPipeline(**A_ )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = self.get_dummy_inputs(A_ )
snake_case_ = audioldm_pipe(**A_ )
snake_case_ = output.audios[0]
assert audio.ndim == 1
assert len(A_ ) == 2_56
snake_case_ = audio[:10]
snake_case_ = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_dummy_components()
snake_case_ = AudioLDMPipeline(**A_ )
snake_case_ = audioldm_pipe.to(A_ )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = self.get_dummy_inputs(A_ )
snake_case_ = 3 * [inputs['prompt']]
# forward
snake_case_ = audioldm_pipe(**A_ )
snake_case_ = output.audios[0]
snake_case_ = self.get_dummy_inputs(A_ )
snake_case_ = 3 * [inputs.pop('prompt' )]
snake_case_ = audioldm_pipe.tokenizer(
A_ , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=A_ , return_tensors='pt' , )
snake_case_ = text_inputs['input_ids'].to(A_ )
snake_case_ = audioldm_pipe.text_encoder(
A_ , )
snake_case_ = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
snake_case_ = F.normalize(A_ , dim=-1 )
snake_case_ = prompt_embeds
# forward
snake_case_ = audioldm_pipe(**A_ )
snake_case_ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_dummy_components()
snake_case_ = AudioLDMPipeline(**A_ )
snake_case_ = audioldm_pipe.to(A_ )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = self.get_dummy_inputs(A_ )
snake_case_ = 3 * ['this is a negative prompt']
snake_case_ = negative_prompt
snake_case_ = 3 * [inputs['prompt']]
# forward
snake_case_ = audioldm_pipe(**A_ )
snake_case_ = output.audios[0]
snake_case_ = self.get_dummy_inputs(A_ )
snake_case_ = 3 * [inputs.pop('prompt' )]
snake_case_ = []
for p in [prompt, negative_prompt]:
snake_case_ = audioldm_pipe.tokenizer(
A_ , padding='max_length' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=A_ , return_tensors='pt' , )
snake_case_ = text_inputs['input_ids'].to(A_ )
snake_case_ = audioldm_pipe.text_encoder(
A_ , )
snake_case_ = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
snake_case_ = F.normalize(A_ , dim=-1 )
embeds.append(A_ )
snake_case_ , snake_case_ = embeds
# forward
snake_case_ = audioldm_pipe(**A_ )
snake_case_ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = PNDMScheduler(skip_prk_steps=A_ )
snake_case_ = AudioLDMPipeline(**A_ )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = self.get_dummy_inputs(A_ )
snake_case_ = 'egg cracking'
snake_case_ = audioldm_pipe(**A_ , negative_prompt=A_ )
snake_case_ = output.audios[0]
assert audio.ndim == 1
assert len(A_ ) == 2_56
snake_case_ = audio[:10]
snake_case_ = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = PNDMScheduler(skip_prk_steps=A_ )
snake_case_ = AudioLDMPipeline(**A_ )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = 'A hammer hitting a wooden surface'
# test num_waveforms_per_prompt=1 (default)
snake_case_ = audioldm_pipe(A_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 2_56)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
snake_case_ = 2
snake_case_ = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 2_56)
# test num_waveforms_per_prompt for single prompt
snake_case_ = 2
snake_case_ = audioldm_pipe(A_ , num_inference_steps=2 , num_waveforms_per_prompt=A_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 2_56)
# test num_waveforms_per_prompt for batch of prompts
snake_case_ = 2
snake_case_ = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=A_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = AudioLDMPipeline(**A_ )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = audioldm_pipe.vocoder.config.sampling_rate
snake_case_ = self.get_dummy_inputs(A_ )
snake_case_ = audioldm_pipe(audio_length_in_s=0.016 , **A_ )
snake_case_ = output.audios[0]
assert audio.ndim == 1
assert len(A_ ) / vocoder_sampling_rate == 0.016
snake_case_ = audioldm_pipe(audio_length_in_s=0.032 , **A_ )
snake_case_ = output.audios[0]
assert audio.ndim == 1
assert len(A_ ) / vocoder_sampling_rate == 0.032
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_dummy_components()
snake_case_ = AudioLDMPipeline(**A_ )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = ['hey']
snake_case_ = audioldm_pipe(A_ , num_inference_steps=1 )
snake_case_ = output.audios.shape
assert audio_shape == (1, 2_56)
snake_case_ = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
snake_case_ = SpeechTaHifiGan(A_ ).to(A_ )
snake_case_ = audioldm_pipe(A_ , num_inference_steps=1 )
snake_case_ = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 2_56)
def __lowerCAmelCase ( self ):
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=A_ )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_ )
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase="cpu" , __UpperCamelCase=torch.floataa , __UpperCamelCase=0 ):
"""simple docstring"""
snake_case_ = torch.Generator(device=A_ ).manual_seed(A_ )
snake_case_ = np.random.RandomState(A_ ).standard_normal((1, 8, 1_28, 16) )
snake_case_ = torch.from_numpy(A_ ).to(device=A_ , dtype=A_ )
snake_case_ = {
'prompt': 'A hammer hitting a wooden surface',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 2.5,
}
return inputs
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = self.get_inputs(A_ )
snake_case_ = 25
snake_case_ = audioldm_pipe(**A_ ).audios[0]
assert audio.ndim == 1
assert len(A_ ) == 8_19_20
snake_case_ = audio[7_72_30:7_72_40]
snake_case_ = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
snake_case_ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1E-2
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = AudioLDMPipeline.from_pretrained('cvssp/audioldm' )
snake_case_ = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
snake_case_ = audioldm_pipe.to(A_ )
audioldm_pipe.set_progress_bar_config(disable=A_ )
snake_case_ = self.get_inputs(A_ )
snake_case_ = audioldm_pipe(**A_ ).audios[0]
assert audio.ndim == 1
assert len(A_ ) == 8_19_20
snake_case_ = audio[2_77_80:2_77_90]
snake_case_ = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
snake_case_ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3E-2
| 703
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """unispeech-sat"""
def __init__( self , __UpperCamelCase=32 , __UpperCamelCase=7_68 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=30_72 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase="group" , __UpperCamelCase="gelu" , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) , __UpperCamelCase=False , __UpperCamelCase=1_28 , __UpperCamelCase=16 , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.05 , __UpperCamelCase=10 , __UpperCamelCase=2 , __UpperCamelCase=0.0 , __UpperCamelCase=10 , __UpperCamelCase=0 , __UpperCamelCase=3_20 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , __UpperCamelCase=1_00 , __UpperCamelCase=2_56 , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase="mean" , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=2_56 , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) , __UpperCamelCase=(5, 3, 3, 1, 1) , __UpperCamelCase=(1, 2, 3, 1, 1) , __UpperCamelCase=5_12 , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=2 , __UpperCamelCase=5_04 , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase , pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase )
snake_case_ = hidden_size
snake_case_ = feat_extract_norm
snake_case_ = feat_extract_activation
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = conv_bias
snake_case_ = num_conv_pos_embeddings
snake_case_ = num_conv_pos_embedding_groups
snake_case_ = len(self.conv_dim )
snake_case_ = num_hidden_layers
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = feat_proj_dropout
snake_case_ = final_dropout
snake_case_ = layerdrop
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = vocab_size
snake_case_ = num_clusters
snake_case_ = do_stable_layer_norm
snake_case_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = apply_spec_augment
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
snake_case_ = num_codevectors_per_group
snake_case_ = num_codevector_groups
snake_case_ = contrastive_logits_temperature
snake_case_ = feat_quantizer_dropout
snake_case_ = num_negatives
snake_case_ = codevector_dim
snake_case_ = proj_codevector_dim
snake_case_ = diversity_loss_weight
# ctc loss
snake_case_ = ctc_loss_reduction
snake_case_ = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = xvector_output_dim
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 46
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['''GPTSw3Tokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 704
|
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self.__class__.__name__}({self.name}, {self.val})"""
def __lt__( self , __UpperCamelCase ):
"""simple docstring"""
return self.val < other.val
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = {}
snake_case_ = {}
snake_case_ = self.build_heap(__UpperCamelCase )
def __getitem__( self , __UpperCamelCase ):
"""simple docstring"""
return self.get_value(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return (idx - 1) // 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 1
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return self.heap_dict[key]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) - 1
snake_case_ = self.get_parent_idx(__UpperCamelCase )
for idx, i in enumerate(__UpperCamelCase ):
snake_case_ = idx
snake_case_ = i.val
for i in range(__UpperCamelCase , -1 , -1 ):
self.sift_down(__UpperCamelCase , __UpperCamelCase )
return array
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
while True:
snake_case_ = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741
snake_case_ = self.get_right_child_idx(__UpperCamelCase )
snake_case_ = idx
if l < len(__UpperCamelCase ) and array[l] < array[idx]:
snake_case_ = l
if r < len(__UpperCamelCase ) and array[r] < array[smallest]:
snake_case_ = r
if smallest != idx:
snake_case_ , snake_case_ = array[smallest], array[idx]
(
(
snake_case_
) , (
snake_case_
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
snake_case_ = smallest
else:
break
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.get_parent_idx(__UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
snake_case_ , snake_case_ = self.heap[idx], self.heap[p]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
snake_case_ = p
snake_case_ = self.get_parent_idx(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return self.heap[0]
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.heap[-1], self.heap[0]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
snake_case_ = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
self.heap.append(__UpperCamelCase )
snake_case_ = len(self.heap ) - 1
snake_case_ = node.val
self.sift_up(len(self.heap ) - 1 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.heap ) == 0
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
snake_case_ = new_value
snake_case_ = new_value
self.sift_up(self.idx_of_element[node] )
A = Node('R', -1)
A = Node('B', 6)
A = Node('A', 3)
A = Node('X', 1)
A = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
A = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 0
|
from typing import List
import numpy as np
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {key: len(_A ) for key, value in gen_kwargs.items() if isinstance(_A , _A )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'Sharding is ambiguous for this dataset: '
+ 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
+ '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items() )
+ '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
+ 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
) )
snake_case_ = max(lists_lengths.values() , default=0 )
return max(1 , _A )
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = []
for group_idx in range(_A ):
snake_case_ = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
snake_case_ = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
snake_case_ = range(_A , start + num_shards_to_add )
shards_indices_per_group.append(_A )
return shards_indices_per_group
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = _number_of_shards_in_gen_kwargs(_A )
if num_shards == 1:
return [dict(_A )]
else:
snake_case_ = _distribute_shards(num_shards=_A , max_num_jobs=_A )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(_A , _A )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(_A ) )
]
def a(lowercase__ ):
'''simple docstring'''
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , _A )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = {len(_A ) for value in gen_kwargs.values() if isinstance(_A , _A )}
snake_case_ = {}
for size in list_sizes:
snake_case_ = list(range(_A ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
snake_case_ = dict(_A )
for key, value in shuffled_kwargs.items():
if isinstance(_A , _A ):
snake_case_ = [value[i] for i in indices_per_size[len(_A )]]
return shuffled_kwargs
| 705
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['PerceiverFeatureExtractor']
A = ['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( __lowerCamelCase ):
"""simple docstring"""
__A = """efficientnet"""
def __init__( self , __UpperCamelCase = 3 , __UpperCamelCase = 6_00 , __UpperCamelCase = 2.0 , __UpperCamelCase = 3.1 , __UpperCamelCase = 8 , __UpperCamelCase = [3, 3, 5, 3, 5, 5, 3] , __UpperCamelCase = [32, 16, 24, 40, 80, 1_12, 1_92] , __UpperCamelCase = [16, 24, 40, 80, 1_12, 1_92, 3_20] , __UpperCamelCase = [] , __UpperCamelCase = [1, 2, 2, 2, 1, 2, 1] , __UpperCamelCase = [1, 2, 2, 3, 3, 4, 1] , __UpperCamelCase = [1, 6, 6, 6, 6, 6, 6] , __UpperCamelCase = 0.25 , __UpperCamelCase = "swish" , __UpperCamelCase = 25_60 , __UpperCamelCase = "mean" , __UpperCamelCase = 0.02 , __UpperCamelCase = 0.001 , __UpperCamelCase = 0.99 , __UpperCamelCase = 0.5 , __UpperCamelCase = 0.2 , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
snake_case_ = num_channels
snake_case_ = image_size
snake_case_ = width_coefficient
snake_case_ = depth_coefficient
snake_case_ = depth_divisor
snake_case_ = kernel_sizes
snake_case_ = in_channels
snake_case_ = out_channels
snake_case_ = depthwise_padding
snake_case_ = strides
snake_case_ = num_block_repeats
snake_case_ = expand_ratios
snake_case_ = squeeze_expansion_ratio
snake_case_ = hidden_act
snake_case_ = hidden_dim
snake_case_ = pooling_type
snake_case_ = initializer_range
snake_case_ = batch_norm_eps
snake_case_ = batch_norm_momentum
snake_case_ = dropout_rate
snake_case_ = drop_connect_rate
snake_case_ = sum(UpperCAmelCase_ ) * 4
class SCREAMING_SNAKE_CASE ( __lowerCamelCase ):
"""simple docstring"""
__A = version.parse("""1.11""" )
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return 1E-5
| 706
|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
snake_case_ = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 0
|
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
warnings.warn(
'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use PoolFormerImageProcessor instead.' , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 707
|
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ = int(factor * num_class_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 )
os.makedirs(f"""{class_data_dir}/images""" , exist_ok=lowercase__ )
if len(list(Path(f"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
snake_case_ = client.query(text=lowercase__ )
if len(lowercase__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
snake_case_ = int(factor * num_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 , )
snake_case_ = 0
snake_case_ = 0
snake_case_ = tqdm(desc='downloading real regularization images' , total=lowercase__ )
with open(f"""{class_data_dir}/caption.txt""" , 'w' ) as fa, open(f"""{class_data_dir}/urls.txt""" , 'w' ) as fa, open(
f"""{class_data_dir}/images.txt""" , 'w' ) as fa:
while total < num_class_images:
snake_case_ = class_images[count]
count += 1
try:
snake_case_ = requests.get(images['url'] )
if img.status_code == 200:
snake_case_ = Image.open(BytesIO(img.content ) )
with open(f"""{class_data_dir}/images/{total}.jpg""" , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def a():
'''simple docstring'''
snake_case_ = argparse.ArgumentParser('' , add_help=lowercase__ )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--class_data_dir' , help='path to save images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--num_class_images' , help='number of images to download' , default=200 , type=lowercase__ )
return parser.parse_args()
if __name__ == "__main__":
A = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 46
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMSNModel',
'ViTMSNForImageClassification',
'ViTMSNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 708
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
A = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 46
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
'LukeForEntityClassification',
'LukeForEntityPairClassification',
'LukeForEntitySpanClassification',
'LukeForMultipleChoice',
'LukeForQuestionAnswering',
'LukeForSequenceClassification',
'LukeForTokenClassification',
'LukeForMaskedLM',
'LukeModel',
'LukePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 709
|
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__A = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(default=__snake_case , metadata={"""help""": """The input training data file (a text file)."""} )
__A = field(
default=__snake_case , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__A = field(
default=__snake_case , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
if self.train_file is not None:
snake_case_ = self.train_file.split('.' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
snake_case_ = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = 42
__A = True
__A = None
__A = None
def __call__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 'label' if 'label' in features[0].keys() else 'labels'
snake_case_ = [feature.pop(__UpperCamelCase ) for feature in features]
snake_case_ = len(__UpperCamelCase )
snake_case_ = len(features[0]['input_ids'] )
snake_case_ = [
[{k: v[i] for k, v in feature.items()} for i in range(__UpperCamelCase )] for feature in features
]
snake_case_ = list(chain(*__UpperCamelCase ) )
snake_case_ = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
# Un-flatten
snake_case_ = {k: v.view(__UpperCamelCase , __UpperCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
snake_case_ = torch.tensor(__UpperCamelCase , dtype=torch.intaa )
return batch
def a():
