code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
__lowerCamelCase = ["image_processor", "tokenizer"]
__lowerCamelCase = "BlipImageProcessor"
__lowerCamelCase = "AutoTokenizer"
def __init__( self , __A , __A , __A ) -> Optional[int]:
super().__init__(_a , _a )
# add QFormer tokenizer
SCREAMING_SNAKE_CASE_ : Dict =qformer_tokenizer
def __call__( self , __A = None , __A = None , __A = True , __A = False , __A = None , __A = None , __A = 0 , __A = None , __A = None , __A = False , __A = False , __A = False , __A = False , __A = False , __A = True , __A = None , **__A , ) -> List[Any]:
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
SCREAMING_SNAKE_CASE_ : Any =BatchFeature()
if text is not None:
SCREAMING_SNAKE_CASE_ : Tuple =self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
encoding.update(_a )
SCREAMING_SNAKE_CASE_ : List[Any] =self.qformer_tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
SCREAMING_SNAKE_CASE_ : List[Any] =qformer_text_encoding.pop('''input_ids''' )
SCREAMING_SNAKE_CASE_ : Dict =qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
SCREAMING_SNAKE_CASE_ : List[Any] =self.image_processor(_a , return_tensors=_a )
encoding.update(_a )
return encoding
def _snake_case ( self , *__A , **__A ) -> List[Any]:
return self.tokenizer.batch_decode(*_a , **_a )
def _snake_case ( self , *__A , **__A ) -> Optional[Any]:
return self.tokenizer.decode(*_a , **_a )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _snake_case ( self ) -> str:
SCREAMING_SNAKE_CASE_ : Tuple =self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ : Tuple =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _snake_case ( self , __A , **__A ) -> Tuple:
if os.path.isfile(_a ):
raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(_a , exist_ok=_a )
SCREAMING_SNAKE_CASE_ : List[Any] =os.path.join(_a , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(_a )
return super().save_pretrained(_a , **_a )
@classmethod
def _snake_case ( cls , __A , **__A ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : str =AutoTokenizer.from_pretrained(_a , subfolder='''qformer_tokenizer''' )
SCREAMING_SNAKE_CASE_ : Dict =cls._get_arguments_from_pretrained(_a , **_a )
args.append(_a )
return cls(*_a )
| 443 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__magic_name__ = 16
__magic_name__ = 32
def lowerCamelCase ( lowerCamelCase : Accelerator , lowerCamelCase : int = 16):
A_ : Any = AutoTokenizer.from_pretrained("""bert-base-cased""")
A_ : str = load_dataset("""glue""" , """mrpc""")
def tokenize_function(lowerCamelCase : Dict):
# max_length=None => use the model max length (it's actually the default)
A_ : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A_ : Tuple = datasets.map(
lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ : List[str] = tokenized_datasets.rename_column("""label""" , """labels""")
def collate_fn(lowerCamelCase : Tuple):
# On TPU it's best to pad everything to the same length or training will be very slow.
A_ : str = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A_ : List[Any] = 16
elif accelerator.mixed_precision != "no":
A_ : Any = 8
else:
A_ : Tuple = None
return tokenizer.pad(
lowerCamelCase , padding="""longest""" , max_length=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
A_ : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase , drop_last=lowerCamelCase)
A_ : str = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase , drop_last=(accelerator.mixed_precision == """fp8""") , )
return train_dataloader, eval_dataloader
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Dict):
# Initialize accelerator
A_ : Tuple = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision)
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ : List[Any] = config["""lr"""]
A_ : List[Any] = int(config["""num_epochs"""])
A_ : int = int(config["""seed"""])
A_ : Dict = int(config["""batch_size"""])
A_ : Union[str, Any] = evaluate.load("""glue""" , """mrpc""")
# If the batch size is too big we use gradient accumulation
A_ : int = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A_ : Any = batch_size // MAX_GPU_BATCH_SIZE
A_ : Union[str, Any] = MAX_GPU_BATCH_SIZE
set_seed(lowerCamelCase)
A_ , A_ : List[str] = get_dataloaders(lowerCamelCase , lowerCamelCase)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCamelCase)
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A_ : str = model.to(accelerator.device)
# Instantiate optimizer
A_ : str = AdamW(params=model.parameters() , lr=lowerCamelCase)
# Instantiate scheduler
A_ : Tuple = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(lowerCamelCase) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_ , A_ , A_ , A_ , A_ : Union[str, Any] = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Now we train the model
for epoch in range(lowerCamelCase):
model.train()
for step, batch in enumerate(lowerCamelCase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
A_ : Optional[int] = model(**lowerCamelCase)
A_ : List[Any] = outputs.loss
A_ : Tuple = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase)
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
with torch.no_grad():
A_ : Union[str, Any] = model(**lowerCamelCase)
A_ : Any = outputs.logits.argmax(dim=-1)
A_ , A_ : Tuple = accelerator.gather_for_metrics((predictions, batch["""labels"""]))
metric.add_batch(
predictions=lowerCamelCase , references=lowerCamelCase , )
A_ : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , lowerCamelCase)
def lowerCamelCase ( ):
A_ : Optional[int] = argparse.ArgumentParser(description="""Simple example of training script.""")
parser.add_argument(
"""--mixed_precision""" , type=lowerCamelCase , default=lowerCamelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""")
A_ : Dict = parser.parse_args()
A_ : Dict = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase , lowerCamelCase)
if __name__ == "__main__":
main()
| 665 | 0 |
def snake_case__ ( lowercase = 3 , lowercase = 7 , lowercase = 1000000 ):
lowerCAmelCase_: str = 0
lowerCAmelCase_: List[str] = 1
for current_denominator in range(1 , limit + 1 ):
lowerCAmelCase_: Union[str, Any] = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
lowerCAmelCase_: List[Any] = current_numerator
lowerCAmelCase_: Dict = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0)) | 613 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
raise ValueError("""The parameter days should be a list of integers""")
if len(lowerCamelCase) != 3 or not all(isinstance(lowerCamelCase , lowerCamelCase) for cost in costs):
raise ValueError("""The parameter costs should be a list of three integers""")
if len(lowerCamelCase) == 0:
return 0
if min(lowerCamelCase) <= 0:
raise ValueError("""All days elements should be greater than 0""")
if max(lowerCamelCase) >= 366:
raise ValueError("""All days elements should be less than 366""")
A_ : Tuple = set(lowerCamelCase)
@functools.cache
def dynamic_programming(lowerCamelCase : int) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1)
return min(
costs[0] + dynamic_programming(index + 1) , costs[1] + dynamic_programming(index + 7) , costs[2] + dynamic_programming(index + 30) , )
return dynamic_programming(1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCAmelCase : str = 16
UpperCAmelCase : int = 32
def _A ( SCREAMING_SNAKE_CASE : Accelerator , SCREAMING_SNAKE_CASE : int = 16 ):
"""simple docstring"""
a__ : Optional[int] =AutoTokenizer.from_pretrained("bert-base-cased" )
a__ : Any =load_dataset("glue" , "mrpc" )
def tokenize_function(SCREAMING_SNAKE_CASE : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
a__ : Dict =tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
a__ : Union[str, Any] =datasets.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
a__ : Any =tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
a__ : Optional[int] =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
a__ : Optional[int] =16
elif accelerator.mixed_precision != "no":
a__ : Tuple =8
else:
a__ : List[Any] =None
return tokenizer.pad(
SCREAMING_SNAKE_CASE , padding="longest" , max_length=SCREAMING_SNAKE_CASE , pad_to_multiple_of=SCREAMING_SNAKE_CASE , return_tensors="pt" , )
# Instantiate dataloaders.
a__ : Optional[int] =DataLoader(
tokenized_datasets["train"] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
a__ : Dict =DataLoader(
tokenized_datasets["validation"] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCAmelCase : Dict = mocked_dataloaders # noqa: F811
def _A ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , SCREAMING_SNAKE_CASE ) == "1":
a__ : Any =2
# New Code #
a__ : Any =int(args.gradient_accumulation_steps )
# Initialize accelerator
a__ : str =Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=SCREAMING_SNAKE_CASE )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
a__ : Union[str, Any] =config["""lr"""]
a__ : Union[str, Any] =int(config["num_epochs"] )
a__ : str =int(config["seed"] )
a__ : Any =int(config["batch_size"] )
a__ : Tuple =evaluate.load("glue" , "mrpc" )
set_seed(SCREAMING_SNAKE_CASE )
a__ : int =get_dataloaders(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
a__ : str =AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
a__ : List[Any] =model.to(accelerator.device )
# Instantiate optimizer
a__ : Tuple =AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE )
# Instantiate scheduler
a__ : str =get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(SCREAMING_SNAKE_CASE ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
a__ : str =accelerator.prepare(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE ):
model.train()
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(SCREAMING_SNAKE_CASE ):
a__ : Optional[Any] =model(**SCREAMING_SNAKE_CASE )
a__ : Any =output.loss
accelerator.backward(SCREAMING_SNAKE_CASE )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
a__ : str =model(**SCREAMING_SNAKE_CASE )
a__ : Union[str, Any] =outputs.logits.argmax(dim=-1 )
a__ : Optional[int] =accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE , references=SCREAMING_SNAKE_CASE , )
a__ : Tuple =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , SCREAMING_SNAKE_CASE )
def _A ( ):
"""simple docstring"""
a__ : Optional[Any] =argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=SCREAMING_SNAKE_CASE , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
a__ : Tuple =parser.parse_args()
a__ : List[Any] ={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 563 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : int , ):
A_ , A_ : int = coefficient_matrix.shape
A_ , A_ : Union[str, Any] = constant_matrix.shape
if rowsa != colsa:
A_ : Any = F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
raise ValueError(lowerCamelCase)
if colsa != 1:
A_ : Tuple = F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
raise ValueError(lowerCamelCase)
if rowsa != rowsa:
A_ : Dict = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
)
raise ValueError(lowerCamelCase)
if len(lowerCamelCase) != rowsa:
A_ : Union[str, Any] = (
"""Number of initial values must be equal to number of rows in coefficient """
F'matrix but received {len(lowerCamelCase)} and {rowsa}'
)
raise ValueError(lowerCamelCase)
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""")
A_ : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1)
A_ , A_ : int = table.shape
strictly_diagonally_dominant(lowerCamelCase)
# Iterates the whole matrix for given number of times
for _ in range(lowerCamelCase):
A_ : List[Any] = []
for row in range(lowerCamelCase):
A_ : int = 0
for col in range(lowerCamelCase):
if col == row:
A_ : List[str] = table[row][col]
elif col == cols - 1:
A_ : str = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
A_ : Union[str, Any] = (temp + val) / denom
new_val.append(lowerCamelCase)
A_ : Tuple = new_val
return [float(lowerCamelCase) for i in new_val]
def lowerCamelCase ( lowerCamelCase : NDArray[floataa]):
A_ , A_ : Dict = table.shape
A_ : Union[str, Any] = True
for i in range(0 , lowerCamelCase):
A_ : str = 0
for j in range(0 , cols - 1):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""")
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE : str = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
SCREAMING_SNAKE_CASE : List[Any] = {
"facebook/nllb-large-en-ro": 1024,
"facebook/nllb-200-distilled-600M": 1024,
}
# fmt: off
SCREAMING_SNAKE_CASE : List[Any] = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class snake_case ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = ["""input_ids""", """attention_mask"""]
_a = NllbTokenizer
_a = []
_a = []
def __init__( self, _lowercase=None, _lowercase=None, _lowercase="<s>", _lowercase="</s>", _lowercase="</s>", _lowercase="<s>", _lowercase="<unk>", _lowercase="<pad>", _lowercase="<mask>", _lowercase=None, _lowercase=None, _lowercase=None, _lowercase=False, **_lowercase, ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = AddedToken(_a, lstrip=_a, rstrip=_a ) if isinstance(_a, _a ) else mask_token
SCREAMING_SNAKE_CASE_ = legacy_behaviour
super().__init__(
vocab_file=_a, tokenizer_file=_a, bos_token=_a, eos_token=_a, sep_token=_a, cls_token=_a, unk_token=_a, pad_token=_a, mask_token=_a, src_lang=_a, tgt_lang=_a, additional_special_tokens=_a, legacy_behaviour=_a, **_a, )
SCREAMING_SNAKE_CASE_ = vocab_file
SCREAMING_SNAKE_CASE_ = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE_ = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
SCREAMING_SNAKE_CASE_ = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE_ = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE_ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def a__ ( self ) -> Tuple:
return self._src_lang
@src_lang.setter
def a__ ( self, _lowercase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def a__ ( self, _lowercase, _lowercase = None ) -> Dict:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def a__ ( self, _lowercase, _lowercase = None ) -> Tuple:
SCREAMING_SNAKE_CASE_ = [self.sep_token_id]
SCREAMING_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 + sep + token_ids_a + sep ) * [0]
def a__ ( self, _lowercase, _lowercase, _lowercase, _lowercase, **_lowercase ) -> Union[str, Any]:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
SCREAMING_SNAKE_CASE_ = src_lang
SCREAMING_SNAKE_CASE_ = self(_a, add_special_tokens=_a, return_tensors=_a, **_a )
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE_ = tgt_lang_id
return inputs
def a__ ( self, _lowercase, _lowercase = "eng_Latn", _lowercase = None, _lowercase = "fra_Latn", **_lowercase, ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = src_lang
SCREAMING_SNAKE_CASE_ = tgt_lang
return super().prepare_seqaseq_batch(_a, _a, **_a )
def a__ ( self ) -> List[str]:
return self.set_src_lang_special_tokens(self.src_lang )
def a__ ( self ) -> Optional[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def a__ ( self, _lowercase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE_ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE_ = [self.eos_token_id]
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE_ = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def a__ ( self, _lowercase ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE_ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE_ = [self.eos_token_id]