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_ , snake_case_ , snake_case_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ , snake_case_ , snake_case_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_swag' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
datasets.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
snake_case_ = {}
if data_args.train_file is not None:
snake_case_ = data_args.train_file
if data_args.validation_file is not None:
snake_case_ = data_args.validation_file
snake_case_ = data_args.train_file.split('.' )[-1]
snake_case_ = load_dataset(
lowercase__ , data_files=lowercase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
snake_case_ = load_dataset(
'swag' , 'regular' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
snake_case_ = [f"""ending{i}""" for i in range(4 )]
snake_case_ = 'sent1'
snake_case_ = 'sent2'
if data_args.max_seq_length is None:
snake_case_ = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'
' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'
' override this default with `--block_size xxx`.' )
snake_case_ = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case_ = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowercase__ ):
snake_case_ = [[context] * 4 for context in examples[context_name]]
snake_case_ = examples[question_header_name]
snake_case_ = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase__ )
]
# Flatten out
snake_case_ = list(chain(*lowercase__ ) )
snake_case_ = list(chain(*lowercase__ ) )
# Tokenize
snake_case_ = tokenizer(
lowercase__ , lowercase__ , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowercase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case_ = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_train_samples )
snake_case_ = train_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
snake_case_ = train_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case_ = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_eval_samples )
snake_case_ = eval_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
snake_case_ = eval_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
snake_case_ = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowercase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowercase__ ):
snake_case_ , snake_case_ = eval_predictions
snake_case_ = np.argmax(lowercase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
snake_case_ = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , compute_metrics=lowercase__ , )
# Training
if training_args.do_train:
snake_case_ = None
if training_args.resume_from_checkpoint is not None:
snake_case_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ = last_checkpoint
snake_case_ = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case_ = train_result.metrics
snake_case_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase__ )
)
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('train' , lowercase__ )
trainer.save_metrics('train' , lowercase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case_ = trainer.evaluate()
snake_case_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase__ )
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
snake_case_ = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'multiple-choice',
'dataset_tags': 'swag',
'dataset_args': 'regular',
'dataset': 'SWAG',
'language': 'en',
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 46
| 0
|
import math
def a(lowercase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a(lowercase__ = 0.1 ):
'''simple docstring'''
snake_case_ = 3
snake_case_ = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(lowerCamelCase__ )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 710
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ASTForAudioClassification',
'ASTModel',
'ASTPreTrainedModel',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['ASTFeatureExtractor']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 0
|
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
A = logging.get_logger(__name__)
# General docstring
A = 'RegNetConfig'
# Base docstring
A = 'facebook/regnet-y-040'
A = [1, 1088, 7, 7]
# Image classification docstring
A = 'facebook/regnet-y-040'
A = 'tabby, tabby cat'
A = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase = 3 , __UpperCamelCase = 1 , __UpperCamelCase = 1 , __UpperCamelCase = "relu" , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
snake_case_ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
snake_case_ = tf.keras.layers.ConvaD(
filters=__A , kernel_size=__A , strides=__A , padding='VALID' , groups=__A , use_bias=__A , name='convolution' , )
snake_case_ = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='normalization' )
snake_case_ = ACTaFN[activation] if activation is not None else tf.identity
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.convolution(self.padding(__A ) )
snake_case_ = self.normalization(__A )
snake_case_ = self.activation(__A )
return hidden_state
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = config.num_channels
snake_case_ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='embedder' , )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = shape_list(__A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
snake_case_ = tf.transpose(__A , perm=(0, 2, 3, 1) )
snake_case_ = self.embedder(__A )
return hidden_state
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase = 2 , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = tf.keras.layers.ConvaD(
filters=__A , kernel_size=1 , strides=__A , use_bias=__A , name='convolution' )
snake_case_ = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='normalization' )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = False ):
"""simple docstring"""
return self.normalization(self.convolution(__A ) , training=__A )
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__A , name='pooler' )
snake_case_ = [
tf.keras.layers.ConvaD(filters=__A , kernel_size=1 , activation='relu' , name='attention.0' ),
tf.keras.layers.ConvaD(filters=__A , kernel_size=1 , activation='sigmoid' , name='attention.2' ),
]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.pooler(__A )
for layer_module in self.attention:
snake_case_ = layer_module(__A )
snake_case_ = hidden_state * pooled
return hidden_state
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = in_channels != out_channels or stride != 1
snake_case_ = max(1 , out_channels // config.groups_width )
snake_case_ = (
TFRegNetShortCut(__A , stride=__A , name='shortcut' )
if should_apply_shortcut
else tf.keras.layers.Activation('linear' , name='shortcut' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
snake_case_ = [
TFRegNetConvLayer(__A , kernel_size=1 , activation=config.hidden_act , name='layer.0' ),
TFRegNetConvLayer(
__A , stride=__A , groups=__A , activation=config.hidden_act , name='layer.1' ),
TFRegNetConvLayer(__A , kernel_size=1 , activation=__A , name='layer.2' ),
]
snake_case_ = ACTaFN[config.hidden_act]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = hidden_state
for layer_module in self.layers:
snake_case_ = layer_module(__A )
snake_case_ = self.shortcut(__A )
hidden_state += residual
snake_case_ = self.activation(__A )
return hidden_state
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1 , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = in_channels != out_channels or stride != 1
snake_case_ = max(1 , out_channels // config.groups_width )
snake_case_ = (
TFRegNetShortCut(__A , stride=__A , name='shortcut' )
if should_apply_shortcut
else tf.keras.layers.Activation('linear' , name='shortcut' )
)
snake_case_ = [
TFRegNetConvLayer(__A , kernel_size=1 , activation=config.hidden_act , name='layer.0' ),
TFRegNetConvLayer(
__A , stride=__A , groups=__A , activation=config.hidden_act , name='layer.1' ),
TFRegNetSELayer(__A , reduced_channels=int(round(in_channels / 4 ) ) , name='layer.2' ),
TFRegNetConvLayer(__A , kernel_size=1 , activation=__A , name='layer.3' ),
]
snake_case_ = ACTaFN[config.hidden_act]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = hidden_state
for layer_module in self.layers:
snake_case_ = layer_module(__A )
snake_case_ = self.shortcut(__A )
hidden_state += residual
snake_case_ = self.activation(__A )
return hidden_state
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 2 , __UpperCamelCase = 2 , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
snake_case_ = [
# downsampling is done in the first layer with stride of 2
layer(__A , __A , __A , stride=__A , name='layers.0' ),
*[layer(__A , __A , __A , name=f"""layers.{i+1}""" ) for i in range(depth - 1 )],
]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
for layer_module in self.layers:
snake_case_ = layer_module(__A )
return hidden_state
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
__A , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='stages.0' , ) )
snake_case_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(__A , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(__A , __A , __A , depth=__A , name=f"""stages.{i+1}""" ) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = False , __UpperCamelCase = True ):
"""simple docstring"""
snake_case_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
snake_case_ = hidden_states + (hidden_state,)
snake_case_ = stage_module(__A )
if output_hidden_states:
snake_case_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=__A , hidden_states=__A )
@keras_serializable
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
__A = RegNetConfig
def __init__( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(**__A )
snake_case_ = config
snake_case_ = TFRegNetEmbeddings(__A , name='embedder' )
snake_case_ = TFRegNetEncoder(__A , name='encoder' )
snake_case_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__A , name='pooler' )
@unpack_inputs
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , ):
"""simple docstring"""
snake_case_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = self.embedder(__A , training=__A )
snake_case_ = self.encoder(
__A , output_hidden_states=__A , return_dict=__A , training=__A )
snake_case_ = encoder_outputs[0]
snake_case_ = self.pooler(__A )
# Change to NCHW output format have uniformity in the modules
snake_case_ = tf.transpose(__A , perm=(0, 3, 1, 2) )
snake_case_ = tf.transpose(__A , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
snake_case_ = tuple([tf.transpose(__A , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__A , pooler_output=__A , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = RegNetConfig
__A = """regnet"""
__A = """pixel_values"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) , dtype=tf.floataa )}
A = R'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
A = R'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , __snake_case , )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(__A , *__A , **__A )
snake_case_ = TFRegNetMainLayer(__A , name='regnet' )
@unpack_inputs
@add_start_docstrings_to_model_forward(__A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__A , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase=False , ):
"""simple docstring"""
snake_case_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = self.regnet(
pixel_values=__A , output_hidden_states=__A , return_dict=__A , training=__A , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"""\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n """ , __snake_case , )
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(__A , *__A , **__A )
snake_case_ = config.num_labels
snake_case_ = TFRegNetMainLayer(__A , name='regnet' )
# classification head
snake_case_ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='classifier.1' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(__A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __lowerCAmelCase ( self , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase=False , ):
"""simple docstring"""
snake_case_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = self.regnet(
__A , output_hidden_states=__A , return_dict=__A , training=__A )
snake_case_ = outputs.pooler_output if return_dict else outputs[1]
snake_case_ = self.classifier[0](__A )
snake_case_ = self.classifier[1](__A )
snake_case_ = None if labels is None else self.hf_compute_loss(labels=__A , logits=__A )
if not return_dict:
snake_case_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=__A , logits=__A , hidden_states=outputs.hidden_states )
| 711
|
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
else:
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
A = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 46
| 0
|
A = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
snake_case_ = 0
snake_case_ = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
for arabic, roman in ROMAN:
(snake_case_) = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
__A = """bit"""
__A = ["""preactivation""", """bottleneck"""]
__A = ["""SAME""", """VALID"""]
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=64 , __UpperCamelCase=[2_56, 5_12, 10_24, 20_48] , __UpperCamelCase=[3, 4, 6, 3] , __UpperCamelCase="preactivation" , __UpperCamelCase="relu" , __UpperCamelCase=None , __UpperCamelCase=32 , __UpperCamelCase=0.0 , __UpperCamelCase=False , __UpperCamelCase=32 , __UpperCamelCase=1 , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
snake_case_ = global_padding.upper()
else:
raise ValueError(f"""Padding strategy {global_padding} not supported""" )
snake_case_ = num_channels
snake_case_ = embedding_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = layer_type
snake_case_ = hidden_act
snake_case_ = global_padding
snake_case_ = num_groups
snake_case_ = drop_path_rate
snake_case_ = embedding_dynamic_padding
snake_case_ = output_stride
snake_case_ = width_factor
snake_case_ = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(__UpperCamelCase ) + 1 )]
snake_case_ , snake_case_ = get_aligned_output_features_output_indices(
out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
| 46
| 0
|
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 713
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = DDIMScheduler()
snake_case_ = self.dummy_vq_model
snake_case_ = LDMPipeline(unet=__UpperCamelCase , vqvae=__UpperCamelCase , scheduler=__UpperCamelCase )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' , return_dict=__UpperCamelCase )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=5 , output_type='numpy' ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 46
| 0
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = LDMTextToImagePipeline
__A = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
__A = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
__A = TEXT_TO_IMAGE_BATCH_PARAMS
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
torch.manual_seed(0 )
snake_case_ = AutoencoderKL(
block_out_channels=(32, 64) , in_channels=3 , out_channels=3 , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , latent_channels=4 , )
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
snake_case_ = CLIPTextModel(_UpperCAmelCase )
snake_case_ = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
snake_case_ = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=0 ):
"""simple docstring"""
if str(_UpperCAmelCase ).startswith('mps' ):
snake_case_ = torch.manual_seed(_UpperCAmelCase )
else:
snake_case_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
snake_case_ = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = LDMTextToImagePipeline(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
snake_case_ = self.get_dummy_inputs(_UpperCAmelCase )
snake_case_ = pipe(**_UpperCAmelCase ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
snake_case_ = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=torch.floataa , __UpperCamelCase=0 ):
"""simple docstring"""
snake_case_ = torch.manual_seed(_UpperCAmelCase )
snake_case_ = np.random.RandomState(_UpperCAmelCase ).standard_normal((1, 4, 32, 32) )
snake_case_ = torch.from_numpy(_UpperCAmelCase ).to(device=_UpperCAmelCase , dtype=_UpperCAmelCase )
snake_case_ = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
snake_case_ = self.get_inputs(_UpperCAmelCase )
snake_case_ = pipe(**_UpperCAmelCase ).images
snake_case_ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878] )
snake_case_ = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=torch.floataa , __UpperCamelCase=0 ):
"""simple docstring"""
snake_case_ = torch.manual_seed(_UpperCAmelCase )
snake_case_ = np.random.RandomState(_UpperCAmelCase ).standard_normal((1, 4, 32, 32) )
snake_case_ = torch.from_numpy(_UpperCAmelCase ).to(device=_UpperCAmelCase , dtype=_UpperCAmelCase )
snake_case_ = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
snake_case_ = self.get_inputs(_UpperCAmelCase )
snake_case_ = pipe(**_UpperCAmelCase ).images[0]
snake_case_ = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
snake_case_ = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 714
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = LEDConfig
__A = {}
__A = """gelu"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=20 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=4 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
snake_case_ = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
snake_case_ = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
snake_case_ = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
snake_case_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
snake_case_ = tf.concat([input_ids, eos_tensor] , axis=1 )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
snake_case_ = prepare_led_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = tf.concat(
[tf.zeros_like(__UpperCamelCase )[:, :-1], tf.ones_like(__UpperCamelCase )[:, -1:]] , axis=-1 , )
snake_case_ = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFLEDModel(config=__UpperCamelCase ).get_decoder()
snake_case_ = inputs_dict['input_ids']
snake_case_ = input_ids[:1, :]
snake_case_ = inputs_dict['attention_mask'][:1, :]
snake_case_ = 1
# first forward pass
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
snake_case_ = tf.concat([input_ids, next_tokens] , axis=-1 )
snake_case_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
snake_case_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
snake_case_ = output_from_no_past[:, -3:, random_slice_idx]
snake_case_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-3 )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
__A = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
__A = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A = True
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = tf.zeros_like(inputs_dict['attention_mask'] )
snake_case_ = 2
snake_case_ = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['global_attention_mask'] , )
snake_case_ = True
snake_case_ = self.model_tester.seq_length
snake_case_ = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__UpperCamelCase ):
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__UpperCamelCase ):
snake_case_ = [t.numpy() for t in outputs.encoder_attentions]
snake_case_ = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = len(__UpperCamelCase )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_decoder_attentions_output(__UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def a(lowercase__ ):
'''simple docstring'''
return tf.constant(lowercase__ , dtype=tf.intaa )
A = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, 7_68)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 , rtol=1E-3 )
| 46
| 0
|
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def a():
'''simple docstring'''
snake_case_ = torch.nn.Linear(2 , 4 )
snake_case_ = torch.optim.AdamW(model.parameters() , lr=1.0 )
snake_case_ = torch.optim.lr_scheduler.OneCycleLR(__A , max_lr=0.01 , steps_per_epoch=2 , epochs=1 )
snake_case_ = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
snake_case_ = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def a(lowercase__ ):
'''simple docstring'''
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(__A )
class SCREAMING_SNAKE_CASE ( __lowerCAmelCase ):
"""simple docstring"""
@require_cuda
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(_UpperCamelCase ):
snake_case_ = Accelerator(cpu=_UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
snake_case_ = GradientState()
assert state.num_steps == 1
snake_case_ = 4
assert state.num_steps == 4
assert state.sync_gradients is True
snake_case_ = False
assert state.sync_gradients is False
GradientState._reset_state()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
snake_case_ = create_components()
(
snake_case_
) = accelerator.prepare(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
snake_case_ = create_components()
accelerator.prepare(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*__UpperCamelCase , **__UpperCamelCase ):
pass
with patch('torch.cuda.set_device' , _UpperCamelCase ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64' ):
snake_case_ = Accelerator()
self.assertEqual(str(accelerator.state.device ) , 'cuda:64' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
snake_case_ = create_components()
accelerator.prepare(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
snake_case_ = get_signature(_UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(_UpperCamelCase )
# make sure random weights don't match
load_random_weights(_UpperCamelCase )
self.assertTrue(abs(model_signature - get_signature(_UpperCamelCase ) ) > 1E-3 )
# make sure loaded weights match
accelerator.load_state(_UpperCamelCase )
self.assertTrue(abs(model_signature - get_signature(_UpperCamelCase ) ) < 1E-3 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
snake_case_ = create_components()
accelerator.prepare(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
snake_case_ = get_signature(_UpperCamelCase )
# saving hook
def save_config(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = {"""class_name""": models[0].__class__.__name__}
with open(os.path.join(_UpperCamelCase , 'data.json' ) , 'w' ) as f:
json.dump(_UpperCamelCase , _UpperCamelCase )
# loading hook
def load_config(__UpperCamelCase , __UpperCamelCase ):
with open(os.path.join(_UpperCamelCase , 'data.json' ) , 'r' ) as f:
snake_case_ = json.load(_UpperCamelCase )
snake_case_ = config["""class_name"""]
snake_case_ = accelerator.register_save_state_pre_hook(_UpperCamelCase )
snake_case_ = accelerator.register_load_state_pre_hook(_UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(_UpperCamelCase )
# make sure random weights don't match with hooks
load_random_weights(_UpperCamelCase )
self.assertTrue(abs(model_signature - get_signature(_UpperCamelCase ) ) > 1E-3 )
# random class name to verify correct one is loaded
snake_case_ = """random"""
# make sure loaded weights match with hooks
accelerator.load_state(_UpperCamelCase )
self.assertTrue(abs(model_signature - get_signature(_UpperCamelCase ) ) < 1E-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(_UpperCamelCase )
# make sure random weights don't match with hooks removed
load_random_weights(_UpperCamelCase )
self.assertTrue(abs(model_signature - get_signature(_UpperCamelCase ) ) > 1E-3 )
# random class name to verify correct one is loaded
snake_case_ = """random"""
# make sure loaded weights match with hooks removed
accelerator.load_state(_UpperCamelCase )
self.assertTrue(abs(model_signature - get_signature(_UpperCamelCase ) ) < 1E-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
snake_case_ = create_components()
snake_case_ = None
# This should work
snake_case_ = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
self.assertTrue(dummy_obj is None )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = Accelerator()
snake_case_ = create_components()
snake_case_ = [1, 2, 3]
# This should work
snake_case_ = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
self.assertEqual(
getattr(_UpperCamelCase , '_is_accelerate_prepared' , _UpperCamelCase ) , _UpperCamelCase , 'Dummy object should have `_is_accelerate_prepared` set to `True`' , )
self.assertEqual(
getattr(_UpperCamelCase , '_is_accelerate_prepared' , _UpperCamelCase ) , _UpperCamelCase , 'Model is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(_UpperCamelCase , '_is_accelerate_prepared' , _UpperCamelCase ) , _UpperCamelCase , 'Optimizer is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(_UpperCamelCase , '_is_accelerate_prepared' , _UpperCamelCase ) , _UpperCamelCase , 'Scheduler is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(_UpperCamelCase , '_is_accelerate_prepared' , _UpperCamelCase ) , _UpperCamelCase , 'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`' , )
self.assertEqual(
getattr(_UpperCamelCase , '_is_accelerate_prepared' , _UpperCamelCase ) , _UpperCamelCase , 'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`' , )
@slow
@require_bnb
def __lowerCAmelCase ( self ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
snake_case_ = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , load_in_abit=_UpperCamelCase , device_map={'': 0} , )
snake_case_ = Accelerator()
# This should work
snake_case_ = accelerator.prepare(_UpperCamelCase )
@slow
@require_bnb
def __lowerCAmelCase ( self ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
snake_case_ = Accelerator()
with init_empty_weights():
snake_case_ = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , )
model.tie_weights()
snake_case_ = infer_auto_device_map(_UpperCamelCase )
snake_case_ = """cpu"""
snake_case_ = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , device_map=_UpperCamelCase , load_in_abit=_UpperCamelCase , llm_inta_enable_fpaa_cpu_offload=_UpperCamelCase )
# This should not work and get value error
with self.assertRaises(_UpperCamelCase ):
snake_case_ = accelerator.prepare(_UpperCamelCase )
@slow
@require_bnb
@require_multi_gpu
def __lowerCAmelCase ( self ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
snake_case_ = {"""distributed_type""": DistributedType.MULTI_GPU}
with init_empty_weights():
snake_case_ = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , )
model.tie_weights()
snake_case_ = infer_auto_device_map(_UpperCamelCase )
snake_case_ = 1
snake_case_ = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , load_in_abit=_UpperCamelCase , device_map=_UpperCamelCase , )
snake_case_ = Accelerator()
# This should not work and get value error
with self.assertRaises(_UpperCamelCase ):
snake_case_ = accelerator.prepare(_UpperCamelCase )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def __lowerCAmelCase ( self ):
"""simple docstring"""
from transformers import AutoModelForCausalLM
with init_empty_weights():
snake_case_ = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , )
snake_case_ = infer_auto_device_map(_UpperCamelCase )
snake_case_ = 1
snake_case_ = AutoModelForCausalLM.from_pretrained(
'EleutherAI/gpt-neo-125m' , load_in_abit=_UpperCamelCase , device_map=_UpperCamelCase , )
snake_case_ = Accelerator()
# This should work
snake_case_ = accelerator.prepare(_UpperCamelCase )
@require_cuda
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = torch.nn.Linear(10 , 10 )
snake_case_ = torch.optim.SGD(model.parameters() , lr=0.01 )
snake_case_ = Accelerator(cpu=_UpperCamelCase )
snake_case_ = accelerator.prepare(_UpperCamelCase )
| 715
|
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replace(' ' , '' )
# Strings of different lengths are not anagrams
if len(lowercase__ ) != len(lowercase__ ):
return False
# Default values for count should be 0
snake_case_ = defaultdict(lowercase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowercase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
A = input('Enter the first string ').strip()
A = input('Enter the second string ').strip()
A = check_anagrams(input_a, input_b)
print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 46
| 0
|
from __future__ import annotations
from decimal import Decimal
from numpy import array
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(_lowerCamelCase ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
snake_case_ = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creates a copy of the matrix with swapped positions of the elements
snake_case_ = [[0.0, 0.0], [0.0, 0.0]]
snake_case_ = matrix[1][1], matrix[0][0]
snake_case_ = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(_lowerCamelCase ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(_lowerCamelCase ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
snake_case_ = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creating cofactor matrix
snake_case_ = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
snake_case_ = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
snake_case_ = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
snake_case_ = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
snake_case_ = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
snake_case_ = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
snake_case_ = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
snake_case_ = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
snake_case_ = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
snake_case_ = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
snake_case_ = array(_lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
snake_case_ = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
snake_case_ = array(_lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(_lowerCamelCase )
# Calculate the inverse of the matrix
return [[float(d(_lowerCamelCase ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
| 716
|
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = ScoreSdeVeScheduler()
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase , return_dict=__UpperCamelCase )[
0
]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'google/ncsnpp-church-256'
snake_case_ = UNetaDModel.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 46
| 0
|
'''simple docstring'''
from __future__ import annotations
def a(lowercase__ , lowercase__ = None , lowercase__ = None ):
'''simple docstring'''
if start is None:
snake_case_ = 0
if end is None:
snake_case_ = len(_lowerCamelCase ) - 1
if start >= end:
return
snake_case_ = (start + end) // 2
slowsort(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
slowsort(_lowerCamelCase , mid + 1 , _lowerCamelCase )
if sequence[end] < sequence[mid]:
snake_case_ = sequence[mid], sequence[end]
slowsort(_lowerCamelCase , _lowerCamelCase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 717
|
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
@register_to_config
def __init__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None ):
"""simple docstring"""
super().__init__()
snake_case_ = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
snake_case_ = torch.zeros(__UpperCamelCase , __UpperCamelCase )
else:
snake_case_ = None
snake_case_ = torch.nn.Parameter(__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
super().__init__()
self.register_modules(
vqvae=__UpperCamelCase , transformer=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , scheduler=__UpperCamelCase , learned_classifier_free_sampling_embeddings=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else 1
# get prompt text embeddings
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
snake_case_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
snake_case_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
snake_case_ = text_input_ids[:, : self.tokenizer.model_max_length]
snake_case_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
snake_case_ = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate text embeddings for each generation per prompt
snake_case_ = prompt_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
snake_case_ = self.learned_classifier_free_sampling_embeddings.embeddings
snake_case_ = negative_prompt_embeds.unsqueeze(0 ).repeat(__UpperCamelCase , 1 , 1 )
else:
snake_case_ = [''] * batch_size
snake_case_ = text_input_ids.shape[-1]
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors='pt' , )
snake_case_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
snake_case_ = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case_ = negative_prompt_embeds.shape[1]
snake_case_ = negative_prompt_embeds.repeat(1 , __UpperCamelCase , 1 )
snake_case_ = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __UpperCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case_ = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , __UpperCamelCase , __UpperCamelCase = 1_00 , __UpperCamelCase = 5.0 , __UpperCamelCase = 1.0 , __UpperCamelCase = 1 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = "pil" , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = 1 , ):
"""simple docstring"""
if isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = 1
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = len(__UpperCamelCase )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase )}""" )
snake_case_ = batch_size * num_images_per_prompt
snake_case_ = guidance_scale > 1.0
snake_case_ = self._encode_prompt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__UpperCamelCase , __UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__UpperCamelCase )}.""" )
# get the initial completely masked latents unless the user supplied it
snake_case_ = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
snake_case_ = self.transformer.num_vector_embeds - 1
snake_case_ = torch.full(__UpperCamelCase , __UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
f""" {self.transformer.num_vector_embeds - 1} (inclusive).""" )
snake_case_ = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__UpperCamelCase , device=self.device )
snake_case_ = self.scheduler.timesteps.to(self.device )
snake_case_ = latents
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
snake_case_ = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
snake_case_ = self.transformer(__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , timestep=__UpperCamelCase ).sample
if do_classifier_free_guidance:
snake_case_ , snake_case_ = model_output.chunk(2 )
snake_case_ = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__UpperCamelCase , dim=1 , keepdim=__UpperCamelCase )
snake_case_ = self.truncate(__UpperCamelCase , __UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
snake_case_ = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(__UpperCamelCase , timestep=__UpperCamelCase , sample=__UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = self.vqvae.config.vq_embed_dim
snake_case_ = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
snake_case_ = self.vqvae.quantize.get_codebook_entry(__UpperCamelCase , shape=__UpperCamelCase )
snake_case_ = self.vqvae.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase ).sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ , snake_case_ = torch.sort(__UpperCamelCase , 1 , descending=__UpperCamelCase )
snake_case_ = torch.exp(__UpperCamelCase )
snake_case_ = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
snake_case_ = torch.full_like(keep_mask[:, 0:1, :] , __UpperCamelCase )
snake_case_ = torch.cat((all_true, keep_mask) , dim=1 )
snake_case_ = keep_mask[:, :-1, :]
snake_case_ = keep_mask.gather(1 , indices.argsort(1 ) )
snake_case_ = log_p_x_0.clone()
snake_case_ = -torch.inf # -inf = log(0)
return rv
| 46
| 0
|
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A = [
"word_embeddings_layernorm.weight",
"word_embeddings_layernorm.bias",
"input_layernorm.weight",
"input_layernorm.bias",
"post_attention_layernorm.weight",
"post_attention_layernorm.bias",
"self_attention.dense.bias",
"mlp.dense_4h_to_h.bias",
"ln_f.weight",
"ln_f.bias",
]
A = [
"mlp.dense_4h_to_h.weight",
"self_attention.dense.weight",
]
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
snake_case_ = int(re.match(R'.*layer_(\d*).*' , a_ )[1] )
layer_number -= 3
return f"""h.{layer_number}.""" + key
def a(lowercase__ ):
'''simple docstring'''
if dtype == torch.bool:
return 1 / 8
snake_case_ = re.search(R'[^\d](\d+)$' , str(a_ ) )
if bit_search is None:
raise ValueError(f"""`dtype` is not a valid dtype: {dtype}.""" )
snake_case_ = int(bit_search.groups()[0] )
return bit_size // 8
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if bloom_config_file == "":
snake_case_ = BloomConfig()
else:
snake_case_ = BloomConfig.from_json_file(a_ )
if shard_model:
snake_case_ = os.listdir(a_ )
snake_case_ = sorted(filter(lambda lowercase__ : s.startswith('layer' ) and "model_00" in s , a_ ) )
snake_case_ = {'''weight_map''': {}, '''metadata''': {}}
snake_case_ = 0
snake_case_ = None
snake_case_ = BloomConfig()
for j, file in enumerate(a_ ):
print('Processing file: {}'.format(a_ ) )
snake_case_ = None
for i in range(a_ ):
# load all TP files
snake_case_ = file.replace('model_00' , f"""model_0{i}""" )