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE_ = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE_ = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def a__ ( self, _lowercase, _lowercase = None ) -> Any:
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
return
SCREAMING_SNAKE_CASE_ = os.path.join(
_a, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file, _a )
return (out_vocab_file,)
| 294 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)]
A_ : Union[str, Any] = True
for i in range(lowerCamelCase):
for j in range(m + 1):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
A_ : Optional[int] = True
if a[i].islower():
A_ : List[Any] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@register_to_config
def __init__( self : int , *,
lowerCAmelCase : int = 4 , lowerCAmelCase : int = 7_68 , lowerCAmelCase : int , lowerCAmelCase : Union[str, Any] , ) -> List[Any]:
"""simple docstring"""
super().__init__()
__lowerCAmelCase : List[Any] = nn.Parameter(torch.zeros(_a ) )
# parameters for additional clip time embeddings
__lowerCAmelCase : List[str] = nn.Linear(_a , _a )
__lowerCAmelCase : int = nn.Linear(_a , _a )
# parameters for encoder hidden states
__lowerCAmelCase : Any = clip_extra_context_tokens
__lowerCAmelCase : List[str] = nn.Linear(
_a , self.clip_extra_context_tokens * cross_attention_dim )
__lowerCAmelCase : Optional[int] = nn.Linear(_a , _a )
__lowerCAmelCase : Tuple = nn.LayerNorm(_a )
def SCREAMING_SNAKE_CASE ( self : str , *, lowerCAmelCase : Tuple , lowerCAmelCase : List[str] , lowerCAmelCase : Dict , lowerCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
__lowerCAmelCase : str = image_embeddings.shape[0]
__lowerCAmelCase : Tuple = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
__lowerCAmelCase : List[str] = classifier_free_guidance_embeddings.expand(
_a , -1 )
__lowerCAmelCase : Optional[int] = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
__lowerCAmelCase : Any = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
__lowerCAmelCase : Dict = self.embedding_proj(_a )
__lowerCAmelCase : Any = self.clip_image_embeddings_project_to_time_embeddings(_a )
__lowerCAmelCase : Union[str, Any] = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
__lowerCAmelCase : Optional[Any] = self.clip_extra_context_tokens_proj(_a )
__lowerCAmelCase : Tuple = clip_extra_context_tokens.reshape(_a , -1 , self.clip_extra_context_tokens )
__lowerCAmelCase : Union[str, Any] = clip_extra_context_tokens.permute(0 , 2 , 1 )
__lowerCAmelCase : str = self.encoder_hidden_states_proj(_a )
__lowerCAmelCase : Union[str, Any] = self.text_encoder_hidden_states_norm(_a )
__lowerCAmelCase : Tuple = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 651 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] ,_a : int ):
'''simple docstring'''
A_ : list[list[Edge]] = [[] for _ in range(_a )]
A_ : List[Any] = size
def __getitem__( self : int ,_a : int ):
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def _a ( self : str ):
'''simple docstring'''
return self._size
def _a ( self : str ,_a : int ,_a : int ,_a : int ):
'''simple docstring'''
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(_a ,_a ) )
def _a ( self : Dict ,_a : int ,_a : int ):
'''simple docstring'''
A_ : Tuple = deque([start_vertex] )
A_ : list[int | None] = [None] * self.size
A_ : Union[str, Any] = 0
while queue:
A_ : List[Any] = queue.popleft()
A_ : Tuple = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
A_ : Union[str, Any] = current_distance + edge.weight
A_ : Optional[Any] = distances[edge.destination_vertex]
if (
isinstance(_a ,_a )
and new_distance >= dest_vertex_distance
):
continue
A_ : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A__ :
@property
def UpperCamelCase__ ( self ):
return self.get_dummy_input()
@property
def UpperCamelCase__ ( self ):
if self.block_type == "down":
return (4, 3_2, 1_6, 1_6)
elif self.block_type == "mid":
return (4, 3_2, 3_2, 3_2)
elif self.block_type == "up":
return (4, 3_2, 6_4, 6_4)
raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.''' )
def UpperCamelCase__ ( self , __magic_name__=True , __magic_name__=False , __magic_name__=False , __magic_name__=False , ):
lowerCamelCase : str = 4
lowerCamelCase : Tuple = 3_2
lowerCamelCase : Union[str, Any] = (3_2, 3_2)
lowerCamelCase : Optional[Any] = torch.manual_seed(0 )
lowerCamelCase : Dict = torch.device(_a )
lowerCamelCase : Optional[int] = (batch_size, num_channels) + sizes
lowerCamelCase : List[str] = randn_tensor(_a , generator=_a , device=_a )
lowerCamelCase : str = {"""hidden_states""": hidden_states}
if include_temb:
lowerCamelCase : List[str] = 1_2_8
lowerCamelCase : str = randn_tensor((batch_size, temb_channels) , generator=_a , device=_a )
if include_res_hidden_states_tuple:
lowerCamelCase : List[str] = torch.manual_seed(1 )
lowerCamelCase : Union[str, Any] = (randn_tensor(_a , generator=_a , device=_a ),)
if include_encoder_hidden_states:
lowerCamelCase : List[Any] = floats_tensor((batch_size, 3_2, 3_2) ).to(_a )
if include_skip_sample:
lowerCamelCase : Any = randn_tensor(((batch_size, 3) + sizes) , generator=_a , device=_a )
return dummy_input
def UpperCamelCase__ ( self ):
lowerCamelCase : Optional[int] = {
"""in_channels""": 3_2,
"""out_channels""": 3_2,
"""temb_channels""": 1_2_8,
}
if self.block_type == "up":
lowerCamelCase : int = 3_2
if self.block_type == "mid":
init_dict.pop("""out_channels""" )
lowerCamelCase : Optional[int] = self.dummy_input
return init_dict, inputs_dict
def UpperCamelCase__ ( self , __magic_name__ ):
lowerCamelCase : Union[str, Any] = self.prepare_init_args_and_inputs_for_common()
lowerCamelCase : Optional[Any] = self.block_class(**_a )
unet_block.to(_a )
unet_block.eval()
with torch.no_grad():
lowerCamelCase : Union[str, Any] = unet_block(**_a )
if isinstance(_a , _a ):
lowerCamelCase : Union[str, Any] = output[0]
self.assertEqual(output.shape , self.output_shape )
lowerCamelCase : Any = output[0, -1, -3:, -3:]
lowerCamelCase : str = torch.tensor(_a ).to(_a )
assert torch_all_close(output_slice.flatten() , _a , atol=5e-3 )
@unittest.skipIf(torch_device == """mps""" , """Training is not supported in mps""" )
def UpperCamelCase__ ( self ):
lowerCamelCase : List[Any] = self.prepare_init_args_and_inputs_for_common()
lowerCamelCase : str = self.block_class(**_a )
model.to(_a )
model.train()
lowerCamelCase : int = model(**_a )
if isinstance(_a , _a ):
lowerCamelCase : str = output[0]
lowerCamelCase : Dict = torch.device(_a )
lowerCamelCase : List[str] = randn_tensor(output.shape , device=_a )
lowerCamelCase : Tuple = torch.nn.functional.mse_loss(_a , _a )
loss.backward()
| 681 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
A_ : Optional[Any] = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""")
| 665 | 0 |
'''simple docstring'''
import os
import re
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"""vocab_file""": """spiece.model"""}
lowercase_ = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
}
}
lowercase_ = {
"""google/bigbird-roberta-base""": 4_096,
"""google/bigbird-roberta-large""": 4_096,
"""google/bigbird-base-trivia-itc""": 4_096,
}
class a_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ['''input_ids''', '''attention_mask''']
UpperCamelCase = []
def __init__( self , A , A="<unk>" , A="<s>" , A="</s>" , A="<pad>" , A="[SEP]" , A="[MASK]" , A="[CLS]" , A = None , **A , ) -> Any:
_SCREAMING_SNAKE_CASE = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else bos_token
_SCREAMING_SNAKE_CASE = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else eos_token
_SCREAMING_SNAKE_CASE = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else unk_token
_SCREAMING_SNAKE_CASE = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else pad_token
_SCREAMING_SNAKE_CASE = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else cls_token
_SCREAMING_SNAKE_CASE = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
_SCREAMING_SNAKE_CASE = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , pad_token=_a , sep_token=_a , mask_token=_a , cls_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def snake_case_( self ) -> Dict:
return self.sp_model.get_piece_size()
def snake_case_( self ) -> Any:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A ) -> List[str]:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def snake_case_( self , A ) -> Tuple:
return self.sp_model.encode(_a , out_type=_a )
def snake_case_( self , A ) -> Any:
return self.sp_model.piece_to_id(_a )
def snake_case_( self , A ) -> Dict:
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(_a )
return token
def snake_case_( self , A ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
_SCREAMING_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(_a ) + token
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(_a )
_SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def snake_case_( self , A , A = False , A = None , A = True , **A , ) -> List[str]:
_SCREAMING_SNAKE_CASE = kwargs.pop("""use_source_tokenizer""" , _a )
_SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(_a , skip_special_tokens=_a )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_a ) )
_SCREAMING_SNAKE_CASE = []
sub_texts.append(_a )
else:
current_sub_text.append(_a )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_a ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
_SCREAMING_SNAKE_CASE = re.sub(R""" (\[(MASK|SEP)\])""" , R"""\1""" , """ """.join(_a ) )
else:
_SCREAMING_SNAKE_CASE = """""".join(_a )
_SCREAMING_SNAKE_CASE = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
_SCREAMING_SNAKE_CASE = self.clean_up_tokenization(_a )
return clean_text
else:
return text
def snake_case_( self , A , A = None ) -> Tuple:
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_SCREAMING_SNAKE_CASE = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
def snake_case_( self , A , A = None ) -> str:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_SCREAMING_SNAKE_CASE = [self.cls_token_id]
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def snake_case_( self , A , A = None , A = False ) -> Optional[Any]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
def snake_case_( self , A , A = None ) -> Tuple:
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
_SCREAMING_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]
| 314 |
'''simple docstring'''
# Copyright 2021 The HuggingFace 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 argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def lowerCamelCase ( ):
A_ : Optional[int] = ArgumentParser("""Accelerate CLI tool""" , usage="""accelerate <command> [<args>]""" , allow_abbrev=lowerCamelCase)
A_ : Optional[int] = parser.add_subparsers(help="""accelerate command helpers""")
# Register commands
get_config_parser(subparsers=lowerCamelCase)
env_command_parser(subparsers=lowerCamelCase)
launch_command_parser(subparsers=lowerCamelCase)
tpu_command_parser(subparsers=lowerCamelCase)
test_command_parser(subparsers=lowerCamelCase)
# Let's go
A_ : Dict = parser.parse_args()
if not hasattr(lowerCamelCase , """func"""):
parser.print_help()
exit(1)
# Run
args.func(lowerCamelCase)
if __name__ == "__main__":
main()
| 665 | 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 _UpperCamelCase :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=7 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=99 , lowerCAmelCase__=32 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=37 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=5_12 , lowerCAmelCase__=16 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=3 , lowerCAmelCase__=4 , lowerCAmelCase__=None , ) -> List[str]:
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
if self.use_token_type_ids:
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''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=_a , initializer_range=self.initializer_range , )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
'''simple docstring'''
__lowercase = LlamaModel(config=_a )
model.to(_a )
model.eval()
__lowercase = model(_a , attention_mask=_a )
__lowercase = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> List[str]:
'''simple docstring'''
__lowercase = True
__lowercase = LlamaModel(_a )
model.to(_a )
model.eval()
__lowercase = model(
_a , attention_mask=_a , encoder_hidden_states=_a , encoder_attention_mask=_a , )
__lowercase = model(
_a , attention_mask=_a , encoder_hidden_states=_a , )
__lowercase = model(_a , attention_mask=_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> int:
'''simple docstring'''
__lowercase = LlamaForCausalLM(config=_a )
model.to(_a )
model.eval()
__lowercase = model(_a , attention_mask=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> Optional[int]:
'''simple docstring'''
__lowercase = True
__lowercase = True
__lowercase = LlamaForCausalLM(config=_a )
model.to(_a )
model.eval()
# first forward pass
__lowercase = model(
_a , attention_mask=_a , encoder_hidden_states=_a , encoder_attention_mask=_a , use_cache=_a , )
__lowercase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowercase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowercase = torch.cat([input_mask, next_mask] , dim=-1 )
__lowercase = model(
_a , attention_mask=_a , encoder_hidden_states=_a , encoder_attention_mask=_a , output_hidden_states=_a , )["""hidden_states"""][0]
__lowercase = model(
_a , attention_mask=_a , encoder_hidden_states=_a , encoder_attention_mask=_a , past_key_values=_a , output_hidden_states=_a , )["""hidden_states"""][0]
# select random slice
__lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowercase = 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(_a , _a , atol=1E-3 ) )
def _SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
(
__lowercase
) = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,unittest.TestCase ):
"""simple docstring"""
__a : Optional[int] = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
__a : List[str] = (LlamaForCausalLM,) if is_torch_available() else ()
__a : Any = (
{
'''feature-extraction''': LlamaModel,
'''text-classification''': LlamaForSequenceClassification,
'''text-generation''': LlamaForCausalLM,
'''zero-shot''': LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : Dict = False
__a : List[Any] = False
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = LlamaModelTester(self )
__lowercase = ConfigTester(self , config_class=_a , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowercase = type
self.model_tester.create_and_check_model(*_a )
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = input_dict["""input_ids"""]
__lowercase = input_ids.ne(1 ).to(_a )
__lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowercase = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
__lowercase = model(_a , attention_mask=_a , labels=_a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = """single_label_classification"""
__lowercase = input_dict["""input_ids"""]
__lowercase = input_ids.ne(1 ).to(_a )
__lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowercase = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
__lowercase = model(_a , attention_mask=_a , labels=_a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = """multi_label_classification"""
__lowercase = input_dict["""input_ids"""]
__lowercase = input_ids.ne(1 ).to(_a )
__lowercase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__lowercase = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
__lowercase = model(_a , attention_mask=_a , labels=_a )
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 _SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = ids_tensor([1, 10] , config.vocab_size )