snake_case_ = torch.load(os.path.join(a_ , a_ ) , map_location='cpu' )
# Rename keys in the transformers names
snake_case_ = list(temp.keys() )
for key in keys:
snake_case_ = temp.pop(a_ )
if tensors is None:
snake_case_ = temp
else:
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ = torch.cat([tensors[key], temp[key]] , dim=a_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ = tensors[key] / pretraining_tp
torch.save(
a_ , os.path.join(
a_ , 'pytorch_model_{}-of-{}.bin'.format(str(j + 1 ).zfill(5 ) , str(len(a_ ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
snake_case_ = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
snake_case_ = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(a_ ) ).zfill(5 ) )
snake_case_ = BloomConfig()
snake_case_ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
snake_case_ = total_size
with open(a_ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(a_ , WEIGHTS_NAME + '.index.json' ) , 'w' , encoding='utf-8' ) as f:
snake_case_ = json.dumps(a_ , indent=2 , sort_keys=a_ ) + '''\n'''
f.write(a_ )
else:
snake_case_ = BloomModel(a_ )
snake_case_ = os.listdir(a_ )
snake_case_ = sorted(filter(lambda lowercase__ : s.startswith('layer' ) and "model_00" in s , a_ ) )
snake_case_ = None
for i, file in enumerate(a_ ):
snake_case_ = None
for i in range(a_ ):
# load all TP files
snake_case_ = file.replace('model_00' , f"""model_0{i}""" )
snake_case_ = torch.load(os.path.join(a_ , a_ ) , map_location='cpu' )
# Rename keys in the transformers names
snake_case_ = list(temp.keys() )
for key in keys:
snake_case_ = temp.pop(a_ )
if tensors is None:
snake_case_ = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ = torch.cat([tensors[key], temp[key]] , dim=a_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ = tensors[key] / pretraining_tp
snake_case_ = model.load_state_dict(a_ , strict=a_ )
assert not other_keys.unexpected_keys, f"""The keys {other_keys.unexpected_keys} are unexpected"""
if missing_keys is None:
snake_case_ = set(other_keys.missing_keys )
else:
snake_case_ = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"""The keys {missing_keys} are missing"""
# Save pytorch-model
os.makedirs(a_ , exist_ok=a_ )
snake_case_ = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
snake_case_ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f"""Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}""" )
if config.torch_dtype is not None:
snake_case_ = model.to(config.torch_dtype )
torch.save(model.state_dict() , a_ )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(a_ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bloom_checkpoint_path',
default=None,
type=str,
required=True,
help='Path to the Megatron-LM checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--bloom_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--shard_model',
action='store_true',
help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
)
parser.add_argument(
'--pretraining_tp',
default=4,
type=int,
help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
)
A = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 718
|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'neck_hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'num_attention_heads' ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=6_40 , __UpperCamelCase=4 , __UpperCamelCase="silu" , __UpperCamelCase=3 , __UpperCamelCase=32 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = last_hidden_size
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = conv_kernel_size
snake_case_ = output_stride
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = classifier_dropout_prob
snake_case_ = use_labels
snake_case_ = is_training
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MobileViTModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForSemanticSegmentation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__A = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTModelTester(self )
snake_case_ = MobileViTConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = 5
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case_ = 2
for i in range(len(__UpperCamelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MobileViTModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits
# verify the logits
snake_case_ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=__UpperCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits.detach().cpu()
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase , target_sizes=[(50, 60)] )
snake_case_ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase )
snake_case_ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
| 46
| 0
|
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
A = get_logger()
A = None
class SCREAMING_SNAKE_CASE ( TensorFormatter[Mapping, """jax.Array""", Mapping] ):
"""simple docstring"""
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(features=__UpperCamelCase )
import jax
from jaxlib.xla_client import Device
if isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError(
f"""Expected {device} to be a `str` not {type(__UpperCamelCase )}, as `jaxlib.xla_extension.Device` """
'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
'the device with `str()` to get its string identifier that will be internally mapped '
'to the actual `jaxlib.xla_extension.Device`.' )
snake_case_ = device if isinstance(__UpperCamelCase , __UpperCamelCase ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
snake_case_ = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"""Device with string identifier {self.device} not listed among the available """
f"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
f"""device: {str(jax.devices()[0] )}.""" )
snake_case_ = str(jax.devices()[0] )
snake_case_ = jnp_array_kwargs
@staticmethod
def __lowerCAmelCase ( ):
"""simple docstring"""
import jax
return {str(__UpperCamelCase ): device for device in jax.devices()}
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(__UpperCamelCase , __UpperCamelCase ) and column:
if all(
isinstance(__UpperCamelCase , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__UpperCamelCase , axis=0 )
return column
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(__UpperCamelCase , (str, bytes, type(__UpperCamelCase )) ):
return value
elif isinstance(__UpperCamelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
snake_case_ = {}
if isinstance(__UpperCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
snake_case_ = {'dtype': jnp.intaa}
else:
snake_case_ = {'dtype': jnp.intaa}
elif isinstance(__UpperCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
snake_case_ = {'dtype': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__UpperCamelCase , PIL.Image.Image ):
snake_case_ = np.asarray(__UpperCamelCase )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
snake_case_ = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__UpperCamelCase , **{**default_dtype, **self.jnp_array_kwargs} )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__UpperCamelCase , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__UpperCamelCase , '__array__' ) and not isinstance(__UpperCamelCase , jax.Array ):
snake_case_ = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__UpperCamelCase , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__UpperCamelCase ) for substruct in data_struct] )
elif isinstance(__UpperCamelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__UpperCamelCase ) for substruct in data_struct] )
return self._tensorize(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return map_nested(self._recursive_tensorize , __UpperCamelCase , map_list=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.numpy_arrow_extractor().extract_row(__UpperCamelCase )
snake_case_ = self.python_features_decoder.decode_row(__UpperCamelCase )
return self.recursive_tensorize(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.numpy_arrow_extractor().extract_column(__UpperCamelCase )
snake_case_ = self.python_features_decoder.decode_column(__UpperCamelCase , pa_table.column_names[0] )
snake_case_ = self.recursive_tensorize(__UpperCamelCase )
snake_case_ = self._consolidate(__UpperCamelCase )
return column
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.numpy_arrow_extractor().extract_batch(__UpperCamelCase )
snake_case_ = self.python_features_decoder.decode_batch(__UpperCamelCase )
snake_case_ = self.recursive_tensorize(__UpperCamelCase )
for column_name in batch:
snake_case_ = self._consolidate(batch[column_name] )
return batch
| 719
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 46
| 0
|
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = 13
snake_case_ = 7
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 99
snake_case_ = 3_84
snake_case_ = 2
snake_case_ = 4
snake_case_ = 37
snake_case_ = 'gelu'
snake_case_ = 0.1
snake_case_ = 0.1
snake_case_ = 5_12
snake_case_ = 16
snake_case_ = 2
snake_case_ = 0.02
snake_case_ = 3
snake_case_ = 4
snake_case_ = 1_28
snake_case_ = 2
snake_case_ = 9
snake_case_ = 1
snake_case_ = None
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=snake_case_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFConvBertModel(config=snake_case_ )
snake_case_ = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
snake_case_ = [input_ids, input_mask]
snake_case_ = model(snake_case_ )
snake_case_ = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFConvBertForMaskedLM(config=snake_case_ )
snake_case_ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case_ = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = TFConvBertForSequenceClassification(config=snake_case_ )
snake_case_ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case_ = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_choices
snake_case_ = TFConvBertForMultipleChoice(config=snake_case_ )
snake_case_ = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
snake_case_ = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
snake_case_ = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
snake_case_ = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
snake_case_ = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = TFConvBertForTokenClassification(config=snake_case_ )
snake_case_ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case_ = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFConvBertForQuestionAnswering(config=snake_case_ )
snake_case_ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case_ = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( _a , _a , unittest.TestCase ):
"""simple docstring"""
__A : Any = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__A : str = (
{
"""feature-extraction""": TFConvBertModel,
"""fill-mask""": TFConvBertForMaskedLM,
"""question-answering""": TFConvBertForQuestionAnswering,
"""text-classification""": TFConvBertForSequenceClassification,
"""token-classification""": TFConvBertForTokenClassification,
"""zero-shot""": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__A : Any = False
__A : Dict = False
__A : str = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFConvBertModelTester(self )
snake_case_ = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case_ )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
snake_case_ = True
if hasattr(snake_case_ , 'use_cache' ):
snake_case_ = True
snake_case_ = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
snake_case_ = getattr(self.model_tester , 'key_length' , snake_case_ )
for model_class in self.all_model_classes:
snake_case_ = self._prepare_for_class(snake_case_ , snake_case_ )
snake_case_ = model_class(snake_case_ )
snake_case_ = len(model(snake_case_ ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ , saved_model=snake_case_ )
snake_case_ = os.path.join(snake_case_ , 'saved_model' , '1' )
snake_case_ = tf.keras.models.load_model(snake_case_ )
snake_case_ = model(snake_case_ )
if self.is_encoder_decoder:
snake_case_ = outputs['encoder_hidden_states']
snake_case_ = outputs['encoder_attentions']
else:
snake_case_ = outputs['hidden_states']
snake_case_ = outputs['attentions']
self.assertEqual(len(snake_case_ ) , snake_case_ )
snake_case_ = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(snake_case_ ) , snake_case_ )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
self.assertIsNotNone(snake_case_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
snake_case_ = getattr(self.model_tester , 'decoder_seq_length' , self.model_tester.seq_length )
snake_case_ = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
snake_case_ = getattr(self.model_tester , 'key_length' , snake_case_ )
snake_case_ = getattr(self.model_tester , 'key_length' , snake_case_ )
def check_decoder_attentions_output(__UpperCamelCase ):
snake_case_ = len(snake_case_ )
self.assertEqual(out_len % 2 , 0 )
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(__UpperCamelCase ):
snake_case_ = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = model_class(snake_case_ )
snake_case_ = model(self._prepare_for_class(snake_case_ , snake_case_ ) )
snake_case_ = len(snake_case_ )
self.assertEqual(config.output_hidden_states , snake_case_ )
check_encoder_attentions_output(snake_case_ )
if self.is_encoder_decoder:
snake_case_ = model_class(snake_case_ )
snake_case_ = model(self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(config.output_hidden_states , snake_case_ )
check_decoder_attentions_output(snake_case_ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(snake_case_ )
snake_case_ = model(self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(config.output_hidden_states , snake_case_ )
check_encoder_attentions_output(snake_case_ )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(snake_case_ )
snake_case_ = model(self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(snake_case_ ) )
self.assertEqual(model.config.output_hidden_states , snake_case_ )
check_encoder_attentions_output(snake_case_ )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
snake_case_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
snake_case_ = model(snake_case_ )[0]
snake_case_ = [1, 6, 7_68]
self.assertEqual(output.shape , snake_case_ )
snake_case_ = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 720
|
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
snake_case_ = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
snake_case_ = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
snake_case_ = tf_top_k_top_p_filtering(__UpperCamelCase , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
snake_case_ = output[output != -float('inf' )]
snake_case_ = tf.cast(
tf.where(tf.not_equal(__UpperCamelCase , tf.constant(-float('inf' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-12 )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase , __snake_case ):
"""simple docstring"""
if is_tf_available():
__A = {
"""AutoModelForCausalLM""": TFAutoModelForCausalLM,
"""AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
"""AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
"""AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
"""LogitsProcessorList""": TFLogitsProcessorList,
"""MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
"""create_tensor_fn""": tf.convert_to_tensor,
"""floats_tensor""": floats_tensor,
"""return_tensors""": """tf""",
}
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 2
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='input_ids' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2, 0], [1_02, 1_03]]
snake_case_ = [[1, 0], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for batch_size in range(1 , len(__UpperCamelCase ) + 1 ):
snake_case_ = {
'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 1
snake_case_ = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super(__UpperCamelCase , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='input_ids' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='attention_mask' ),
) , jit_compile=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model.generate(
input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , max_new_tokens=__UpperCamelCase , return_dict_in_generate=__UpperCamelCase , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2], [1_02, 1_03]]
snake_case_ = [[1], [1, 1]]
snake_case_ = DummyModel(model=__UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__UpperCamelCase , __UpperCamelCase , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(__UpperCamelCase ).signatures['serving_default']
for input_row in range(len(__UpperCamelCase ) ):
snake_case_ = {
'input_ids': tf.constant([dummy_input_ids[input_row]] ),
'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
}
snake_case_ = serving_func(**__UpperCamelCase )['sequences']
snake_case_ = test_model.generate(**__UpperCamelCase , max_new_tokens=__UpperCamelCase )
tf.debugging.assert_equal(__UpperCamelCase , __UpperCamelCase )
@slow
@require_tensorflow_text
def __lowerCAmelCase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='google/flan-t5-small' , filename='spiece.model' , local_dir=__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
super().__init__()
snake_case_ = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(__UpperCamelCase , 'spiece.model' ) , 'rb' ).read() )
snake_case_ = TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )
def __lowerCAmelCase ( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.tokenizer.tokenize(__UpperCamelCase )
snake_case_ , snake_case_ = text.pad_model_inputs(
__UpperCamelCase , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
snake_case_ = self.model.generate(input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase )
return self.tokenizer.detokenize(__UpperCamelCase )
snake_case_ = CompleteSentenceTransformer()
snake_case_ = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='inputs' )
snake_case_ = complete_model(__UpperCamelCase )
snake_case_ = tf.keras.Model(__UpperCamelCase , __UpperCamelCase )
keras_model.save(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = {
'do_sample': True,
'num_beams': 1,
'top_p': 0.7,
'top_k': 10,
'temperature': 0.7,
}
snake_case_ = 14
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 'Hello, my dog is cute and'
snake_case_ = tokenizer(__UpperCamelCase , return_tensors='tf' )
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
snake_case_ = [6_38, 1_98]
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = 'Hugging Face is a technology company based in New York and Paris.'
snake_case_ = bart_tokenizer(__UpperCamelCase , return_tensors='tf' ).input_ids
snake_case_ = TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=None , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(__UpperCamelCase , foo='bar' ).numpy()
self.assertTrue(np.array_equal(__UpperCamelCase , __UpperCamelCase ) )
class SCREAMING_SNAKE_CASE ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def __lowerCAmelCase ( self , __UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return super().call(__UpperCamelCase , **__UpperCamelCase )
snake_case_ = FakeEncoder(bart_model.config , bart_model.model.shared )
snake_case_ = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
snake_case_ = bart_model.generate(__UpperCamelCase ).numpy()
with self.assertRaises(__UpperCamelCase ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(__UpperCamelCase , foo='bar' )
| 46
| 0
|
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A = 50_0000
A = os.path.split(__file__)
A = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def a(lowercase__ , **lowercase__ ):
'''simple docstring'''
snake_case_ = dataset.map(**__UpperCamelCase )
@get_duration
def a(lowercase__ , **lowercase__ ):
'''simple docstring'''
snake_case_ = dataset.filter(**__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ = datasets.Features({'text': datasets.Value('string' ), 'numbers': datasets.Value('float32' )} )
snake_case_ = generate_example_dataset(
os.path.join(__UpperCamelCase , 'dataset.arrow' ) , __UpperCamelCase , num_examples=__UpperCamelCase )
snake_case_ = transformers.AutoTokenizer.from_pretrained('bert-base-cased' , use_fast=__UpperCamelCase )
def tokenize(lowercase__ ):
return tokenizer(examples['text'] )
snake_case_ = map(__UpperCamelCase )
snake_case_ = map(__UpperCamelCase , batched=__UpperCamelCase )
snake_case_ = map(__UpperCamelCase , function=lambda lowercase__ : None , batched=__UpperCamelCase )
with dataset.formatted_as(type='numpy' ):
snake_case_ = map(__UpperCamelCase , function=lambda lowercase__ : None , batched=__UpperCamelCase )
with dataset.formatted_as(type='pandas' ):
snake_case_ = map(__UpperCamelCase , function=lambda lowercase__ : None , batched=__UpperCamelCase )
with dataset.formatted_as(type='torch' , columns='numbers' ):
snake_case_ = map(__UpperCamelCase , function=lambda lowercase__ : None , batched=__UpperCamelCase )
with dataset.formatted_as(type='tensorflow' , columns='numbers' ):
snake_case_ = map(__UpperCamelCase , function=lambda lowercase__ : None , batched=__UpperCamelCase )
snake_case_ = map(__UpperCamelCase , function=__UpperCamelCase , batched=__UpperCamelCase )
snake_case_ = filter(__UpperCamelCase )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(__UpperCamelCase , 'wb' ) as f:
f.write(json.dumps(__UpperCamelCase ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 721
|
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=2 , __UpperCamelCase=8 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=16 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=36 , __UpperCamelCase="gelu" , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.get_config()
snake_case_ = 3_00
return config
def __lowerCAmelCase ( self ):
"""simple docstring"""
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = self.prepare_config_and_inputs()
snake_case_ = True
snake_case_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase , token_type_ids=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = MraModel(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , )
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MraForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , start_positions=__UpperCamelCase , end_positions=__UpperCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MraForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_choices
snake_case_ = MraForMultipleChoice(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
__A = False
__A = False
__A = False
__A = False
__A = ()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MraModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@unittest.skip(reason='MRA does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraModel.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = torch.Size((1, 2_56, 7_68) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
snake_case_ = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 2_56, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
snake_case_ = torch.arange(40_96 ).unsqueeze(0 )
with torch.no_grad():
snake_case_ = model(__UpperCamelCase )[0]
snake_case_ = 5_02_65
snake_case_ = torch.Size((1, 40_96, vocab_size) )
self.assertEqual(output.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
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|
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = len(snake_case__ )
snake_case_ = [[0] * n for i in range(snake_case__ )]
for i in range(snake_case__ ):
snake_case_ = y_points[i]
for i in range(2 , snake_case__ ):
for j in range(snake_case__ , snake_case__ ):
snake_case_ = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700
|
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
snake_case_ = TapasConfig.from_json_file(lowercase__ )
# set absolute/relative position embeddings parameter
snake_case_ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WTQ":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = True
# hparam_utils.py hparams
snake_case_ = 0.66_4694
snake_case_ = 0.20_7951
snake_case_ = 0.12_1194
snake_case_ = True
snake_case_ = True
snake_case_ = False
snake_case_ = 0.035_2513
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = False
# hparam_utils.py hparams
snake_case_ = 36.4519
snake_case_ = 0.90_3421
snake_case_ = 222.088
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 0.76_3141
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "TABFACT":
snake_case_ = TapasForSequenceClassification(config=lowercase__ )
elif task == "MLM":
snake_case_ = TapasForMaskedLM(config=lowercase__ )
elif task == "INTERMEDIATE_PRETRAINING":
snake_case_ = TapasModel(config=lowercase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
snake_case_ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' , model_max_length=512 )
tokenizer.save_pretrained(lowercase__ )
print('Used relative position embeddings:' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
)
parser.add_argument(
'--reset_position_index_per_cell',
default=False,
action='store_true',
help='Whether to use relative position embeddings or not. Defaults to True.',
)
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--tapas_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained TAPAS model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 46
| 0
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
A = logging.get_logger(__name__)
A = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case_ = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if weight_type is not None:
snake_case_ = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).shape
else:
snake_case_ = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case_ = value
elif weight_type == "weight_g":
snake_case_ = value
elif weight_type == "weight_v":
snake_case_ = value
elif weight_type == "bias":
snake_case_ = value
else:
snake_case_ = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = fairseq_model.state_dict()
snake_case_ = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
snake_case_ = False
if "conv_layers" in name:
load_conv_layer(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == 'group' , )
snake_case_ = True
else:
for key, mapped_key in MAPPING.items():
snake_case_ = 'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or (key.split('w2v_model.' )[-1] == name.split('.' )[0] and not is_finetuned):
snake_case_ = True
if "*" in mapped_key:
snake_case_ = name.split(__SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
snake_case_ = mapped_key.replace('*' , __SCREAMING_SNAKE_CASE )
if "weight_g" in name:
snake_case_ = 'weight_g'
elif "weight_v" in name:
snake_case_ = 'weight_v'
elif "weight" in name:
snake_case_ = 'weight'
elif "bias" in name:
snake_case_ = 'bias'
else:
snake_case_ = None
set_recursively(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(__SCREAMING_SNAKE_CASE )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = full_name.split('conv_layers.' )[-1]
snake_case_ = name.split('.' )
snake_case_ = int(items[0] )
snake_case_ = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__SCREAMING_SNAKE_CASE )
@torch.no_grad()
def a(lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=True ):
'''simple docstring'''
if config_path is not None:
snake_case_ = HubertConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
else:
snake_case_ = HubertConfig()
if is_finetuned:
if dict_path:
snake_case_ = Dictionary.load(__SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case_ = target_dict.pad_index
snake_case_ = target_dict.bos_index
snake_case_ = target_dict.eos_index
snake_case_ = len(target_dict.symbols )
snake_case_ = os.path.join(__SCREAMING_SNAKE_CASE , 'vocab.json' )
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE ) )
return
os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices , __SCREAMING_SNAKE_CASE )
snake_case_ = WavaVecaCTCTokenizer(
__SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=__SCREAMING_SNAKE_CASE , )
snake_case_ = True if config.feat_extract_norm == 'layer' else False
snake_case_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , )
snake_case_ = WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
snake_case_ = HubertForCTC(__SCREAMING_SNAKE_CASE )
else:
snake_case_ = HubertModel(__SCREAMING_SNAKE_CASE )
if is_finetuned:
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
snake_case_ = model[0].eval()
recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
A = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 701
|
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=16 , __UpperCamelCase=[1, 2, 1] , __UpperCamelCase=[2, 2, 4] , __UpperCamelCase=2 , __UpperCamelCase=2.0 , __UpperCamelCase=True , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase="gelu" , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=8 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = embed_dim
snake_case_ = depths
snake_case_ = num_heads
snake_case_ = window_size
snake_case_ = mlp_ratio
snake_case_ = qkv_bias
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = drop_path_rate
snake_case_ = hidden_act
snake_case_ = use_absolute_embeddings
snake_case_ = patch_norm
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = is_training
snake_case_ = scope
snake_case_ = use_labels
snake_case_ = type_sequence_label_size
snake_case_ = encoder_stride
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
snake_case_ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaForMaskedImageModeling(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case_ = 1
snake_case_ = SwinvaForMaskedImageModeling(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.type_sequence_label_size
snake_case_ = SwinvaForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
__A = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , embed_dim=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
snake_case_ = len(self.model_tester.depths )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = config.window_size**2
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
snake_case_ = len(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
if hasattr(self.model_tester , 'num_hidden_states_types' ):
snake_case_ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case_ = 2
self.assertEqual(out_len + added_hidden_states , len(__UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# Swinv2 has a different seq_length
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
snake_case_ = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = reshaped_hidden_states[0].shape
snake_case_ = (
reshaped_hidden_states[0].view(__UpperCamelCase , __UpperCamelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = SwinvaModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = _config_zero_init(__UpperCamelCase )
for model_class in self.all_model_classes:
snake_case_ = model_class(config=__UpperCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46
| 0
|
import math
from numpy import inf
from scipy.integrate import quad
def a(lowercase__ ):
'''simple docstring'''
if num <= 0:
raise ValueError('math domain error' )
return quad(lowercase__ , 0 , lowercase__ , args=(lowercase__) )[0]
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
return math.pow(lowercase__ , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 702
|
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A = parser.parse_args()
A = 'cpu'
A = 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'
A = 'path-to-your-trained-model'
A = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
A = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A = pipe.to(device)
# to channels last
A = pipe.unet.to(memory_format=torch.channels_last)
A = pipe.vae.to(memory_format=torch.channels_last)
A = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
A = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
A = torch.randn(2, 4, 64, 64)
A = torch.rand(1) * 999
A = torch.randn(2, 77, 768)
A = (sample, timestep, encoder_hidden_status)
try:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
A = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
A = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
A = 666
A = torch.Generator(device).manual_seed(seed)
A = {'generator': generator}
if args.steps is not None:
A = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
A = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('generated.png')
| 46
| 0
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=UpperCamelCase_ ):
"""simple docstring"""
__A = ["""torch""", """torchsde"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(self , ['torch', 'torchsde'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'torchsde'] )
@classmethod
def __lowerCAmelCase ( cls , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'torchsde'] )
| 703
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """unispeech-sat"""
def __init__( self , __UpperCamelCase=32 , __UpperCamelCase=7_68 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=30_72 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase="group" , __UpperCamelCase="gelu" , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) , __UpperCamelCase=False , __UpperCamelCase=1_28 , __UpperCamelCase=16 , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.05 , __UpperCamelCase=10 , __UpperCamelCase=2 , __UpperCamelCase=0.0 , __UpperCamelCase=10 , __UpperCamelCase=0 , __UpperCamelCase=3_20 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , __UpperCamelCase=1_00 , __UpperCamelCase=2_56 , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase="mean" , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=2_56 , __UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) , __UpperCamelCase=(5, 3, 3, 1, 1) , __UpperCamelCase=(1, 2, 3, 1, 1) , __UpperCamelCase=5_12 , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=2 , __UpperCamelCase=5_04 , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase , pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase )
snake_case_ = hidden_size
snake_case_ = feat_extract_norm
snake_case_ = feat_extract_activation
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = conv_bias
snake_case_ = num_conv_pos_embeddings
snake_case_ = num_conv_pos_embedding_groups
snake_case_ = len(self.conv_dim )
snake_case_ = num_hidden_layers
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = feat_proj_dropout
snake_case_ = final_dropout
snake_case_ = layerdrop
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = vocab_size
snake_case_ = num_clusters
snake_case_ = do_stable_layer_norm
snake_case_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = apply_spec_augment
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
snake_case_ = num_codevectors_per_group
snake_case_ = num_codevector_groups
snake_case_ = contrastive_logits_temperature
snake_case_ = feat_quantizer_dropout
snake_case_ = num_negatives
snake_case_ = codevector_dim
snake_case_ = proj_codevector_dim
snake_case_ = diversity_loss_weight
# ctc loss
snake_case_ = ctc_loss_reduction
snake_case_ = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = list(__UpperCamelCase )
snake_case_ = xvector_output_dim
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 46
| 0
|
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=5 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = LlamaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = LlamaModel(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , )
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = LlamaForCausalLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = True
snake_case_ = LlamaForCausalLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
# first forward pass
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase , )
snake_case_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = torch.cat([input_mask, next_mask] , dim=-1 )
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , output_hidden_states=__UpperCamelCase , )["hidden_states"][0]
snake_case_ = model(