__lowercase = 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
__lowercase = LlamaModel(_a )
original_model.to(_a )
original_model.eval()
__lowercase = original_model(_a ).last_hidden_state
__lowercase = original_model(_a ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__lowercase = {"""type""": scaling_type, """factor""": 10.0}
__lowercase = LlamaModel(_a )
scaled_model.to(_a )
scaled_model.eval()
__lowercase = scaled_model(_a ).last_hidden_state
__lowercase = scaled_model(_a ).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(_a , _a , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(_a , _a , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_a , _a , atol=1E-5 ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
__lowercase = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''' , device_map='''auto''' )
__lowercase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
__lowercase = 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 ) , _a , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
__lowercase = 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] , _a , atol=1E-5 , rtol=1E-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
__lowercase = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
__lowercase = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''' , device_map='''auto''' )
__lowercase = model(torch.tensor(_a ) )
# Expected mean on dim = -1
__lowercase = 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 ) , _a , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
__lowercase = 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] , _a , atol=1E-5 , rtol=1E-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def _SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
__lowercase = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
__lowercase = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' , device_map='''auto''' )
__lowercase = model(torch.tensor(_a ) )
# Expected mean on dim = -1
__lowercase = 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 ) , _a , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
__lowercase = 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 ) , _a , 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 _SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
__lowercase = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
__lowercase = LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''' , device_map='''auto''' )
__lowercase = model(torch.tensor(_a ) )
__lowercase = 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 ) , _a , atol=1E-2 , rtol=1E-2 )
# fmt: off
__lowercase = 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] , _a , atol=1E-5 , rtol=1E-5 )
@unittest.skip('''Model is curently gated''' )
@slow
def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = """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"""
__lowercase = """Simply put, the theory of relativity states that """
__lowercase = LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' )
__lowercase = tokenizer.encode(_a , return_tensors='''pt''' )
__lowercase = LlamaForCausalLM.from_pretrained(
'''meta-llama/Llama-2-13b-chat-hf''' , device_map='''sequential''' , use_safetensors=_a )
# greedy generation outputs
__lowercase = model.generate(_a , max_new_tokens=64 , top_p=_a , temperature=1 , do_sample=_a )
__lowercase = tokenizer.decode(generated_ids[0] , skip_special_tokens=_a )
self.assertEqual(_a , _a ) | 534 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_lowercase : List[Any] =logging.getLogger(__name__)
_lowercase : Optional[int] =50 # max width of layer names
_lowercase : Tuple =70 # max width of quantizer names
def lowerCAmelCase_ ( _lowercase : Any) -> Dict:
"""simple docstring"""
a__ : Optional[int] = parser.add_argument_group("""quant_trainer arguments""")
group.add_argument("""--wprec""" , type=_lowercase , default=8 , help="""weight precision""")
group.add_argument("""--aprec""" , type=_lowercase , default=8 , help="""activation precision""")
group.add_argument("""--quant-per-tensor""" , action="""store_true""" , help="""per tensor weight scaling""")
group.add_argument("""--quant-disable""" , action="""store_true""" , help="""disable all quantizers""")
group.add_argument("""--quant-disable-embeddings""" , action="""store_true""" , help="""disable all embeddings quantizers""")
group.add_argument("""--quant-disable-keyword""" , type=_lowercase , nargs="""+""" , help="""disable quantizers by keyword""")
group.add_argument("""--quant-disable-layer-module""" , type=_lowercase , help="""disable quantizers by keyword under layer.""")
group.add_argument("""--quant-enable-layer-module""" , type=_lowercase , help="""enable quantizers by keyword under layer""")
group.add_argument("""--calibrator""" , default="""max""" , help="""which quantization range calibrator to use""")
group.add_argument("""--percentile""" , default=_lowercase , type=_lowercase , help="""percentile for PercentileCalibrator""")
group.add_argument("""--fuse-qkv""" , action="""store_true""" , help="""use the same scale factor for qkv""")
group.add_argument("""--clip-gelu""" , metavar="""N""" , type=_lowercase , help="""clip gelu output maximum value to N""")
group.add_argument(
"""--recalibrate-weights""" , action="""store_true""" , help=(
"""recalibrate weight amaxes by taking the max of the weights."""
""" amaxes will be computed with the current quantization granularity (axis)."""
) , )
def lowerCAmelCase_ ( _lowercase : Tuple) -> Tuple:
"""simple docstring"""
if args.calibrator == "max":
a__ : Union[str, Any] = """max"""
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError("""Specify --percentile when using percentile calibrator""")
a__ : Dict = """histogram"""
elif args.calibrator == "mse":
a__ : Union[str, Any] = """histogram"""
else:
raise ValueError(F'''Invalid calibrator {args.calibrator}''')
a__ : str = QuantDescriptor(num_bits=args.aprec , calib_method=_lowercase)
a__ : Tuple = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)))
quant_nn.QuantLinear.set_default_quant_desc_input(_lowercase)
quant_nn.QuantLinear.set_default_quant_desc_weight(_lowercase)
def lowerCAmelCase_ ( _lowercase : Optional[Any] , _lowercase : Dict , _lowercase : str=False , _lowercase : Any=False) -> List[Any]:
"""simple docstring"""
logger.info("""Configuring Model for Quantization""")
logger.info(F'''using quantization package {pytorch_quantization.__file__}''')
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_lowercase , ["""embeddings"""] , which="""weight""" , _disabled=_lowercase)
if args.quant_disable:
set_quantizer_by_name(_lowercase , [""""""] , _disabled=_lowercase)
if args.quant_disable_keyword:
set_quantizer_by_name(_lowercase , args.quant_disable_keyword , _disabled=_lowercase)
if args.quant_disable_layer_module:
set_quantizer_by_name(_lowercase , [R"""layer.\d+.""" + args.quant_disable_layer_module] , _disabled=_lowercase)
if args.quant_enable_layer_module:
set_quantizer_by_name(_lowercase , [R"""layer.\d+.""" + args.quant_enable_layer_module] , _disabled=_lowercase)
if args.recalibrate_weights:
recalibrate_weights(_lowercase)
if args.fuse_qkv:
fuse_qkv(_lowercase , _lowercase)
if args.clip_gelu:
clip_gelu(_lowercase , args.clip_gelu)
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_lowercase)
def lowerCAmelCase_ ( _lowercase : Dict) -> Any:
"""simple docstring"""
logger.info("""Enabling Calibration""")
for name, module in model.named_modules():
if name.endswith("""_quantizer"""):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F'''{name:80}: {module}''')
def lowerCAmelCase_ ( _lowercase : Dict , _lowercase : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
logger.info("""Loading calibrated amax""")
for name, module in model.named_modules():
if name.endswith("""_quantizer"""):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator):
module.load_calib_amax()
else:
module.load_calib_amax("""percentile""" , percentile=args.percentile)
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_lowercase)
def lowerCAmelCase_ ( _lowercase : List[str] , _lowercase : Dict) -> Union[str, Any]:
"""simple docstring"""
def fusea(_lowercase : int , _lowercase : Dict , _lowercase : str):
for mod in [qq, qk, qv]:
if not hasattr(_lowercase , """_amax"""):
print(""" WARNING: NO AMAX BUFFER""")
return
a__ : Dict = qq._amax.detach().item()
a__ : int = qk._amax.detach().item()
a__ : Union[str, Any] = qv._amax.detach().item()
a__ : Any = max(_lowercase , _lowercase , _lowercase)
qq._amax.fill_(_lowercase)
qk._amax.fill_(_lowercase)
qv._amax.fill_(_lowercase)
logger.info(F''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''')
for name, mod in model.named_modules():
if name.endswith(""".attention.self"""):
logger.info(F'''FUSE_QKV: {name:{name_width}}''')
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer)
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer)
def lowerCAmelCase_ ( _lowercase : Optional[int] , _lowercase : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
for name, mod in model.named_modules():
if name.endswith(""".output.dense""") and not name.endswith("""attention.output.dense"""):
a__ : Union[str, Any] = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_lowercase)
a__ : Union[str, Any] = mod._input_quantizer._amax.data.detach().item()
logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''')
def lowerCAmelCase_ ( _lowercase : Optional[Any]) -> Tuple:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(_lowercase , """_weight_quantizer""") and mod._weight_quantizer.axis is not None:
a__ : List[str] = mod.weight.shape[0]
a__ : Tuple = mod._weight_quantizer._amax.detach()
a__ : int = torch.ones(_lowercase , dtype=amax.dtype , device=amax.device) * amax
print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''')
def lowerCAmelCase_ ( _lowercase : List[str]) -> Optional[int]:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(_lowercase , """_weight_quantizer"""):
if not hasattr(mod.weight_quantizer , """_amax"""):
print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER""")
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
a__ : Dict = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis)
a__ : int = set(range(len(mod.weight.size()))) - axis_set
a__ : Any = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_lowercase , keepdims=_lowercase).detach()
logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''')
a__ : int = amax
def lowerCAmelCase_ ( _lowercase : Any , _lowercase : Dict=25 , _lowercase : Union[str, Any]=180 , _lowercase : int=None) -> Optional[Any]:
"""simple docstring"""
if ignore is None:
a__ : List[Any] = []
elif not isinstance(_lowercase , _lowercase):
a__ : Optional[Any] = [ignore]
a__ : Optional[int] = 0
for name, mod in model.named_modules():
if not hasattr(_lowercase , """weight"""):
continue
a__ : str = max(_lowercase , len(_lowercase))
for name, mod in model.named_modules():
a__ : Any = getattr(_lowercase , """_input_quantizer""" , _lowercase)
a__ : Optional[int] = getattr(_lowercase , """_weight_quantizer""" , _lowercase)
if not hasattr(_lowercase , """weight"""):
continue
if type(_lowercase) in ignore:
continue
if [True for s in ignore if type(_lowercase) is str and s in name]:
continue
a__ : Optional[Any] = F'''Act:{input_q.extra_repr()}'''
a__ : List[str] = F'''Wgt:{weight_q.extra_repr()}'''
a__ : Tuple = F'''{name:{name_width}} {act_str} {wgt_str}'''
if len(_lowercase) <= line_width:
logger.info(_lowercase)
else:
logger.info(F'''{name:{name_width}} {act_str}''')
logger.info(F'''{' ':{name_width}} {wgt_str}''')
def lowerCAmelCase_ ( _lowercase : int) -> Any:
"""simple docstring"""
a__ : Optional[Any] = 0
for name, mod in model.named_modules():
if isinstance(_lowercase , pytorch_quantization.nn.TensorQuantizer):
print(F'''{name:80} {mod}''')
count += 1
print(F'''{count} TensorQuantizers found in model''')
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str , _lowercase : int , _lowercase : Optional[int] , _lowercase : Optional[int]) -> List[Any]:
"""simple docstring"""
a__ : Optional[Any] = getattr(_lowercase , _lowercase , _lowercase)
if quantizer_mod is not None:
assert hasattr(_lowercase , _lowercase)
setattr(_lowercase , _lowercase , _lowercase)
else:
logger.warning(F'''{name} has no {quantizer}''')
def lowerCAmelCase_ ( _lowercase : int , _lowercase : Optional[int] , _lowercase : List[Any]="both" , **_lowercase : Tuple) -> Dict:
"""simple docstring"""
a__ : Dict = F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(_lowercase , _lowercase , """_input_quantizer""" , _lowercase , _lowercase)
if which in ["weight", "both"]:
set_quantizer(_lowercase , _lowercase , """_weight_quantizer""" , _lowercase , _lowercase)
logger.info(_lowercase)
def lowerCAmelCase_ ( _lowercase : Optional[int] , _lowercase : Tuple , **_lowercase : Tuple) -> Union[str, Any]:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(_lowercase , """_input_quantizer""") or hasattr(_lowercase , """_weight_quantizer"""):
for n in names:
if re.search(_lowercase , _lowercase):
set_quantizers(_lowercase , _lowercase , **_lowercase)
elif name.endswith("""_quantizer"""):
for n in names:
if re.search(_lowercase , _lowercase):
a__ : Optional[Any] = F'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += F''' {k}={v}'''
setattr(_lowercase , _lowercase , _lowercase)
logger.info(_lowercase)
| 136 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['YolosFeatureExtractor']
__magic_name__ = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
'YolosForObjectDetection',
'YolosModel',
'YolosPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ = '''\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n'''
UpperCAmelCase__ = '''\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'''
UpperCAmelCase__ = r'''\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n >>> metric = datasets.load_metric("competition_math")\n >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n >>> print(results)\n {\'accuracy\': 1.0}\n'''
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase ( self : Optional[Any] ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/hendrycks/math' , codebase_urls=['https://github.com/hendrycks/math'] , )
def lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] ) -> int:
__A= 0.0
for i, j in zip(_a , _a ):
n_correct += 1.0 if math_equivalence.is_equiv(_a , _a ) else 0.0
__A= n_correct / len(_a )
return {
"accuracy": accuracy,
}
| 186 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class a_ ( __SCREAMING_SNAKE_CASE ):
lowerCamelCase__ : Dict = 'git_vision_model'
def __init__( self , UpperCAmelCase=7_68 , UpperCAmelCase=30_72 , UpperCAmelCase=12 , UpperCAmelCase=12 , UpperCAmelCase=3 , UpperCAmelCase=2_24 , UpperCAmelCase=16 , UpperCAmelCase="quick_gelu" , UpperCAmelCase=1e-5 , UpperCAmelCase=0.0 , UpperCAmelCase=0.02 , **UpperCAmelCase , ):
super().__init__(**_a )
a_ = hidden_size
a_ = intermediate_size
a_ = num_hidden_layers
a_ = num_attention_heads
a_ = num_channels
a_ = patch_size
a_ = image_size
a_ = initializer_range
a_ = attention_dropout
a_ = layer_norm_eps
a_ = hidden_act
@classmethod
def lowerCAmelCase__ ( cls , UpperCAmelCase , **UpperCAmelCase ):