__UpperCamelCase , attention_mask=__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , encoder_attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase , output_hidden_states=__UpperCamelCase , )["hidden_states"][0]
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
snake_case_
) = config_and_inputs
snake_case_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
__A = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
__A = (LlamaForCausalLM,) if is_torch_available() else ()
__A = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LlamaModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = input_dict["input_ids"]
snake_case_ = input_ids.ne(1 ).to(__UpperCamelCase )
snake_case_ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
snake_case_ = LlamaForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = "single_label_classification"
snake_case_ = input_dict["input_ids"]
snake_case_ = input_ids.ne(1 ).to(__UpperCamelCase )
snake_case_ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
snake_case_ = LlamaForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = "multi_label_classification"
snake_case_ = input_dict["input_ids"]
snake_case_ = input_ids.ne(1 ).to(__UpperCamelCase )
snake_case_ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
snake_case_ = LlamaForSequenceClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , labels=__UpperCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = ids_tensor([1, 10] , config.vocab_size )
snake_case_ = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
snake_case_ = LlamaModel(__UpperCamelCase )
original_model.to(__UpperCamelCase )
original_model.eval()
snake_case_ = original_model(__UpperCamelCase ).last_hidden_state
snake_case_ = original_model(__UpperCamelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
snake_case_ = {"type": scaling_type, "factor": 10.0}
snake_case_ = LlamaModel(__UpperCamelCase )
scaled_model.to(__UpperCamelCase )
scaled_model.eval()
snake_case_ = scaled_model(__UpperCamelCase ).last_hidden_state
snake_case_ = scaled_model(__UpperCamelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-5 ) )
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
snake_case_ = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
snake_case_ = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
snake_case_ = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCamelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
snake_case_ = model(torch.tensor(__UpperCamelCase ) )
# Expected mean on dim = -1
snake_case_ = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
snake_case_ = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCamelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
snake_case_ = model(torch.tensor(__UpperCamelCase ) )
# Expected mean on dim = -1
snake_case_ = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
snake_case_ = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
snake_case_ = model(torch.tensor(__UpperCamelCase ) )
snake_case_ = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , __UpperCamelCase , atol=1E-2 , rtol=1E-2 )
# fmt: off
snake_case_ = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __UpperCamelCase , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = "Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
snake_case_ = "Simply put, the theory of relativity states that "
snake_case_ = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
snake_case_ = tokenizer.encode(__UpperCamelCase , return_tensors='pt' )
snake_case_ = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=__UpperCamelCase )
# greedy generation outputs
snake_case_ = model.generate(__UpperCamelCase , max_new_tokens=64 , top_p=__UpperCamelCase , temperature=1 , do_sample=__UpperCamelCase )
snake_case_ = tokenizer.decode(generated_ids[0] , skip_special_tokens=__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
| 704
|
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self.__class__.__name__}({self.name}, {self.val})"""
def __lt__( self , __UpperCamelCase ):
"""simple docstring"""
return self.val < other.val
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = {}
snake_case_ = {}
snake_case_ = self.build_heap(__UpperCamelCase )
def __getitem__( self , __UpperCamelCase ):
"""simple docstring"""
return self.get_value(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return (idx - 1) // 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 1
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return self.heap_dict[key]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) - 1
snake_case_ = self.get_parent_idx(__UpperCamelCase )
for idx, i in enumerate(__UpperCamelCase ):
snake_case_ = idx
snake_case_ = i.val
for i in range(__UpperCamelCase , -1 , -1 ):
self.sift_down(__UpperCamelCase , __UpperCamelCase )
return array
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
while True:
snake_case_ = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741
snake_case_ = self.get_right_child_idx(__UpperCamelCase )
snake_case_ = idx
if l < len(__UpperCamelCase ) and array[l] < array[idx]:
snake_case_ = l
if r < len(__UpperCamelCase ) and array[r] < array[smallest]:
snake_case_ = r
if smallest != idx:
snake_case_ , snake_case_ = array[smallest], array[idx]
(
(
snake_case_
) , (
snake_case_
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
snake_case_ = smallest
else:
break
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.get_parent_idx(__UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
snake_case_ , snake_case_ = self.heap[idx], self.heap[p]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
snake_case_ = p
snake_case_ = self.get_parent_idx(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return self.heap[0]
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.heap[-1], self.heap[0]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
snake_case_ = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
self.heap.append(__UpperCamelCase )
snake_case_ = len(self.heap ) - 1
snake_case_ = node.val
self.sift_up(len(self.heap ) - 1 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.heap ) == 0
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
snake_case_ = new_value
snake_case_ = new_value
self.sift_up(self.idx_of_element[node] )
A = Node('R', -1)
A = Node('B', 6)
A = Node('A', 3)
A = Node('X', 1)
A = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
A = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 0
|
import operator as op
A = 'scaler.pt'
A = 'pytorch_model'
A = 'random_states'
A = 'optimizer'
A = 'scheduler'
A = 'pytorch_model.bin'
A = 'pytorch_model.bin.index.json'
A = 'model.safetensors'
A = 'model.safetensors.index.json'
A = '1.10.2'
A = 'py38'
A = '4.17.0'
A = ['ml.p3.16xlarge', 'ml.p3dn.24xlarge', 'ml.p4dn.24xlarge']
A = ['FULL_SHARD', 'SHARD_GRAD_OP', 'NO_SHARD', 'HYBRID_SHARD', 'HYBRID_SHARD_ZERO2']
A = ['TRANSFORMER_BASED_WRAP', 'SIZE_BASED_WRAP', 'NO_WRAP']
A = ['BACKWARD_PRE', 'BACKWARD_POST', 'NO_PREFETCH']
A = ['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT']
A = '2.0.1'
A = ['pdsh', 'standard', 'openmpi', 'mvapich']
A = ['default', 'reduce-overhead', 'max-autotune']
A = {'>': op.gt, '>=': op.ge, '==': op.eq, '!=': op.ne, '<=': op.le, '<': op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
A = [
'nnodes',
'nproc_per_node',
'rdzv_backend',
'rdzv_endpoint',
'rdzv_id',
'rdzv_conf',
'standalone',
'max_restarts',
'monitor_interval',
'start_method',
'role',
'module',
'm',
'no_python',
'run_path',
'log_dir',
'r',
'redirects',
't',
'tee',
'node_rank',
'master_addr',
'master_port',
]
A = ['DEEPSPEED', 'MULTI_GPU', 'FSDP', 'MEGATRON_LM']
A = ['DEEPSPEED', 'MULTI_XPU', 'FSDP']
| 705
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['PerceiverFeatureExtractor']
A = ['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
'tokenization_roformer': ['RoFormerTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['RoFormerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoFormerForCausalLM',
'RoFormerForMaskedLM',
'RoFormerForMultipleChoice',
'RoFormerForQuestionAnswering',
'RoFormerForSequenceClassification',
'RoFormerForTokenClassification',
'RoFormerLayer',
'RoFormerModel',
'RoFormerPreTrainedModel',
'load_tf_weights_in_roformer',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRoFormerForCausalLM',
'TFRoFormerForMaskedLM',
'TFRoFormerForMultipleChoice',
'TFRoFormerForQuestionAnswering',
'TFRoFormerForSequenceClassification',
'TFRoFormerForTokenClassification',
'TFRoFormerLayer',
'TFRoFormerModel',
'TFRoFormerPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxRoFormerForMaskedLM',
'FlaxRoFormerForMultipleChoice',
'FlaxRoFormerForQuestionAnswering',
'FlaxRoFormerForSequenceClassification',
'FlaxRoFormerForTokenClassification',
'FlaxRoFormerModel',
'FlaxRoFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 706
|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
snake_case_ = ''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46
| 0
|
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """M-CLIP"""
def __init__( self , __UpperCamelCase=10_24 , __UpperCamelCase=7_68 , **__UpperCamelCase ):
"""simple docstring"""
snake_case_ = transformerDimSize
snake_case_ = imageDimSize
super().__init__(**__lowerCAmelCase )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = MCLIPConfig
def __init__( self , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
super().__init__(__lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase )
snake_case_ = XLMRobertaModel(__lowerCAmelCase )
snake_case_ = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.transformer(input_ids=__lowerCAmelCase , attention_mask=__lowerCAmelCase )[0]
snake_case_ = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(__lowerCAmelCase ), embs
| 707
|
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ = int(factor * num_class_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 )
os.makedirs(f"""{class_data_dir}/images""" , exist_ok=lowercase__ )
if len(list(Path(f"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
snake_case_ = client.query(text=lowercase__ )
if len(lowercase__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
snake_case_ = int(factor * num_images )
snake_case_ = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=lowercase__ , aesthetic_weight=0.1 , )
snake_case_ = 0
snake_case_ = 0
snake_case_ = tqdm(desc='downloading real regularization images' , total=lowercase__ )
with open(f"""{class_data_dir}/caption.txt""" , 'w' ) as fa, open(f"""{class_data_dir}/urls.txt""" , 'w' ) as fa, open(
f"""{class_data_dir}/images.txt""" , 'w' ) as fa:
while total < num_class_images:
snake_case_ = class_images[count]
count += 1
try:
snake_case_ = requests.get(images['url'] )
if img.status_code == 200:
snake_case_ = Image.open(BytesIO(img.content ) )
with open(f"""{class_data_dir}/images/{total}.jpg""" , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def a():
'''simple docstring'''
snake_case_ = argparse.ArgumentParser('' , add_help=lowercase__ )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--class_data_dir' , help='path to save images' , required=lowercase__ , type=lowercase__ )
parser.add_argument('--num_class_images' , help='number of images to download' , default=200 , type=lowercase__ )
return parser.parse_args()
if __name__ == "__main__":
A = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 46
| 0
|
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=5 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase_ , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = LlamaModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ )
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = LlamaModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , encoder_hidden_states=lowercase_ , encoder_attention_mask=lowercase_ , )
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , encoder_hidden_states=lowercase_ , )
snake_case_ = model(lowercase_ , attention_mask=lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = LlamaForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
snake_case_ = True
snake_case_ = True
snake_case_ = LlamaForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
# first forward pass
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , encoder_hidden_states=lowercase_ , encoder_attention_mask=lowercase_ , use_cache=lowercase_ , )
snake_case_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = torch.cat([input_mask, next_mask] , dim=-1 )
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , encoder_hidden_states=lowercase_ , encoder_attention_mask=lowercase_ , output_hidden_states=lowercase_ , )["""hidden_states"""][0]
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , encoder_hidden_states=lowercase_ , encoder_attention_mask=lowercase_ , past_key_values=lowercase_ , output_hidden_states=lowercase_ , )["""hidden_states"""][0]
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1E-3 ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
(
snake_case_
) = config_and_inputs
snake_case_ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
__A = (LlamaForCausalLM,) if is_torch_available() else ()
__A = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LlamaModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ = type
self.model_tester.create_and_check_model(*lowercase_ )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = input_dict["""input_ids"""]
snake_case_ = input_ids.ne(1 ).to(lowercase_ )
snake_case_ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
snake_case_ = LlamaForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = """single_label_classification"""
snake_case_ = input_dict["""input_ids"""]
snake_case_ = input_ids.ne(1 ).to(lowercase_ )
snake_case_ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
snake_case_ = LlamaForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = """multi_label_classification"""
snake_case_ = input_dict["""input_ids"""]
snake_case_ = input_ids.ne(1 ).to(lowercase_ )
snake_case_ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
snake_case_ = LlamaForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = ids_tensor([1, 10] , config.vocab_size )
snake_case_ = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
snake_case_ = LlamaModel(lowercase_ )
original_model.to(lowercase_ )
original_model.eval()
snake_case_ = original_model(lowercase_ ).last_hidden_state
snake_case_ = original_model(lowercase_ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
snake_case_ = {"""type""": scaling_type, """factor""": 10.0}
snake_case_ = LlamaModel(lowercase_ )
scaled_model.to(lowercase_ )
scaled_model.eval()
snake_case_ = scaled_model(lowercase_ ).last_hidden_state
snake_case_ = scaled_model(lowercase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(lowercase_ , lowercase_ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowercase_ , lowercase_ , atol=1E-5 ) )
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
snake_case_ = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
snake_case_ = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , lowercase_ , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
snake_case_ = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , lowercase_ , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
snake_case_ = model(torch.tensor(lowercase_ ) )
# Expected mean on dim = -1
snake_case_ = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , lowercase_ , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
snake_case_ = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , lowercase_ , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
snake_case_ = model(torch.tensor(lowercase_ ) )
# Expected mean on dim = -1
snake_case_ = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , lowercase_ , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
snake_case_ = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , lowercase_ , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
snake_case_ = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
snake_case_ = model(torch.tensor(lowercase_ ) )
snake_case_ = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , lowercase_ , atol=1E-2 , rtol=1E-2 )
# fmt: off
snake_case_ = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , lowercase_ , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
snake_case_ = """Simply put, the theory of relativity states that """
snake_case_ = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
snake_case_ = tokenizer.encode(lowercase_ , return_tensors='pt' )
snake_case_ = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=lowercase_ )
# greedy generation outputs
snake_case_ = model.generate(lowercase_ , max_new_tokens=64 , top_p=lowercase_ , temperature=1 , do_sample=lowercase_ )
snake_case_ = tokenizer.decode(generated_ids[0] , skip_special_tokens=lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
| 708
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
A = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 46
| 0
|
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
A = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = SavedModel()
snake_case_ = []
with open(os.path.join(_lowercase , 'utils' , 'tf_ops' , 'onnx.json' ) ) as f:
snake_case_ = json.load(_lowercase )["opsets"]
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(_lowercase )] )
with open(_lowercase , 'rb' ) as f:
saved_model.ParseFromString(f.read() )
snake_case_ = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
snake_case_ = sorted(_lowercase )
snake_case_ = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(_lowercase )