cls._set_token_in_kwargs(_a )
a_ = cls.get_config_dict(_a , **_a )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
a_ = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_a , **_a )
class a_ ( __SCREAMING_SNAKE_CASE ):
lowerCamelCase__ : Optional[int] = 'git'
def __init__( self , UpperCAmelCase=None , UpperCAmelCase=3_05_22 , UpperCAmelCase=7_68 , UpperCAmelCase=6 , UpperCAmelCase=12 , UpperCAmelCase=30_72 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=10_24 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-12 , UpperCAmelCase=0 , UpperCAmelCase="absolute" , UpperCAmelCase=True , UpperCAmelCase=False , UpperCAmelCase=1_01 , UpperCAmelCase=1_02 , UpperCAmelCase=None , **UpperCAmelCase , ):
super().__init__(bos_token_id=_a , eos_token_id=_a , pad_token_id=_a , **_a )
if vision_config is None:
a_ = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
a_ = GitVisionConfig(**_a )
a_ = vocab_size
a_ = hidden_size
a_ = num_hidden_layers
a_ = num_attention_heads
a_ = hidden_act
a_ = intermediate_size
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = max_position_embeddings
a_ = initializer_range
a_ = layer_norm_eps
a_ = position_embedding_type
a_ = use_cache
a_ = tie_word_embeddings
a_ = num_image_with_embedding
a_ = bos_token_id
a_ = eos_token_id
def lowerCAmelCase__ ( self ):
a_ = copy.deepcopy(self.__dict__ )
a_ = self.vision_config.to_dict()
a_ = self.__class__.model_type
return output
| 263 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCamelCase)):
if indegree[i] == 0:
queue.append(lowerCamelCase)
while queue:
A_ : Any = queue.pop(0)
cnt += 1
topo.append(lowerCamelCase)
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(lowerCamelCase)
if cnt != len(lowerCamelCase):
print("""Cycle exists""")
else:
print(lowerCamelCase)
# Adjacency List of Graph
__magic_name__ = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 665 | 0 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Features ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : Any =np.inf
def set_batch_size(UpperCAmelCase_ : FeatureType ) -> None:
nonlocal batch_size
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE_ : int =min(UpperCAmelCase_ , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE_ : List[str] =min(UpperCAmelCase_ , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and feature.dtype == "binary":
SCREAMING_SNAKE_CASE_ : Tuple =min(UpperCAmelCase_ , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(UpperCAmelCase_ , UpperCAmelCase_ )
return None if batch_size is np.inf else batch_size
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , __A , __A = None , __A = None , __A = None , __A = False , __A = False , __A = None , **__A , ) -> Any:
super().__init__(
_a , split=_a , features=_a , cache_dir=_a , keep_in_memory=_a , streaming=_a , num_proc=_a , **_a , )
SCREAMING_SNAKE_CASE_ : Optional[Any] =path_or_paths if isinstance(_a , _a ) else {self.split: path_or_paths}
SCREAMING_SNAKE_CASE_ : Optional[int] =_PACKAGED_DATASETS_MODULES["""parquet"""][1]
SCREAMING_SNAKE_CASE_ : str =Parquet(
cache_dir=_a , data_files=_a , features=_a , hash=_a , **_a , )
def _snake_case ( self ) -> Optional[Any]:
if self.streaming:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE_ : Optional[int] =None
SCREAMING_SNAKE_CASE_ : List[Any] =None
SCREAMING_SNAKE_CASE_ : Union[str, Any] =None
SCREAMING_SNAKE_CASE_ : List[Any] =None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.builder.as_dataset(
split=self.split , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class lowercase_ :
def __init__( self , __A , __A , __A = None , **__A , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : Tuple =dataset
SCREAMING_SNAKE_CASE_ : Tuple =path_or_buf
SCREAMING_SNAKE_CASE_ : Optional[Any] =batch_size or get_writer_batch_size(dataset.features )
SCREAMING_SNAKE_CASE_ : List[str] =parquet_writer_kwargs
def _snake_case ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : List[str] =self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , '''wb+''' ) as buffer:
SCREAMING_SNAKE_CASE_ : Dict =self._write(file_obj=_a , batch_size=_a , **self.parquet_writer_kwargs )
else:
SCREAMING_SNAKE_CASE_ : List[Any] =self._write(file_obj=self.path_or_buf , batch_size=_a , **self.parquet_writer_kwargs )
return written
def _snake_case ( self , __A , __A , **__A ) -> int:
SCREAMING_SNAKE_CASE_ : List[Any] =0
SCREAMING_SNAKE_CASE_ : int =parquet_writer_kwargs.pop('''path_or_buf''' , _a )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.dataset.features.arrow_schema
SCREAMING_SNAKE_CASE_ : List[Any] =pq.ParquetWriter(_a , schema=_a , **_a )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , _a ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating parquet from Arrow format''' , ):
SCREAMING_SNAKE_CASE_ : Optional[Any] =query_table(
table=self.dataset._data , key=slice(_a , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(_a )
written += batch.nbytes
writer.close()
return written
| 443 |
'''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 __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : List[Any] ,_a : Dict=13 ,_a : List[str]=7 ,_a : Dict=True ,_a : List[Any]=True ,_a : Dict=False ,_a : Optional[int]=True ,_a : List[Any]=99 ,_a : Any=32 ,_a : Optional[int]=5 ,_a : List[Any]=4 ,_a : int=37 ,_a : List[Any]="gelu" ,_a : List[str]=0.1 ,_a : Union[str, Any]=0.1 ,_a : Any=512 ,_a : int=16 ,_a : Optional[int]=2 ,_a : Any=0.02 ,_a : Any=3 ,_a : Any=4 ,_a : List[str]=None ,):
'''simple docstring'''
A_ : List[str] = parent
A_ : Any = batch_size
A_ : Tuple = seq_length
A_ : List[str] = is_training
A_ : Tuple = use_input_mask
A_ : Dict = use_token_type_ids
A_ : List[Any] = use_labels
A_ : Union[str, Any] = vocab_size
A_ : Any = hidden_size
A_ : str = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : str = intermediate_size
A_ : Tuple = hidden_act
A_ : Any = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : List[str] = max_position_embeddings
A_ : int = type_vocab_size
A_ : Union[str, Any] = type_sequence_label_size
A_ : Any = initializer_range
A_ : List[Any] = num_labels
A_ : Optional[Any] = num_choices
A_ : List[Any] = scope
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : int = None
if self.use_input_mask:
A_ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
A_ : Dict = None
if self.use_token_type_ids:
A_ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : str = None
A_ : Any = None
A_ : str = None
if self.use_labels:
A_ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : Any = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Optional[int] = ids_tensor([self.batch_size] ,self.num_choices )
A_ : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ):
'''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=_a ,initializer_range=self.initializer_range ,)
def _a ( self : Union[str, Any] ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Any ,_a : Any ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Tuple ):
'''simple docstring'''
A_ : Any = LlamaModel(config=_a )
model.to(_a )
model.eval()
A_ : Optional[Any] = model(_a ,attention_mask=_a )
A_ : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Optional[int] ,_a : int ,_a : List[str] ,_a : Any ,_a : Any ,_a : Dict ,_a : List[str] ,_a : Optional[int] ,_a : Any ,_a : List[str] ,):
'''simple docstring'''
A_ : List[str] = True
A_ : Union[str, Any] = LlamaModel(_a )
model.to(_a )
model.eval()
A_ : Tuple = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,)
A_ : List[Any] = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,)
A_ : int = model(_a ,attention_mask=_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Any ,_a : Any ,_a : Optional[int] ,_a : List[Any] ,_a : List[Any] ,_a : Dict ,_a : Tuple ,_a : Optional[int] ,_a : List[Any] ,_a : Union[str, Any] ,):
'''simple docstring'''
A_ : List[Any] = LlamaForCausalLM(config=_a )
model.to(_a )
model.eval()
A_ : Dict = model(_a ,attention_mask=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : str ,_a : List[Any] ,_a : Dict ,_a : str ,_a : Tuple ,_a : Tuple ,_a : Tuple ,_a : Optional[Any] ,_a : Dict ,_a : Union[str, Any] ,):
'''simple docstring'''
A_ : Optional[Any] = True
A_ : Any = True
A_ : Tuple = LlamaForCausalLM(config=_a )
model.to(_a )
model.eval()
# first forward pass
A_ : Optional[int] = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,use_cache=_a ,)
A_ : Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
A_ : int = ids_tensor((self.batch_size, 3) ,config.vocab_size )
A_ : List[Any] = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
A_ : Tuple = torch.cat([input_ids, next_tokens] ,dim=-1 )
A_ : int = torch.cat([input_mask, next_mask] ,dim=-1 )
A_ : List[str] = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,output_hidden_states=_a ,)["""hidden_states"""][0]
A_ : Any = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,past_key_values=_a ,output_hidden_states=_a ,)["""hidden_states"""][0]
# select random slice
A_ : List[str] = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
A_ : str = output_from_no_past[:, -3:, random_slice_idx].detach()
A_ : int = 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(_a ,_a ,atol=1e-3 ) )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Any = config_and_inputs
A_ : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , 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 _a ( self : List[Any] ):
'''simple docstring'''
A_ : Union[str, Any] = LlamaModelTester(self )
A_ : List[str] = ConfigTester(self ,config_class=_a ,hidden_size=37 )
def _a ( self : Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A_ : Dict = type
self.model_tester.create_and_check_model(*_a )
def _a ( self : List[Any] ):
'''simple docstring'''
A_ , A_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
A_ : List[str] = 3
A_ : Any = input_dict["""input_ids"""]
A_ : Union[str, Any] = input_ids.ne(1 ).to(_a )
A_ : Union[str, Any] = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
A_ : List[Any] = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : int = model(_a ,attention_mask=_a ,labels=_a )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self : Dict ):
'''simple docstring'''
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : str = 3
A_ : Union[str, Any] = """single_label_classification"""
A_ : Union[str, Any] = input_dict["""input_ids"""]
A_ : List[Any] = input_ids.ne(1 ).to(_a )
A_ : Dict = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
A_ : List[Any] = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : List[str] = model(_a ,attention_mask=_a ,labels=_a )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Dict = 3
A_ : Dict = """multi_label_classification"""
A_ : Any = input_dict["""input_ids"""]
A_ : Optional[Any] = input_ids.ne(1 ).to(_a )
A_ : List[str] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size ).to(torch.float )
A_ : Optional[int] = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,attention_mask=_a ,labels=_a )
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 _a ( self : Any ):
'''simple docstring'''
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def _a ( self : Optional[Any] ,_a : List[Any] ):
'''simple docstring'''
A_ , A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = ids_tensor([1, 10] ,config.vocab_size )
A_ : Union[str, Any] = 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
A_ : int = LlamaModel(_a )
original_model.to(_a )
original_model.eval()
A_ : Tuple = original_model(_a ).last_hidden_state
A_ : Union[str, Any] = original_model(_a ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
A_ : Tuple = {"""type""": scaling_type, """factor""": 10.0}
A_ : int = LlamaModel(_a )
scaled_model.to(_a )
scaled_model.eval()
A_ : List[Any] = scaled_model(_a ).last_hidden_state
A_ : Any = scaled_model(_a ).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(_a ,_a ,atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(_a ,_a ,atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_a ,_a ,atol=1e-5 ) )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Any = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : List[str] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf""" ,device_map="""auto""" )
A_ : str = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
A_ : Union[str, Any] = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
A_ : str = 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] ,_a ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def _a ( self : str ):
'''simple docstring'''
A_ : Dict = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : Optional[int] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf""" ,device_map="""auto""" )
A_ : Tuple = model(torch.tensor(_a ) )
# Expected mean on dim = -1
A_ : str = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
A_ : str = 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] ,_a ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Union[str, Any] = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : Optional[int] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" ,device_map="""auto""" )
A_ : int = model(torch.tensor(_a ) )
# Expected mean on dim = -1
A_ : Union[str, Any] = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
A_ : Optional[int] = 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 ) ,_a ,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 _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : str = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf""" ,device_map="""auto""" )
A_ : Tuple = model(torch.tensor(_a ) )
A_ : Dict = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# fmt: off
A_ : List[str] = 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] ,_a ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip("""Model is curently gated""" )
@slow
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = """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"""
A_ : List[str] = """Simply put, the theory of relativity states that """
A_ : Any = LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" )
A_ : Union[str, Any] = tokenizer.encode(_a ,return_tensors="""pt""" )
A_ : List[str] = LlamaForCausalLM.from_pretrained(
"""meta-llama/Llama-2-13b-chat-hf""" ,device_map="""sequential""" ,use_safetensors=_a )
# greedy generation outputs
A_ : str = model.generate(_a ,max_new_tokens=64 ,top_p=_a ,temperature=1 ,do_sample=_a )
A_ : Optional[Any] = tokenizer.decode(generated_ids[0] ,skip_special_tokens=_a )
self.assertEqual(_a ,_a )
| 665 | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class _lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: Tuple = 'pixel_values'
SCREAMING_SNAKE_CASE: Dict = False
SCREAMING_SNAKE_CASE: int = TimmBackboneConfig
def __init__( self , lowerCamelCase__ , **lowerCamelCase__ ):
requires_backends(self , "timm" )
super().__init__(_a )
lowerCAmelCase_: List[str] = config
if config.backbone is None:
raise ValueError("backbone is not set in the config. Please set it to a timm model name." )
if config.backbone not in timm.list_models():
raise ValueError(F'''backbone {config.backbone} is not supported by timm.''' )
if hasattr(_a , "out_features" ) and config.out_features is not None:
raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )
lowerCAmelCase_: str = getattr(_a , "use_pretrained_backbone" , _a )
if pretrained is None:
raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )
# We just take the final layer by default. This matches the default for the transformers models.
lowerCAmelCase_: Any = config.out_indices if getattr(_a , "out_indices" , _a ) is not None else (-1,)
lowerCAmelCase_: Union[str, Any] = timm.create_model(
config.backbone , pretrained=_a , features_only=config.features_only , in_chans=config.num_channels , out_indices=_a , **_a , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
lowerCAmelCase_: Dict = self._backbone.return_layers
lowerCAmelCase_: Union[str, Any] = {layer["""module"""]: str(_a ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(_a )
@classmethod
def _a ( cls , lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ ):
requires_backends(cls , ["vision", "timm"] )
from ...models.timm_backbone import TimmBackboneConfig
lowerCAmelCase_: List[str] = kwargs.pop("config" , TimmBackboneConfig() )
lowerCAmelCase_: Any = kwargs.pop("use_timm_backbone" , _a )
if not use_timm:
raise ValueError("use_timm_backbone must be True for timm backbones" )
lowerCAmelCase_: Dict = kwargs.pop("num_channels" , config.num_channels )
lowerCAmelCase_: Any = kwargs.pop("features_only" , config.features_only )
lowerCAmelCase_: Tuple = kwargs.pop("use_pretrained_backbone" , config.use_pretrained_backbone )
lowerCAmelCase_: Union[str, Any] = kwargs.pop("out_indices" , config.out_indices )
lowerCAmelCase_: Dict = TimmBackboneConfig(
backbone=_a , num_channels=_a , features_only=_a , use_pretrained_backbone=_a , out_indices=_a , )
return super()._from_config(_a , **_a )
def _a ( self , lowerCamelCase__ ):
pass
def _a ( self , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , **lowerCamelCase__ ):
lowerCAmelCase_: Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase_: Optional[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase_: Dict = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("Cannot output attentions for timm backbones at the moment" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
lowerCAmelCase_: Optional[int] = self._all_layers
lowerCAmelCase_: Tuple = self._backbone(_a , **_a )
lowerCAmelCase_: Any = self._return_layers
lowerCAmelCase_: Optional[Any] = tuple(hidden_states[i] for i in self.out_indices )
else:
lowerCAmelCase_: Union[str, Any] = self._backbone(_a , **_a )
lowerCAmelCase_: List[Any] = None
lowerCAmelCase_: List[str] = tuple(_a )
lowerCAmelCase_: int = tuple(_a ) if hidden_states is not None else None
if not return_dict:
lowerCAmelCase_: int = (feature_maps,)
if output_hidden_states:
lowerCAmelCase_: Dict = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=_a , hidden_states=_a , attentions=_a ) | 613 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n'
__magic_name__ = '\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'
__magic_name__ = r'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n >>> metric = datasets.load_metric("competition_math")\n >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n >>> print(results)\n {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
'''simple docstring'''
def _a ( self : Optional[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ),
"""references""": datasets.Value("""string""" ),
} ) ,homepage="""https://github.com/hendrycks/math""" ,codebase_urls=["""https://github.com/hendrycks/math"""] ,)
def _a ( self : List[Any] ,_a : Union[str, Any] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Union[str, Any] = 0.0
for i, j in zip(_a ,_a ):
n_correct += 1.0 if math_equivalence.is_equiv(_a ,_a ) else 0.0
A_ : List[str] = n_correct / len(_a )
return {
"accuracy": accuracy,
}
| 665 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
UpperCAmelCase : Optional[Any] = {
"""vocab_file""": {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
"""allenai/longformer-large-4096""": (
"""https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
),
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
),
},
"""merges_file""": {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
"""allenai/longformer-large-4096""": (
"""https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
),
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
),
},
}
UpperCAmelCase : int = {
"""allenai/longformer-base-4096""": 4096,
"""allenai/longformer-large-4096""": 4096,
"""allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
"""allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
"""allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _A ( ):
"""simple docstring"""
a__ : Union[str, Any] =(
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
a__ : Optional[Any] =bs[:]
a__ : List[str] =0
for b in range(2**8 ):
if b not in bs:
bs.append(SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
a__ : List[Any] =[chr(SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
a__ : int =set()
a__ : int =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
a__ : List[str] =char
return pairs
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE):
_lowercase : str = VOCAB_FILES_NAMES
_lowercase : int = PRETRAINED_VOCAB_FILES_MAP
_lowercase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : List[str] = ["""input_ids""", """attention_mask"""]
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__="replace" , lowerCAmelCase__="<s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__=False , **lowerCAmelCase__ , ) -> str:
'''simple docstring'''
a__ : Dict =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else bos_token
a__ : Optional[int] =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else eos_token
a__ : Optional[Any] =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else sep_token
a__ : int =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else cls_token
a__ : int =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else unk_token
a__ : Optional[Any] =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
a__ : Any =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
errors=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , add_prefix_space=_a , **_a , )
with open(_a , encoding="utf-8" ) as vocab_handle:
a__ : str =json.load(_a )
a__ : Optional[int] ={v: k for k, v in self.encoder.items()}
a__ : List[str] =errors # how to handle errors in decoding
a__ : List[str] =bytes_to_unicode()
a__ : str ={v: k for k, v in self.byte_encoder.items()}
with open(_a , encoding="utf-8" ) as merges_handle:
a__ : Any =merges_handle.read().split("\n" )[1:-1]
a__ : str =[tuple(merge.split() ) for merge in bpe_merges]
a__ : int =dict(zip(_a , range(len(_a ) ) ) )
a__ : List[Any] ={}
a__ : Optional[int] =add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
a__ : Optional[Any] =re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
return len(self.encoder )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _lowercase ( self , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
a__ : Optional[int] =tuple(_a )
a__ : Any =get_pairs(_a )
if not pairs:
return token
while True:
a__ : Optional[Any] =min(_a , key=lambda lowerCAmelCase__ : self.bpe_ranks.get(_a , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
a__ : Dict =bigram
a__ : int =[]
a__ : Optional[Any] =0
while i < len(_a ):
try:
a__ : List[str] =word.index(_a , _a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
a__ : Tuple =j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
a__ : str =tuple(_a )
a__ : str =new_word
if len(_a ) == 1:
break
else:
a__ : int =get_pairs(_a )
a__ : Optional[int] =""" """.join(_a )
a__ : List[str] =word
return word
def _lowercase ( self , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
a__ : Any =[]
for token in re.findall(self.pat , _a ):
a__ : Any ="""""".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_a ).split(" " ) )
return bpe_tokens
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.encoder.get(_a , self.encoder.get(self.unk_token ) )
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
return self.decoder.get(_a )
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
a__ : Optional[int] ="""""".join(_a )
a__ : Dict =bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[str]:
'''simple docstring'''
if not os.path.isdir(_a ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a__ : int =os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
a__ : int =os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_a , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_a , ensure_ascii=_a ) + "\n" )
a__ : int =0
with open(_a , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCAmelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
" Please check that the tokenizer is not corrupted!" )
a__ : Dict =token_index
writer.write(" ".join(_a ) + "\n" )
index += 1
return vocab_file, merge_file
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Dict:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a__ : int =[self.cls_token_id]
a__ : int =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ) -> str:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> str:
'''simple docstring'''
a__ : Union[str, Any] =[self.sep_token_id]
a__ : Union[str, Any] =[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 + sep + token_ids_a + sep ) * [0]
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__=False , **lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
a__ : Any =kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_a ) > 0 and not text[0].isspace()):
a__ : Optional[int] =""" """ + text
return (text, kwargs)
| 563 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """retribert"""
def __init__( self : int ,_a : Dict=30522 ,_a : List[Any]=768 ,_a : Optional[Any]=8 ,_a : str=12 ,_a : str=3072 ,_a : Tuple="gelu" ,_a : Optional[int]=0.1 ,_a : Dict=0.1 ,_a : List[Any]=512 ,_a : Union[str, Any]=2 ,_a : Tuple=0.02 ,_a : List[str]=1e-12 ,_a : Dict=True ,_a : Tuple=128 ,_a : Optional[int]=0 ,**_a : Tuple ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,**_a )
A_ : Dict = vocab_size
A_ : int = hidden_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : int = intermediate_size
A_ : Tuple = hidden_dropout_prob
A_ : Optional[int] = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : Optional[int] = initializer_range
A_ : Dict = layer_norm_eps
A_ : str = share_encoders
A_ : List[Any] = projection_dim
| 665 | 0 |
'''simple docstring'''
def _UpperCamelCase ( lowerCAmelCase__: dict ) -> Any:
SCREAMING_SNAKE_CASE_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE_ = set()
return any(
node not in visited and depth_first_search(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
for node in graph )
def _UpperCamelCase ( lowerCAmelCase__: dict ,lowerCAmelCase__: int ,lowerCAmelCase__: set ,lowerCAmelCase__: set ) -> List[Any]:
visited.add(lowerCAmelCase__ )
rec_stk.add(lowerCAmelCase__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(lowerCAmelCase__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 294 |
'''simple docstring'''
import os
import re
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
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'vocab_file': 'spiece.model'}
__magic_name__ = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
__magic_name__ = {
'google/bigbird-roberta-base': 4_096,
'google/bigbird-roberta-large': 4_096,
'google/bigbird-base-trivia-itc': 4_096,
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
a_ = []
def __init__( self : Optional[int] ,_a : int ,_a : Optional[Any]="<unk>" ,_a : int="<s>" ,_a : str="</s>" ,_a : Optional[Any]="<pad>" ,_a : Tuple="[SEP]" ,_a : Tuple="[MASK]" ,_a : Union[str, Any]="[CLS]" ,_a : Optional[Dict[str, Any]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : Dict = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else bos_token
A_ : Union[str, Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else eos_token
A_ : Optional[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else unk_token
A_ : Union[str, Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else pad_token
A_ : Any = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else cls_token
A_ : Optional[int] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
A_ : List[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else mask_token
A_ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_a ,eos_token=_a ,unk_token=_a ,pad_token=_a ,sep_token=_a ,mask_token=_a ,cls_token=_a ,sp_model_kwargs=self.sp_model_kwargs ,**_a ,)
A_ : Optional[int] = vocab_file
A_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def _a ( self : Union[str, Any] ):
'''simple docstring'''
return self.sp_model.get_piece_size()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : Tuple = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[Any] ):
'''simple docstring'''
A_ : Union[str, Any] = self.__dict__.copy()
A_ : Union[str, Any] = None
return state
def __setstate__( self : List[Any] ,_a : Any ):
'''simple docstring'''
A_ : Tuple = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
A_ : Tuple = {}
A_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self : Union[str, Any] ,_a : str ):
'''simple docstring'''
return self.sp_model.encode(_a ,out_type=_a )
def _a ( self : Optional[int] ,_a : str ):
'''simple docstring'''
return self.sp_model.piece_to_id(_a )
def _a ( self : int ,_a : Optional[int] ):
'''simple docstring'''
A_ : List[str] = self.sp_model.IdToPiece(_a )
return token
def _a ( self : Dict ,_a : int ):
'''simple docstring'''
A_ : int = []