if strict and len(_lowercase ) > 0:
raise Exception(f"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops )
elif len(_lowercase ) > 0:
print(f"""Found the following incompatible ops for the opset {opset}:""" )
print(*_lowercase , sep='\n' )
else:
print(f"""The saved model {saved_model_path} can properly be converted with ONNX.""" )
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
parser.add_argument(
'--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
)
parser.add_argument(
'--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
)
parser.add_argument(
'--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
)
A = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 709
|
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__A = field(
default=__snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__A = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(default=__snake_case , metadata={"""help""": """The input training data file (a text file)."""} )
__A = field(
default=__snake_case , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__A = field(
default=__snake_case , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__A = field(
default=__snake_case , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__A = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
if self.train_file is not None:
snake_case_ = self.train_file.split('.' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
snake_case_ = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = 42
__A = True
__A = None
__A = None
def __call__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 'label' if 'label' in features[0].keys() else 'labels'
snake_case_ = [feature.pop(__UpperCamelCase ) for feature in features]
snake_case_ = len(__UpperCamelCase )
snake_case_ = len(features[0]['input_ids'] )
snake_case_ = [
[{k: v[i] for k, v in feature.items()} for i in range(__UpperCamelCase )] for feature in features
]
snake_case_ = list(chain(*__UpperCamelCase ) )
snake_case_ = self.tokenizer.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
# Un-flatten
snake_case_ = {k: v.view(__UpperCamelCase , __UpperCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
snake_case_ = torch.tensor(__UpperCamelCase , dtype=torch.intaa )
return batch
def a():
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_ , snake_case_ , snake_case_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ , snake_case_ , snake_case_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_swag' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
datasets.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
snake_case_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
snake_case_ = {}
if data_args.train_file is not None:
snake_case_ = data_args.train_file
if data_args.validation_file is not None:
snake_case_ = data_args.validation_file
snake_case_ = data_args.train_file.split('.' )[-1]
snake_case_ = load_dataset(
lowercase__ , data_files=lowercase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
snake_case_ = load_dataset(
'swag' , 'regular' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
snake_case_ = [f"""ending{i}""" for i in range(4 )]
snake_case_ = 'sent1'
snake_case_ = 'sent2'
if data_args.max_seq_length is None:
snake_case_ = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'
' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'
' override this default with `--block_size xxx`.' )
snake_case_ = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
snake_case_ = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowercase__ ):
snake_case_ = [[context] * 4 for context in examples[context_name]]
snake_case_ = examples[question_header_name]
snake_case_ = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase__ )
]
# Flatten out
snake_case_ = list(chain(*lowercase__ ) )
snake_case_ = list(chain(*lowercase__ ) )
# Tokenize
snake_case_ = tokenizer(
lowercase__ , lowercase__ , truncation=lowercase__ , max_length=lowercase__ , padding='max_length' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowercase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
snake_case_ = raw_datasets['train']
if data_args.max_train_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_train_samples )
snake_case_ = train_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
snake_case_ = train_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
snake_case_ = raw_datasets['validation']
if data_args.max_eval_samples is not None:
snake_case_ = min(len(lowercase__ ) , data_args.max_eval_samples )
snake_case_ = eval_dataset.select(range(lowercase__ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
snake_case_ = eval_dataset.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
snake_case_ = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowercase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowercase__ ):
snake_case_ , snake_case_ = eval_predictions
snake_case_ = np.argmax(lowercase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
snake_case_ = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , compute_metrics=lowercase__ , )
# Training
if training_args.do_train:
snake_case_ = None
if training_args.resume_from_checkpoint is not None:
snake_case_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ = last_checkpoint
snake_case_ = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
snake_case_ = train_result.metrics
snake_case_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase__ )
)
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('train' , lowercase__ )
trainer.save_metrics('train' , lowercase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
snake_case_ = trainer.evaluate()
snake_case_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase__ )
snake_case_ = min(lowercase__ , len(lowercase__ ) )
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
snake_case_ = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'multiple-choice',
'dataset_tags': 'swag',
'dataset_args': 'regular',
'dataset': 'SWAG',
'language': 'en',
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 46
| 0
|
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if gpta_config_file == "":
snake_case_ = GPTaConfig()
else:
snake_case_ = GPTaConfig.from_json_file(UpperCAmelCase__ )
snake_case_ = GPTaModel(UpperCAmelCase__ )
# Load weights from numpy
load_tf_weights_in_gpta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
snake_case_ = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
snake_case_ = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(f"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , UpperCAmelCase__ )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(UpperCAmelCase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--gpt2_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained OpenAI model. \n'
'This specifies the model architecture.'
),
)
A = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 710
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ASTForAudioClassification',
'ASTModel',
'ASTPreTrainedModel',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['ASTFeatureExtractor']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 46
| 0
|
from __future__ import annotations
from statistics import mean
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = [0] * no_of_processes
snake_case_ = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_snake_case ):
snake_case_ = burst_time[i]
snake_case_ = []
snake_case_ = 0
snake_case_ = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
snake_case_ = []
snake_case_ = -1
for i in range(_snake_case ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(_snake_case )
if len(_snake_case ) > 0:
snake_case_ = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
snake_case_ = i
total_time += burst_time[target_process]
completed += 1
snake_case_ = 0
snake_case_ = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = [0] * no_of_processes
for i in range(_snake_case ):
snake_case_ = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('[TEST CASE 01]')
A = 4
A = [2, 5, 3, 7]
A = [0, 0, 0, 0]
A = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
A = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
for i, process_id in enumerate(list(range(1, 5))):
print(
f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
)
print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")
| 711
|
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
else:
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
snake_case_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
A = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 46
| 0
|
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
A = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
A = 'zero2'
A = 'zero3'
A = [ZEROa, ZEROa]
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
snake_case_ = parameterized.to_safe_name('_'.join(str(_lowercase ) for x in param.args ) )
return f"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
A = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( UpperCamelCase_ ):
"""simple docstring"""
@parameterized.expand(UpperCamelCase__ , name_func=UpperCamelCase__ )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
self.run_and_check(
stage=UpperCamelCase__ , model=UpperCamelCase__ , distributed=UpperCamelCase__ , fpaa=UpperCamelCase__ , )
@require_torch_multi_gpu
@parameterized.expand(UpperCamelCase__ , name_func=UpperCamelCase__ )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
self.run_and_check(
stage=UpperCamelCase__ , model=UpperCamelCase__ , distributed=UpperCamelCase__ , fpaa=UpperCamelCase__ , )
@parameterized.expand(UpperCamelCase__ , name_func=UpperCamelCase__ )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
self.run_and_check(
stage=UpperCamelCase__ , model=UpperCamelCase__ , distributed=UpperCamelCase__ , fpaa=UpperCamelCase__ , )
@require_torch_multi_gpu
@parameterized.expand(UpperCamelCase__ , name_func=UpperCamelCase__ )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
self.run_and_check(
stage=UpperCamelCase__ , model=UpperCamelCase__ , distributed=UpperCamelCase__ , fpaa=UpperCamelCase__ , )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 10 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = True , ):
"""simple docstring"""
snake_case_ = models[model]
snake_case_ = self.run_trainer(
stage=UpperCamelCase__ , model_name=UpperCamelCase__ , eval_steps=UpperCamelCase__ , num_train_epochs=1 , distributed=UpperCamelCase__ , fpaa=UpperCamelCase__ , )
self.do_checks(UpperCamelCase__ )
return output_dir
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 10 , __UpperCamelCase = 1 , __UpperCamelCase = True , __UpperCamelCase = True , ):
"""simple docstring"""
snake_case_ = self.get_auto_remove_tmp_dir('./xxx' , after=UpperCamelCase__ )
snake_case_ = f"""
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(UpperCamelCase__ )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
""".split()
if fpaa:
args.extend(['--fp16'] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
snake_case_ = f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
snake_case_ = [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
snake_case_ = self.get_launcher(UpperCamelCase__ )
snake_case_ = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(UpperCamelCase__ , env=self.get_env() )
return output_dir
def __lowerCAmelCase ( self , __UpperCamelCase=False ):
"""simple docstring"""
snake_case_ = min(2 , get_gpu_count() ) if distributed else 1
return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
| 712
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
__A = """bit"""
__A = ["""preactivation""", """bottleneck"""]
__A = ["""SAME""", """VALID"""]
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=64 , __UpperCamelCase=[2_56, 5_12, 10_24, 20_48] , __UpperCamelCase=[3, 4, 6, 3] , __UpperCamelCase="preactivation" , __UpperCamelCase="relu" , __UpperCamelCase=None , __UpperCamelCase=32 , __UpperCamelCase=0.0 , __UpperCamelCase=False , __UpperCamelCase=32 , __UpperCamelCase=1 , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
snake_case_ = global_padding.upper()
else:
raise ValueError(f"""Padding strategy {global_padding} not supported""" )
snake_case_ = num_channels
snake_case_ = embedding_size
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = layer_type
snake_case_ = hidden_act
snake_case_ = global_padding
snake_case_ = num_groups
snake_case_ = drop_path_rate
snake_case_ = embedding_dynamic_padding
snake_case_ = output_stride
snake_case_ = width_factor
snake_case_ = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(__UpperCamelCase ) + 1 )]
snake_case_ , snake_case_ = get_aligned_output_features_output_indices(
out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
| 46
| 0
|
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a():
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(__UpperCamelCase ):
requests.request('GET' , 'https://huggingface.co' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('GET' , 'https://huggingface.co' , timeout=1.0 )
@pytest.mark.integration
def a():
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET' , 'https://huggingface.co' )
def a():
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(__UpperCamelCase ):
http_head('https://huggingface.co' )
| 713
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = DDIMScheduler()
snake_case_ = self.dummy_vq_model
snake_case_ = LDMPipeline(unet=__UpperCamelCase , vqvae=__UpperCamelCase , scheduler=__UpperCamelCase )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=2 , output_type='numpy' , return_dict=__UpperCamelCase )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(__UpperCamelCase )
ldm.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = ldm(generator=__UpperCamelCase , num_inference_steps=5 , output_type='numpy' ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
snake_case_ = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 46
| 0
|
A = {}
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
snake_case_ = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
snake_case_ = _calculate(days - 1 , lowercase__ , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
snake_case_ = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
snake_case_ = _calculate(days - 1 , lowercase__ , 0 )
snake_case_ = state_late + state_absent + state_ontime
snake_case_ = prizestrings
return prizestrings
def a(lowercase__ = 30 ):
'''simple docstring'''
return _calculate(lowercase__ , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 714
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = LEDConfig
__A = {}
__A = """gelu"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=20 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=4 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
snake_case_ = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
snake_case_ = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
snake_case_ = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
snake_case_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
snake_case_ = tf.concat([input_ids, eos_tensor] , axis=1 )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
snake_case_ = prepare_led_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = tf.concat(
[tf.zeros_like(__UpperCamelCase )[:, :-1], tf.ones_like(__UpperCamelCase )[:, -1:]] , axis=-1 , )
snake_case_ = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFLEDModel(config=__UpperCamelCase ).get_decoder()
snake_case_ = inputs_dict['input_ids']
snake_case_ = input_ids[:1, :]
snake_case_ = inputs_dict['attention_mask'][:1, :]
snake_case_ = 1
# first forward pass
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
snake_case_ = tf.concat([input_ids, next_tokens] , axis=-1 )
snake_case_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
snake_case_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
snake_case_ = output_from_no_past[:, -3:, random_slice_idx]
snake_case_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-3 )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
__A = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
__A = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A = True
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = tf.zeros_like(inputs_dict['attention_mask'] )
snake_case_ = 2
snake_case_ = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['global_attention_mask'] , )
snake_case_ = True
snake_case_ = self.model_tester.seq_length
snake_case_ = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__UpperCamelCase ):
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__UpperCamelCase ):
snake_case_ = [t.numpy() for t in outputs.encoder_attentions]
snake_case_ = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = len(__UpperCamelCase )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_decoder_attentions_output(__UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def a(lowercase__ ):
'''simple docstring'''
return tf.constant(lowercase__ , dtype=tf.intaa )
A = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, 7_68)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 , rtol=1E-3 )
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|
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
snake_case_ = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
snake_case_ = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
snake_case_ = max(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_ ) , b_binary.zfill(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715