A_ : Any = """"""
A_ : str = 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(_a ) + token
A_ : Dict = True
A_ : Union[str, Any] = []
else:
current_sub_tokens.append(_a )
A_ : str = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def _a ( self : int ,_a : List[int] ,_a : bool = False ,_a : bool = None ,_a : bool = True ,**_a : str ,):
'''simple docstring'''
A_ : Any = kwargs.pop("""use_source_tokenizer""" ,_a )
A_ : Union[str, Any] = self.convert_ids_to_tokens(_a ,skip_special_tokens=_a )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
A_ : str = []
A_ : int = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_a ) )
A_ : List[str] = []
sub_texts.append(_a )
else:
current_sub_text.append(_a )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_a ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
A_ : Optional[int] = re.sub(r""" (\[(MASK|SEP)\])""" ,r"""\1""" ,""" """.join(_a ) )
else:
A_ : Tuple = """""".join(_a )
A_ : str = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
A_ : Optional[Any] = self.clean_up_tokenization(_a )
return clean_text
else:
return text
def _a ( self : int ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A_ : int = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_a )
elif not os.path.isfile(self.vocab_file ):
with open(_a ,"""wb""" ) as fi:
A_ : str = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
def _a ( self : Optional[Any] ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A_ : List[Any] = [self.cls_token_id]
A_ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def _a ( self : Optional[int] ,_a : List[int] ,_a : Optional[List[int]] = None ,_a : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a ,token_ids_a=_a ,already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
def _a ( self : Tuple ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : Tuple = [self.sep_token_id]
A_ : Optional[int] = [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]
| 665 | 0 |
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase : str =42
lowerCamelCase : int =42
def snake_case_ (__A : str ) -> Tuple:
if not isinstance(__A , __A ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__A ) )]
def snake_case_ (__A : str ) -> str:
if not isinstance(__A , __A ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
__lowerCAmelCase : Any = all_rotations(__A )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
__lowerCAmelCase : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__A ),
}
return response
def snake_case_ (__A : str , __A : int ) -> str:
if not isinstance(__A , __A ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
__lowerCAmelCase : List[Any] = int(__A )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__A ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
__lowerCAmelCase : Any = [""""""] * len(__A )
for _ in range(len(__A ) ):
for i in range(len(__A ) ):
__lowerCAmelCase : int = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
__UpperCAmelCase = """Provide a string that I will generate its BWT transform: """
__UpperCAmelCase = input(entry_msg).strip()
__UpperCAmelCase = bwt_transform(s)
print(
F'Burrows Wheeler transform for string \'{s}\' results '
F'in \'{result["bwt_string"]}\''
)
__UpperCAmelCase = reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
print(
F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
F'we get original string \'{original_string}\''
)
| 651 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
a_ = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
a_ = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def _a ( self : List[str] ,_a : int ,_a : Any ,_a : int ):
'''simple docstring'''
A_ : Dict = TextaTextGenerationPipeline(model=_a ,tokenizer=_a )
return generator, ["Something to write", "Something else"]
def _a ( self : str ,_a : Union[str, Any] ,_a : int ):
'''simple docstring'''
A_ : Any = generator("""Something there""" )
self.assertEqual(_a ,[{"""generated_text""": ANY(_a )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )
A_ : List[Any] = generator(["""This is great !""", """Something else"""] ,num_return_sequences=2 ,do_sample=_a )
self.assertEqual(
_a ,[
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
] ,)
A_ : List[str] = generator(
["""This is great !""", """Something else"""] ,num_return_sequences=2 ,batch_size=2 ,do_sample=_a )
self.assertEqual(
_a ,[
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
] ,)
with self.assertRaises(_a ):
generator(4 )
@require_torch
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : int = pipeline("""text2text-generation""" ,model="""patrickvonplaten/t5-tiny-random""" ,framework="""pt""" )
# do_sample=False necessary for reproducibility
A_ : Tuple = generator("""Something there""" ,do_sample=_a )
self.assertEqual(_a ,[{"""generated_text""": """"""}] )
A_ : Optional[int] = 3
A_ : Tuple = generator(
"""Something there""" ,num_return_sequences=_a ,num_beams=_a ,)
A_ : Optional[Any] = [
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """"""},
]
self.assertEqual(_a ,_a )
A_ : Optional[int] = generator("""This is a test""" ,do_sample=_a ,num_return_sequences=2 ,return_tensors=_a )
self.assertEqual(
_a ,[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
] ,)
A_ : Dict = generator.model.config.eos_token_id
A_ : Optional[int] = """<pad>"""
A_ : List[Any] = generator(
["""This is a test""", """This is a second test"""] ,do_sample=_a ,num_return_sequences=2 ,batch_size=2 ,return_tensors=_a ,)
self.assertEqual(
_a ,[
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
] ,)
@require_tf
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = pipeline("""text2text-generation""" ,model="""patrickvonplaten/t5-tiny-random""" ,framework="""tf""" )
# do_sample=False necessary for reproducibility
A_ : Dict = generator("""Something there""" ,do_sample=_a )
self.assertEqual(_a ,[{"""generated_text""": """"""}] )
| 665 | 0 |
from collections import defaultdict
def _a ( lowerCamelCase, lowerCamelCase ):
lowerCamelCase : List[Any] = first_str.lower().strip()
lowerCamelCase : Union[str, Any] = second_str.lower().strip()
# Remove whitespace
lowerCamelCase : Union[str, Any] = first_str.replace(""" """, """""" )
lowerCamelCase : Union[str, Any] = second_str.replace(""" """, """""" )
# Strings of different lengths are not anagrams
if len(lowerCamelCase ) != len(lowerCamelCase ):
return False
# Default values for count should be 0
lowerCamelCase : defaultdict[str, int] = defaultdict(lowerCamelCase )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowerCamelCase ) ):
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()
_lowerCamelCase =input("""Enter the first string """).strip()
_lowerCamelCase =input("""Enter the second string """).strip()
_lowerCamelCase =check_anagrams(input_a, input_b)
print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')
| 681 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """gpt_bigcode"""
a_ = ["""past_key_values"""]
a_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Optional[int] ,_a : Optional[int]=50257 ,_a : Dict=1024 ,_a : Union[str, Any]=768 ,_a : Union[str, Any]=12 ,_a : Union[str, Any]=12 ,_a : Tuple=None ,_a : int="gelu_pytorch_tanh" ,_a : Optional[Any]=0.1 ,_a : List[str]=0.1 ,_a : Union[str, Any]=0.1 ,_a : List[Any]=1e-5 ,_a : List[str]=0.02 ,_a : Any=True ,_a : Union[str, Any]=True ,_a : Tuple=50256 ,_a : Optional[int]=50256 ,_a : int=True ,_a : Optional[int]=True ,_a : Optional[int]=True ,**_a : List[str] ,):
'''simple docstring'''
A_ : Optional[Any] = vocab_size
A_ : int = n_positions
A_ : Union[str, Any] = n_embd
A_ : int = n_layer
A_ : Optional[int] = n_head
A_ : Union[str, Any] = n_inner
A_ : List[Any] = activation_function
A_ : Dict = resid_pdrop
A_ : int = embd_pdrop
A_ : Optional[int] = attn_pdrop
A_ : Union[str, Any] = layer_norm_epsilon
A_ : int = initializer_range
A_ : Union[str, Any] = scale_attn_weights
A_ : List[str] = use_cache
A_ : Tuple = attention_softmax_in_fpaa
A_ : List[str] = scale_attention_softmax_in_fpaa
A_ : Union[str, Any] = multi_query
A_ : Any = bos_token_id
A_ : Optional[int] = eos_token_id
super().__init__(bos_token_id=_a ,eos_token_id=_a ,**_a )
| 665 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class a_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = DistilBertTokenizer
UpperCamelCase = DistilBertTokenizerFast
UpperCamelCase = True
@slow
def snake_case_( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=_a )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_a )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(_a )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 314 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
__magic_name__ = {
'vocab_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
},
'merges_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
},
}
__magic_name__ = {
'allenai/longformer-base-4096': 4_096,
'allenai/longformer-large-4096': 4_096,
'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase ( ):
A_ : Union[str, Any] = (
list(range(ord("""!""") , ord("""~""") + 1)) + list(range(ord("""¡""") , ord("""¬""") + 1)) + list(range(ord("""®""") , ord("""ÿ""") + 1))
)
A_ : Optional[Any] = bs[:]
A_ : List[str] = 0
for b in range(2**8):
if b not in bs:
bs.append(lowerCamelCase)
cs.append(2**8 + n)
n += 1
A_ : List[Any] = [chr(lowerCamelCase) for n in cs]
return dict(zip(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int):
A_ : int = set()
A_ : int = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
A_ : List[str] = char
return pairs
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self : int ,_a : Tuple ,_a : Union[str, Any] ,_a : Optional[Any]="replace" ,_a : Union[str, Any]="<s>" ,_a : Union[str, Any]="</s>" ,_a : int="</s>" ,_a : List[str]="<s>" ,_a : List[Any]="<unk>" ,_a : Any="<pad>" ,_a : Dict="<mask>" ,_a : Optional[int]=False ,**_a : List[Any] ,):
'''simple docstring'''
A_ : Dict = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else bos_token
A_ : Optional[int] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else eos_token
A_ : Optional[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else sep_token
A_ : int = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else cls_token
A_ : int = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else unk_token
A_ : Optional[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
A_ : Any = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else mask_token
super().__init__(
errors=_a ,bos_token=_a ,eos_token=_a ,unk_token=_a ,sep_token=_a ,cls_token=_a ,pad_token=_a ,mask_token=_a ,add_prefix_space=_a ,**_a ,)
with open(_a ,encoding="""utf-8""" ) as vocab_handle:
A_ : str = json.load(_a )
A_ : Optional[int] = {v: k for k, v in self.encoder.items()}
A_ : List[str] = errors # how to handle errors in decoding
A_ : List[str] = bytes_to_unicode()
A_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(_a ,encoding="""utf-8""" ) as merges_handle:
A_ : Any = merges_handle.read().split("""\n""" )[1:-1]
A_ : str = [tuple(merge.split() ) for merge in bpe_merges]
A_ : int = dict(zip(_a ,range(len(_a ) ) ) )
A_ : List[Any] = {}
A_ : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
A_ : Optional[Any] = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
def _a ( self : Any ):
'''simple docstring'''
return len(self.encoder )
def _a ( self : str ):
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def _a ( self : int ,_a : int ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
A_ : Optional[int] = tuple(_a )
A_ : Any = get_pairs(_a )
if not pairs:
return token
while True:
A_ : Optional[Any] = min(_a ,key=lambda _a : self.bpe_ranks.get(_a ,float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
A_ , A_ : Dict = bigram
A_ : int = []
A_ : Optional[Any] = 0
while i < len(_a ):
try:
A_ : List[str] = word.index(_a ,_a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
A_ : Tuple = j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
A_ : str = tuple(_a )
A_ : str = new_word
if len(_a ) == 1:
break
else:
A_ : int = get_pairs(_a )
A_ : Optional[int] = """ """.join(_a )
A_ : List[str] = word
return word
def _a ( self : Dict ,_a : Optional[int] ):
'''simple docstring'''
A_ : Any = []
for token in re.findall(self.pat ,_a ):
A_ : Any = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_a ).split(""" """ ) )
return bpe_tokens
def _a ( self : Union[str, Any] ,_a : Optional[int] ):
'''simple docstring'''
return self.encoder.get(_a ,self.encoder.get(self.unk_token ) )
def _a ( self : int ,_a : Dict ):
'''simple docstring'''
return self.decoder.get(_a )
def _a ( self : Optional[int] ,_a : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = """""".join(_a )
A_ : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" ,errors=self.errors )
return text
def _a ( self : int ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A_ : int = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
A_ : int = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_a ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=_a ,ensure_ascii=_a ) + """\n""" )
A_ : int = 0
with open(_a ,"""w""" ,encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda _a : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
A_ : Dict = token_index
writer.write(""" """.join(_a ) + """\n""" )
index += 1
return vocab_file, merge_file
def _a ( self : List[str] ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A_ : int = [self.cls_token_id]
A_ : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self : int ,_a : List[int] ,_a : Optional[List[int]] = None ,_a : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a ,token_ids_a=_a ,already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def _a ( self : Any ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : Union[str, Any] = [self.sep_token_id]
A_ : Union[str, Any] = [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 + sep + token_ids_a + sep ) * [0]
def _a ( self : str ,_a : Optional[int] ,_a : Union[str, Any]=False ,**_a : Dict ):
'''simple docstring'''
A_ : Any = kwargs.pop("""add_prefix_space""" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_a ) > 0 and not text[0].isspace()):
A_ : Optional[int] = """ """ + text
return (text, kwargs)
| 665 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__a : str = TypeVar("""T""")
class _UpperCamelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
__lowercase = None
__lowercase = len(_a )
__lowercase = [any_type for _ in range(self.N )] + arr
__lowercase = fnc
self.build()
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
for p in range(self.N - 1 , 0 , -1 ):
__lowercase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
p += self.N
__lowercase = v
while p > 1:
__lowercase = p // 2
__lowercase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]: # noqa: E741
'''simple docstring'''
__lowercase = l + self.N, r + self.N
__lowercase = None
while l <= r:
if l % 2 == 1:
__lowercase = self.st[l] if res is None else self.fn(_a , self.st[l] )
if r % 2 == 0:
__lowercase = self.st[r] if res is None else self.fn(_a , self.st[r] )
__lowercase = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
__a : Optional[int] = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]
__a : List[Any] = {
0: 7,
1: 2,
2: 6,
3: -1_4,
4: 5,
5: 4,
6: 7,
7: -1_0,
8: 9,
9: 1_0,
1_0: 1_2,
1_1: 1,
}
__a : List[str] = SegmentTree(test_array, min)
__a : List[str] = SegmentTree(test_array, max)
__a : List[Any] = SegmentTree(test_array, lambda a, b: a + b)
def UpperCAmelCase ( ):
"""simple docstring"""
for i in range(len(lowercase ) ):
for j in range(lowercase , len(lowercase ) ):
__lowercase = reduce(lowercase , test_array[i : j + 1] )
__lowercase = reduce(lowercase , test_array[i : j + 1] )
__lowercase = reduce(lambda lowercase , lowercase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(lowercase , lowercase )
assert max_range == max_segment_tree.query(lowercase , lowercase )
assert sum_range == sum_segment_tree.query(lowercase , lowercase )
test_all_segments()
for index, value in test_updates.items():
__a : Optional[Any] = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments() | 534 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'vocab_file': 'vocab.txt'}
__magic_name__ = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
__magic_name__ = {
'YituTech/conv-bert-base': 512,
'YituTech/conv-bert-medium-small': 512,
'YituTech/conv-bert-small': 512,
}
__magic_name__ = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_INIT_CONFIGURATION
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ConvBertTokenizer
def __init__( self : str ,_a : Dict=None ,_a : List[Any]=None ,_a : Dict=True ,_a : List[str]="[UNK]" ,_a : Any="[SEP]" ,_a : str="[PAD]" ,_a : List[Any]="[CLS]" ,_a : List[str]="[MASK]" ,_a : Union[str, Any]=True ,_a : Any=None ,**_a : Optional[int] ,):
'''simple docstring'''
super().__init__(
_a ,tokenizer_file=_a ,do_lower_case=_a ,unk_token=_a ,sep_token=_a ,pad_token=_a ,cls_token=_a ,mask_token=_a ,tokenize_chinese_chars=_a ,strip_accents=_a ,**_a ,)
A_ : Optional[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,_a ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,_a ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,_a ) != tokenize_chinese_chars
):
A_ : Dict = getattr(_a ,normalizer_state.pop("""type""" ) )
A_ : str = do_lower_case
A_ : Any = strip_accents
A_ : int = tokenize_chinese_chars
A_ : Tuple = normalizer_class(**_a )
A_ : Any = do_lower_case
def _a ( self : List[Any] ,_a : List[Any] ,_a : Any=None ):
'''simple docstring'''
A_ : str = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _a ( self : Dict ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : int = [self.sep_token_id]
A_ : Any = [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 _a ( self : int ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
A_ : List[Any] = self._tokenizer.model.save(_a ,name=_a )
return tuple(_a )
| 665 | 0 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def lowerCAmelCase_ ( _lowercase : Union[str, Any]) -> Tuple:
"""simple docstring"""
if hor == 128:
a__ : List[Any] = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
a__ : List[Any] = (32, 128, 256)
a__ : Tuple = ("""UpResnetBlock1D""", """UpResnetBlock1D""")
elif hor == 32:
a__ : str = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
a__ : Optional[Any] = (32, 64, 128, 256)
a__ : Optional[int] = ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
a__ : Optional[int] = torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''')
a__ : Optional[Any] = model.state_dict()
a__ : Optional[int] = {
"""down_block_types""": down_block_types,
"""block_out_channels""": block_out_channels,
"""up_block_types""": up_block_types,
"""layers_per_block""": 1,
"""use_timestep_embedding""": True,
"""out_block_type""": """OutConv1DBlock""",
"""norm_num_groups""": 8,
"""downsample_each_block""": False,
"""in_channels""": 14,
"""out_channels""": 14,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""sample_size""": 6_5536,
"""mid_block_type""": """MidResTemporalBlock1D""",
"""act_fn""": """mish""",
}
a__ : List[str] = UNetaDModel(**_lowercase)
print(F'''length of state dict: {len(state_dict.keys())}''')
print(F'''length of value function dict: {len(hf_value_function.state_dict().keys())}''')
a__ : Dict = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys()))
for k, v in mapping.items():
a__ : Any = state_dict.pop(_lowercase)
hf_value_function.load_state_dict(_lowercase)
torch.save(hf_value_function.state_dict() , F'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''')
with open(F'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' , """w""") as f:
json.dump(_lowercase , _lowercase)
def lowerCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
a__ : Tuple = {
"""in_channels""": 14,
"""down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
"""up_block_types""": (),
"""out_block_type""": """ValueFunction""",
"""mid_block_type""": """ValueFunctionMidBlock1D""",
"""block_out_channels""": (32, 64, 128, 256),
"""layers_per_block""": 1,
"""downsample_each_block""": True,
"""sample_size""": 6_5536,
"""out_channels""": 14,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""use_timestep_embedding""": True,
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""norm_num_groups""": 8,
"""act_fn""": """mish""",
}
a__ : Tuple = torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""")
a__ : List[str] = model
a__ : Dict = UNetaDModel(**_lowercase)
print(F'''length of state dict: {len(state_dict.keys())}''')
print(F'''length of value function dict: {len(hf_value_function.state_dict().keys())}''')
a__ : List[str] = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys()))
for k, v in mapping.items():
a__ : int = state_dict.pop(_lowercase)
hf_value_function.load_state_dict(_lowercase)
torch.save(hf_value_function.state_dict() , """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin""")
with open("""hub/hopper-medium-v2/value_function/config.json""" , """w""") as f:
json.dump(_lowercase , _lowercase)
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 136 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all BART models at https://huggingface.co/models?filter=bart
__magic_name__ = {
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
'tokenizer_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
},
}
__magic_name__ = {
'facebook/bart-base': 1_024,
'facebook/bart-large': 1_024,
'facebook/bart-large-mnli': 1_024,
'facebook/bart-large-cnn': 1_024,
'facebook/bart-large-xsum': 1_024,
'yjernite/bart_eli5': 1_024,
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
a_ = BartTokenizer
def __init__( self : str ,_a : Any=None ,_a : Optional[int]=None ,_a : int=None ,_a : Optional[int]="replace" ,_a : Dict="<s>" ,_a : Optional[Any]="</s>" ,_a : Dict="</s>" ,_a : Tuple="<s>" ,_a : Optional[Any]="<unk>" ,_a : List[str]="<pad>" ,_a : int="<mask>" ,_a : str=False ,_a : List[str]=True ,**_a : Dict ,):
'''simple docstring'''
super().__init__(
_a ,_a ,tokenizer_file=_a ,errors=_a ,bos_token=_a ,eos_token=_a ,sep_token=_a ,cls_token=_a ,unk_token=_a ,pad_token=_a ,mask_token=_a ,add_prefix_space=_a ,trim_offsets=_a ,**_a ,)
A_ : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" ,_a ) != add_prefix_space:
A_ : List[str] = getattr(_a ,pre_tok_state.pop("""type""" ) )
A_ : Optional[int] = add_prefix_space
A_ : int = pre_tok_class(**_a )
A_ : str = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
A_ : str = """post_processor"""
A_ : List[Any] = getattr(self.backend_tokenizer ,_a ,_a )
if tokenizer_component_instance:
A_ : Tuple = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
A_ : Tuple = tuple(state["""sep"""] )
if "cls" in state:
A_ : Tuple = tuple(state["""cls"""] )
A_ : List[str] = False
if state.get("""add_prefix_space""" ,_a ) != add_prefix_space:
A_ : Dict = add_prefix_space
A_ : Any = True
if state.get("""trim_offsets""" ,_a ) != trim_offsets:
A_ : Union[str, Any] = trim_offsets
A_ : List[Any] = True
if changes_to_apply:
A_ : Optional[int] = getattr(_a ,state.pop("""type""" ) )
A_ : Tuple = component_class(**_a )
setattr(self.backend_tokenizer ,_a ,_a )
@property
def _a ( self : List[str] ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def _a ( self : Union[str, Any] ,_a : Any ):
'''simple docstring'''
A_ : int = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else value
A_ : List[Any] = value
def _a ( self : str ,*_a : str ,**_a : Optional[int] ):
'''simple docstring'''
A_ : Optional[Any] = kwargs.get("""is_split_into_words""" ,_a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*_a ,**_a )
def _a ( self : str ,*_a : List[Any] ,**_a : str ):
'''simple docstring'''
A_ : List[str] = kwargs.get("""is_split_into_words""" ,_a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*_a ,**_a )
def _a ( self : Optional[int] ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
A_ : str = self._tokenizer.model.save(_a ,name=_a )
return tuple(_a )
def _a ( self : str ,_a : Optional[int] ,_a : int=None ):
'''simple docstring'''
A_ : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _a ( self : Optional[int] ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : Dict = [self.sep_token_id]
A_ : Any = [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 + sep + token_ids_a + sep ) * [0]
| 665 | 0 |
'''simple docstring'''
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
def merge(_SCREAMING_SNAKE_CASE : list,_SCREAMING_SNAKE_CASE : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return collection
__A= len(_SCREAMING_SNAKE_CASE ) // 2
return merge(merge_sort(collection[:mid] ),merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase__ = input('''Enter numbers separated by a comma:\n''').strip()
UpperCAmelCase__ = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 186 |
'''simple docstring'''
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 lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : Tuple , lowerCamelCase : str):
# 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
A_ : int = TapasConfig.from_json_file(lowerCamelCase)
# set absolute/relative position embeddings parameter
A_ : List[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
A_ : Optional[int] = TapasForQuestionAnswering(config=lowerCamelCase)
elif task == "WTQ":
# run_task_main.py hparams
A_ : Tuple = 4
A_ : Optional[Any] = True
# hparam_utils.py hparams
A_ : Any = 0.66_4694
A_ : str = 0.20_7951
A_ : Any = 0.12_1194
A_ : str = True
A_ : Dict = True
A_ : int = False
A_ : int = 0.035_2513
A_ : Tuple = TapasForQuestionAnswering(config=lowerCamelCase)
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
A_ : int = 4
A_ : Union[str, Any] = False
# hparam_utils.py hparams
A_ : Dict = 36.4519
A_ : List[Any] = 0.90_3421
A_ : Any = 222.088
A_ : Optional[Any] = True
A_ : Optional[int] = True
A_ : Optional[Any] = True
A_ : Optional[int] = 0.76_3141
A_ : Any = TapasForQuestionAnswering(config=lowerCamelCase)
elif task == "TABFACT":
A_ : Any = TapasForSequenceClassification(config=lowerCamelCase)
elif task == "MLM":
A_ : List[Any] = TapasForMaskedLM(config=lowerCamelCase)
elif task == "INTERMEDIATE_PRETRAINING":
A_ : Union[str, Any] = TapasModel(config=lowerCamelCase)
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(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Save pytorch-model (weights and configuration)
print(F'Save PyTorch model to {pytorch_dump_path}')
model.save_pretrained(lowerCamelCase)
# Save tokenizer files
print(F'Save tokenizer files to {pytorch_dump_path}')
A_ : Optional[Any] = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=512)
tokenizer.save_pretrained(lowerCamelCase)
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell)
if __name__ == "__main__":