|
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replace(' ' , '' )
# Strings of different lengths are not anagrams
if len(lowercase__ ) != len(lowercase__ ):
return False
# Default values for count should be 0
snake_case_ = defaultdict(lowercase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowercase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
A = input('Enter the first string ').strip()
A = input('Enter the second string ').strip()
A = check_anagrams(input_a, input_b)
print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 46
| 0
|
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
A = 8
def a(lowercase__ , lowercase__=BITS ):
'''simple docstring'''
snake_case_ = x.device
snake_case_ = (x * 255).int().clamp(0 , 255 )
snake_case_ = 2 ** torch.arange(bits - 1 , -1 , -1 , device=_lowerCAmelCase )
snake_case_ = rearrange(_lowerCAmelCase , 'd -> d 1 1' )
snake_case_ = rearrange(_lowerCAmelCase , 'b c h w -> b c 1 h w' )
snake_case_ = ((x & mask) != 0).float()
snake_case_ = rearrange(_lowerCAmelCase , 'b c d h w -> b (c d) h w' )
snake_case_ = bits * 2 - 1
return bits
def a(lowercase__ , lowercase__=BITS ):
'''simple docstring'''
snake_case_ = x.device
snake_case_ = (x > 0).int()
snake_case_ = 2 ** torch.arange(bits - 1 , -1 , -1 , device=_lowerCAmelCase , dtype=torch.intaa )
snake_case_ = rearrange(_lowerCAmelCase , 'd -> d 1 1' )
snake_case_ = rearrange(_lowerCAmelCase , 'b (c d) h w -> b c d h w' , d=8 )
snake_case_ = reduce(x * mask , 'b c d h w -> b c h w' , 'sum' )
return (dec / 255).clamp(0.0 , 1.0 )
def a(self , lowercase__ , lowercase__ , lowercase__ , lowercase__ = 0.0 , lowercase__ = True , lowercase__=None , lowercase__ = True , ):
'''simple docstring'''
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
snake_case_ = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
snake_case_ = self.alphas_cumprod[timestep]
snake_case_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
snake_case_ = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
snake_case_ = self.bit_scale
if self.config.clip_sample:
snake_case_ = torch.clamp(_lowerCAmelCase , -scale , _lowerCAmelCase )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
snake_case_ = self._get_variance(_lowerCAmelCase , _lowerCAmelCase )
snake_case_ = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
snake_case_ = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case_ = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case_ = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
snake_case_ = model_output.device if torch.is_tensor(_lowerCAmelCase ) else "cpu"
snake_case_ = torch.randn(model_output.shape , dtype=model_output.dtype , generator=_lowerCAmelCase ).to(_lowerCAmelCase )
snake_case_ = self._get_variance(_lowerCAmelCase , _lowerCAmelCase ) ** 0.5 * eta * noise
snake_case_ = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase , pred_original_sample=_lowerCAmelCase )
def a(self , lowercase__ , lowercase__ , lowercase__ , lowercase__="epsilon" , lowercase__=None , lowercase__ = True , ):
'''simple docstring'''
snake_case_ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
snake_case_ = torch.split(_lowerCAmelCase , sample.shape[1] , dim=1 )
else:
snake_case_ = None
# 1. compute alphas, betas
snake_case_ = self.alphas_cumprod[t]
snake_case_ = self.alphas_cumprod[t - 1] if t > 0 else self.one
snake_case_ = 1 - alpha_prod_t
snake_case_ = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
snake_case_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
snake_case_ = model_output
else:
raise ValueError(f"""Unsupported prediction_type {prediction_type}.""" )
# 3. Clip "predicted x_0"
snake_case_ = self.bit_scale
if self.config.clip_sample:
snake_case_ = torch.clamp(_lowerCAmelCase , -scale , _lowerCAmelCase )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case_ = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
snake_case_ = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case_ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
snake_case_ = 0
if t > 0:
snake_case_ = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=_lowerCAmelCase ).to(model_output.device )
snake_case_ = (self._get_variance(_lowerCAmelCase , predicted_variance=_lowerCAmelCase ) ** 0.5) * noise
snake_case_ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase , pred_original_sample=_lowerCAmelCase )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 1.0 , ):
"""simple docstring"""
super().__init__()
snake_case_ = bit_scale
snake_case_ = (
ddim_bit_scheduler_step if isinstance(A_ , A_ ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=A_ , scheduler=A_ )
@torch.no_grad()
def __call__( self , __UpperCamelCase = 2_56 , __UpperCamelCase = 2_56 , __UpperCamelCase = 50 , __UpperCamelCase = None , __UpperCamelCase = 1 , __UpperCamelCase = "pil" , __UpperCamelCase = True , **__UpperCamelCase , ):
"""simple docstring"""
snake_case_ = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) , generator=A_ , )
snake_case_ = decimal_to_bits(A_ ) * self.bit_scale
snake_case_ = latents.to(self.device )
self.scheduler.set_timesteps(A_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
snake_case_ = self.unet(A_ , A_ ).sample
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(A_ , A_ , A_ ).prev_sample
snake_case_ = bits_to_decimal(A_ )
if output_type == "pil":
snake_case_ = self.numpy_to_pil(A_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A_ )
| 716
|
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.dummy_uncond_unet
snake_case_ = ScoreSdeVeScheduler()
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__UpperCamelCase , return_dict=__UpperCamelCase )[
0
]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = 'google/ncsnpp-church-256'
snake_case_ = UNetaDModel.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase )
snake_case_ = ScoreSdeVePipeline(unet=__UpperCamelCase , scheduler=__UpperCamelCase )
sde_ve.to(__UpperCamelCase )
sde_ve.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ = torch.manual_seed(0 )
snake_case_ = sde_ve(num_inference_steps=10 , output_type='numpy' , generator=__UpperCamelCase ).images
snake_case_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 46
| 0
|
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A = logging.get_logger(__name__)
A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A = {
'''vocab_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
},
'''merges_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
},
}
A = {
'''gpt2''': 1024,
'''gpt2-medium''': 1024,
'''gpt2-large''': 1024,
'''gpt2-xl''': 1024,
'''distilgpt2''': 1024,
}
class SCREAMING_SNAKE_CASE ( UpperCamelCase__ ):
"""simple docstring"""
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ["""input_ids""", """attention_mask"""]
__A = GPTaTokenizer
def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase="<|endoftext|>" , __UpperCamelCase=False , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(
__UpperCamelCase , __UpperCamelCase , tokenizer_file=__UpperCamelCase , unk_token=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , **__UpperCamelCase , )
snake_case_ = kwargs.pop('add_bos_token' , __UpperCamelCase )
snake_case_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , __UpperCamelCase ) != add_prefix_space:
snake_case_ = getattr(__UpperCamelCase , pre_tok_state.pop('type' ) )
snake_case_ = add_prefix_space
snake_case_ = pre_tok_class(**__UpperCamelCase )
snake_case_ = add_prefix_space
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
snake_case_ = kwargs.get('is_split_into_words' , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
snake_case_ = kwargs.get('is_split_into_words' , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase = None ):
"""simple docstring"""
snake_case_ = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase ) + [self.eos_token_id] )
if len(__UpperCamelCase ) > self.model_max_length:
snake_case_ = input_ids[-self.model_max_length :]
return input_ids
| 717
|
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
"""simple docstring"""
@register_to_config
def __init__( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None ):
"""simple docstring"""
super().__init__()
snake_case_ = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
snake_case_ = torch.zeros(__UpperCamelCase , __UpperCamelCase )
else:
snake_case_ = None
snake_case_ = torch.nn.Parameter(__UpperCamelCase )
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
__A = 42
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
"""simple docstring"""
super().__init__()
self.register_modules(
vqvae=__UpperCamelCase , transformer=__UpperCamelCase , text_encoder=__UpperCamelCase , tokenizer=__UpperCamelCase , scheduler=__UpperCamelCase , learned_classifier_free_sampling_embeddings=__UpperCamelCase , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else 1
# get prompt text embeddings
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
snake_case_ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
snake_case_ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
snake_case_ = text_input_ids[:, : self.tokenizer.model_max_length]
snake_case_ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
snake_case_ = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate text embeddings for each generation per prompt
snake_case_ = prompt_embeds.repeat_interleave(__UpperCamelCase , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
snake_case_ = self.learned_classifier_free_sampling_embeddings.embeddings
snake_case_ = negative_prompt_embeds.unsqueeze(0 ).repeat(__UpperCamelCase , 1 , 1 )
else:
snake_case_ = [''] * batch_size
snake_case_ = text_input_ids.shape[-1]
snake_case_ = self.tokenizer(
__UpperCamelCase , padding='max_length' , max_length=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors='pt' , )
snake_case_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
snake_case_ = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case_ = negative_prompt_embeds.shape[1]
snake_case_ = negative_prompt_embeds.repeat(1 , __UpperCamelCase , 1 )
snake_case_ = negative_prompt_embeds.view(batch_size * num_images_per_prompt , __UpperCamelCase , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case_ = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , __UpperCamelCase , __UpperCamelCase = 1_00 , __UpperCamelCase = 5.0 , __UpperCamelCase = 1.0 , __UpperCamelCase = 1 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = "pil" , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = 1 , ):
"""simple docstring"""
if isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = 1
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = len(__UpperCamelCase )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase )}""" )
snake_case_ = batch_size * num_images_per_prompt
snake_case_ = guidance_scale > 1.0
snake_case_ = self._encode_prompt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__UpperCamelCase , __UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__UpperCamelCase )}.""" )
# get the initial completely masked latents unless the user supplied it
snake_case_ = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
snake_case_ = self.transformer.num_vector_embeds - 1
snake_case_ = torch.full(__UpperCamelCase , __UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
f""" {self.transformer.num_vector_embeds - 1} (inclusive).""" )
snake_case_ = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__UpperCamelCase , device=self.device )
snake_case_ = self.scheduler.timesteps.to(self.device )
snake_case_ = latents
for i, t in enumerate(self.progress_bar(__UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
snake_case_ = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
snake_case_ = self.transformer(__UpperCamelCase , encoder_hidden_states=__UpperCamelCase , timestep=__UpperCamelCase ).sample
if do_classifier_free_guidance:
snake_case_ , snake_case_ = model_output.chunk(2 )
snake_case_ = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__UpperCamelCase , dim=1 , keepdim=__UpperCamelCase )
snake_case_ = self.truncate(__UpperCamelCase , __UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
snake_case_ = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(__UpperCamelCase , timestep=__UpperCamelCase , sample=__UpperCamelCase , generator=__UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = self.vqvae.config.vq_embed_dim
snake_case_ = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
snake_case_ = self.vqvae.quantize.get_codebook_entry(__UpperCamelCase , shape=__UpperCamelCase )
snake_case_ = self.vqvae.decode(__UpperCamelCase , force_not_quantize=__UpperCamelCase ).sample
snake_case_ = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ , snake_case_ = torch.sort(__UpperCamelCase , 1 , descending=__UpperCamelCase )
snake_case_ = torch.exp(__UpperCamelCase )
snake_case_ = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
snake_case_ = torch.full_like(keep_mask[:, 0:1, :] , __UpperCamelCase )
snake_case_ = torch.cat((all_true, keep_mask) , dim=1 )
snake_case_ = keep_mask[:, :-1, :]
snake_case_ = keep_mask.gather(1 , indices.argsort(1 ) )
snake_case_ = log_p_x_0.clone()
snake_case_ = -torch.inf # -inf = log(0)
return rv
| 46
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "BlipImageProcessor"
__A = "AutoTokenizer"
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = False
super().__init__(lowerCamelCase__ , lowerCamelCase__ )
snake_case_ = self.image_processor
def __call__( self , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = 0 , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
if images is None and text is None:
raise ValueError('You have to specify either images or text.' )
# Get only text
if images is None:
snake_case_ = self.tokenizer
snake_case_ = self.tokenizer(
text=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , stride=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , return_special_tokens_mask=lowerCamelCase__ , return_offsets_mapping=lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ , return_length=lowerCamelCase__ , verbose=lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ , )
return text_encoding
# add pixel_values
snake_case_ = self.image_processor(lowerCamelCase__ , return_tensors=lowerCamelCase__ )
if text is not None:
snake_case_ = self.tokenizer(
text=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , padding=lowerCamelCase__ , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , stride=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , return_special_tokens_mask=lowerCamelCase__ , return_offsets_mapping=lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ , return_length=lowerCamelCase__ , verbose=lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ , )
else:
snake_case_ = None
if text_encoding is not None:
encoding_image_processor.update(lowerCamelCase__ )
return encoding_image_processor
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCamelCase__ , **lowerCamelCase__ )
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCamelCase__ , **lowerCamelCase__ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.tokenizer.model_input_names
snake_case_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 718
|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'neck_hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase , 'num_attention_heads' ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=6_40 , __UpperCamelCase=4 , __UpperCamelCase="silu" , __UpperCamelCase=3 , __UpperCamelCase=32 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=None , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = last_hidden_size
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = conv_kernel_size
snake_case_ = output_stride
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = classifier_dropout_prob
snake_case_ = use_labels
snake_case_ = is_training
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = scope
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = MobileViTModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.num_labels
snake_case_ = MobileViTForSemanticSegmentation(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__A = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTModelTester(self )
snake_case_ = MobileViTConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = 5
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case_ = 2
for i in range(len(__UpperCamelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = MobileViTModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def a():
'''simple docstring'''
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits
# verify the logits
snake_case_ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=__UpperCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __UpperCamelCase , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = model.to(__UpperCamelCase )
snake_case_ = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
snake_case_ = prepare_img()
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
snake_case_ = outputs.logits.detach().cpu()
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase , target_sizes=[(50, 60)] )
snake_case_ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
snake_case_ = image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase )
snake_case_ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __UpperCamelCase )
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