__magic_name__ = 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.'
)
__magic_name__ = 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,
)
| 665 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowercase__ =True
except (ImportError, ModuleNotFoundError):
lowercase__ =False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def UpperCamelCase_ ( A__ ):
re.sub("""<n>""" , """""" , A__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(A__ ) )
| 263 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""vqvae"""]
def __init__( self : Optional[Any] ,_a : AutoencoderKL ,_a : UNetaDConditionModel ,_a : Mel ,_a : Union[DDIMScheduler, DDPMScheduler] ,):
'''simple docstring'''
super().__init__()
self.register_modules(unet=_a ,scheduler=_a ,mel=_a ,vqvae=_a )
def _a ( self : str ):
'''simple docstring'''
return 50 if isinstance(self.scheduler ,_a ) else 1000
@torch.no_grad()
def __call__( self : Optional[int] ,_a : int = 1 ,_a : str = None ,_a : np.ndarray = None ,_a : int = 0 ,_a : int = 0 ,_a : int = None ,_a : torch.Generator = None ,_a : float = 0 ,_a : float = 0 ,_a : torch.Generator = None ,_a : float = 0 ,_a : torch.Tensor = None ,_a : torch.Tensor = None ,_a : int=True ,):
'''simple docstring'''
A_ : List[str] = steps or self.get_default_steps()
self.scheduler.set_timesteps(_a )
A_ : Union[str, Any] = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
A_ : Tuple = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
A_ : int = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) ,generator=_a ,device=self.device ,)
A_ : List[Any] = noise
A_ : str = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(_a ,_a )
A_ : Any = self.mel.audio_slice_to_image(_a )
A_ : Union[str, Any] = np.frombuffer(input_image.tobytes() ,dtype="""uint8""" ).reshape(
(input_image.height, input_image.width) )
A_ : Optional[Any] = (input_image / 255) * 2 - 1
A_ : Union[str, Any] = torch.tensor(input_image[np.newaxis, :, :] ,dtype=torch.float ).to(self.device )
if self.vqvae is not None:
A_ : Union[str, Any] = self.vqvae.encode(torch.unsqueeze(_a ,0 ) ).latent_dist.sample(
generator=_a )[0]
A_ : List[str] = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
A_ : Any = self.scheduler.add_noise(_a ,_a ,self.scheduler.timesteps[start_step - 1] )
A_ : Tuple = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
A_ : Tuple = int(mask_start_secs * pixels_per_second )
A_ : str = int(mask_end_secs * pixels_per_second )
A_ : int = self.scheduler.add_noise(_a ,_a ,torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet ,_a ):
A_ : Optional[Any] = self.unet(_a ,_a ,_a )["""sample"""]
else:
A_ : List[Any] = self.unet(_a ,_a )["""sample"""]
if isinstance(self.scheduler ,_a ):
A_ : Dict = self.scheduler.step(
model_output=_a ,timestep=_a ,sample=_a ,eta=_a ,generator=_a ,)["""prev_sample"""]
else:
A_ : Any = self.scheduler.step(
model_output=_a ,timestep=_a ,sample=_a ,generator=_a ,)["""prev_sample"""]
if mask is not None:
if mask_start > 0:
A_ : Tuple = mask[:, step, :, :mask_start]
if mask_end > 0:
A_ : List[str] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
A_ : str = 1 / self.vqvae.config.scaling_factor * images
A_ : Union[str, Any] = self.vqvae.decode(_a )["""sample"""]
A_ : int = (images / 2 + 0.5).clamp(0 ,1 )
A_ : str = images.cpu().permute(0 ,2 ,3 ,1 ).numpy()
A_ : Optional[int] = (images * 255).round().astype("""uint8""" )
A_ : List[Any] = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(_a ,mode="""RGB""" ).convert("""L""" ) for _ in images) )
A_ : Tuple = [self.mel.image_to_audio(_a ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(_a )[:, np.newaxis, :] ) ,**ImagePipelineOutput(_a ) )
@torch.no_grad()
def _a ( self : Union[str, Any] ,_a : List[Image.Image] ,_a : int = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler ,_a )
self.scheduler.set_timesteps(_a )
A_ : Optional[Any] = np.array(
[np.frombuffer(image.tobytes() ,dtype="""uint8""" ).reshape((1, image.height, image.width) ) for image in images] )
A_ : List[str] = (sample / 255) * 2 - 1
A_ : Optional[int] = torch.Tensor(_a ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps ,(0,) ) ):
A_ : List[str] = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
A_ : Any = self.scheduler.alphas_cumprod[t]
A_ : List[Any] = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
A_ : str = 1 - alpha_prod_t
A_ : List[str] = self.unet(_a ,_a )["""sample"""]
A_ : str = (1 - alpha_prod_t_prev) ** 0.5 * model_output
A_ : Union[str, Any] = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
A_ : Optional[int] = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def _a ( _a : torch.Tensor ,_a : torch.Tensor ,_a : float ):
'''simple docstring'''
A_ : List[Any] = acos(torch.dot(torch.flatten(_a ) ,torch.flatten(_a ) ) / torch.norm(_a ) / torch.norm(_a ) )
return sin((1 - alpha) * theta ) * xa / sin(_a ) + sin(alpha * theta ) * xa / sin(_a )
| 665 | 0 |
from jiwer import compute_measures
import datasets
_lowercase = """\\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"""
_lowercase = """\\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"""
_lowercase = """\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 lowercase_ ( datasets.Metric ):
def _snake_case ( self ) -> Tuple:
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 _snake_case ( self , __A=None , __A=None , __A=False ) -> List[Any]:
if concatenate_texts:
return compute_measures(_a , _a )["wer"]
else:
SCREAMING_SNAKE_CASE_ : Tuple =0
SCREAMING_SNAKE_CASE_ : str =0
for prediction, reference in zip(_a , _a ):
SCREAMING_SNAKE_CASE_ : Dict =compute_measures(_a , _a )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 443 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__magic_name__ = 16
__magic_name__ = 32
def lowerCamelCase ( lowerCamelCase : Accelerator , lowerCamelCase : int = 16):
A_ : Any = AutoTokenizer.from_pretrained("""bert-base-cased""")
A_ : str = load_dataset("""glue""" , """mrpc""")
def tokenize_function(lowerCamelCase : Dict):
# max_length=None => use the model max length (it's actually the default)
A_ : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A_ : Tuple = datasets.map(
lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ : List[str] = tokenized_datasets.rename_column("""label""" , """labels""")
def collate_fn(lowerCamelCase : Tuple):
# On TPU it's best to pad everything to the same length or training will be very slow.
A_ : str = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A_ : List[Any] = 16
elif accelerator.mixed_precision != "no":
A_ : Any = 8
else:
A_ : Tuple = None
return tokenizer.pad(
lowerCamelCase , padding="""longest""" , max_length=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
A_ : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase , drop_last=lowerCamelCase)
A_ : str = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase , drop_last=(accelerator.mixed_precision == """fp8""") , )
return train_dataloader, eval_dataloader
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Dict):
# Initialize accelerator
A_ : Tuple = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision)
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ : List[Any] = config["""lr"""]
A_ : List[Any] = int(config["""num_epochs"""])
A_ : int = int(config["""seed"""])
A_ : Dict = int(config["""batch_size"""])
A_ : Union[str, Any] = evaluate.load("""glue""" , """mrpc""")
# If the batch size is too big we use gradient accumulation
A_ : int = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A_ : Any = batch_size // MAX_GPU_BATCH_SIZE
A_ : Union[str, Any] = MAX_GPU_BATCH_SIZE
set_seed(lowerCamelCase)
A_ , A_ : List[str] = get_dataloaders(lowerCamelCase , lowerCamelCase)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCamelCase)
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A_ : str = model.to(accelerator.device)
# Instantiate optimizer
A_ : str = AdamW(params=model.parameters() , lr=lowerCamelCase)
# Instantiate scheduler
A_ : Tuple = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(lowerCamelCase) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_ , A_ , A_ , A_ , A_ : Union[str, Any] = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Now we train the model
for epoch in range(lowerCamelCase):
model.train()
for step, batch in enumerate(lowerCamelCase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
A_ : Optional[int] = model(**lowerCamelCase)
A_ : List[Any] = outputs.loss
A_ : Tuple = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase)
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
with torch.no_grad():
A_ : Union[str, Any] = model(**lowerCamelCase)
A_ : Any = outputs.logits.argmax(dim=-1)
A_ , A_ : Tuple = accelerator.gather_for_metrics((predictions, batch["""labels"""]))
metric.add_batch(
predictions=lowerCamelCase , references=lowerCamelCase , )
A_ : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , lowerCamelCase)
def lowerCamelCase ( ):
A_ : Optional[int] = argparse.ArgumentParser(description="""Simple example of training script.""")
parser.add_argument(
"""--mixed_precision""" , type=lowerCamelCase , default=lowerCamelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""")
A_ : Dict = parser.parse_args()
A_ : Dict = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase , lowerCamelCase)
if __name__ == "__main__":
main()
| 665 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : Optional[int] = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ConditionalDetrConfig""",
"""ConditionalDetrOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = ["""ConditionalDetrFeatureExtractor"""]
a : int = ["""ConditionalDetrImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : int = [
"""CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConditionalDetrForObjectDetection""",
"""ConditionalDetrForSegmentation""",
"""ConditionalDetrModel""",
"""ConditionalDetrPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
a : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 613 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
raise ValueError("""The parameter days should be a list of integers""")
if len(lowerCamelCase) != 3 or not all(isinstance(lowerCamelCase , lowerCamelCase) for cost in costs):
raise ValueError("""The parameter costs should be a list of three integers""")
if len(lowerCamelCase) == 0:
return 0
if min(lowerCamelCase) <= 0:
raise ValueError("""All days elements should be greater than 0""")
if max(lowerCamelCase) >= 366:
raise ValueError("""All days elements should be less than 366""")
A_ : Tuple = set(lowerCamelCase)
@functools.cache
def dynamic_programming(lowerCamelCase : int) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1)
return min(
costs[0] + dynamic_programming(index + 1) , costs[1] + dynamic_programming(index + 7) , costs[2] + dynamic_programming(index + 30) , )
return dynamic_programming(1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 563 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : int , ):
A_ , A_ : int = coefficient_matrix.shape
A_ , A_ : Union[str, Any] = constant_matrix.shape
if rowsa != colsa:
A_ : Any = F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
raise ValueError(lowerCamelCase)
if colsa != 1:
A_ : Tuple = F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
raise ValueError(lowerCamelCase)
if rowsa != rowsa:
A_ : Dict = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
)
raise ValueError(lowerCamelCase)
if len(lowerCamelCase) != rowsa:
A_ : Union[str, Any] = (
"""Number of initial values must be equal to number of rows in coefficient """
F'matrix but received {len(lowerCamelCase)} and {rowsa}'
)
raise ValueError(lowerCamelCase)
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""")
A_ : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1)
A_ , A_ : int = table.shape
strictly_diagonally_dominant(lowerCamelCase)
# Iterates the whole matrix for given number of times
for _ in range(lowerCamelCase):
A_ : List[Any] = []
for row in range(lowerCamelCase):
A_ : int = 0
for col in range(lowerCamelCase):
if col == row:
A_ : List[str] = table[row][col]
elif col == cols - 1:
A_ : str = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
A_ : Union[str, Any] = (temp + val) / denom
new_val.append(lowerCamelCase)
A_ : Tuple = new_val
return [float(lowerCamelCase) for i in new_val]
def lowerCamelCase ( lowerCamelCase : NDArray[floataa]):
A_ , A_ : Dict = table.shape
A_ : Union[str, Any] = True
for i in range(0 , lowerCamelCase):
A_ : str = 0
for j in range(0 , cols - 1):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""")
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
SCREAMING_SNAKE_CASE : Dict = [
"python",
"tqdm",
"regex",
"requests",
"packaging",
"filelock",
"numpy",
"tokenizers",
"huggingface-hub",
"safetensors",
"accelerate",
"pyyaml",
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def _UpperCamelCase ( lowerCAmelCase__: Optional[int] ,lowerCAmelCase__: Tuple=None ) -> Union[str, Any]:
require_version(deps[pkg] ,lowerCAmelCase__ )
| 294 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)]
A_ : Union[str, Any] = True
for i in range(lowerCamelCase):
for j in range(m + 1):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
A_ : Optional[int] = True
if a[i].islower():
A_ : List[Any] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[int] =KandinskyInpaintPipeline
lowerCamelCase : Optional[Any] =["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
lowerCamelCase : List[Any] =[
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
lowerCamelCase : str =[
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowerCamelCase : Optional[Any] =False
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> str:
"""simple docstring"""
return 32
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[Any]:
"""simple docstring"""
return 32
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
"""simple docstring"""
return self.time_input_dim
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
"""simple docstring"""
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return 1_00
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> int:
"""simple docstring"""
__lowerCAmelCase : List[str] = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase : Optional[int] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
__lowerCAmelCase : List[Any] = MultilingualCLIP(_a )
__lowerCAmelCase : Any = text_encoder.eval()
return text_encoder
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase : Optional[int] = {
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
__lowerCAmelCase : Union[str, Any] = UNetaDConditionModel(**_a )
return model
@property
def SCREAMING_SNAKE_CASE ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
__lowerCAmelCase : Dict = VQModel(**self.dummy_movq_kwargs )
return model
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Any = self.dummy_text_encoder
__lowerCAmelCase : Tuple = self.dummy_tokenizer
__lowerCAmelCase : List[Any] = self.dummy_unet
__lowerCAmelCase : Any = self.dummy_movq
__lowerCAmelCase : Any = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type="""epsilon""" , thresholding=_a , )
__lowerCAmelCase : List[Any] = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : str=0 ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Any = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
__lowerCAmelCase : Optional[int] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
__lowerCAmelCase : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(_a ) ).to(_a )
__lowerCAmelCase : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase : str = Image.fromarray(np.uinta(_a ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create mask
__lowerCAmelCase : Any = np.ones((64, 64) , dtype=np.floataa )
__lowerCAmelCase : List[str] = 0
if str(_a ).startswith("""mps""" ):
__lowerCAmelCase : Union[str, Any] = torch.manual_seed(_a )
else:
__lowerCAmelCase : int = torch.Generator(device=_a ).manual_seed(_a )
__lowerCAmelCase : List[str] = {
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Any = """cpu"""
__lowerCAmelCase : Optional[int] = self.get_dummy_components()
__lowerCAmelCase : Tuple = self.pipeline_class(**_a )
__lowerCAmelCase : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
__lowerCAmelCase : Optional[Any] = pipe(**self.get_dummy_inputs(_a ) )
__lowerCAmelCase : int = output.images
__lowerCAmelCase : Optional[int] = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
__lowerCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
__lowerCAmelCase : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
print(f'''image.shape {image.shape}''' )
assert image.shape == (1, 64, 64, 3)
__lowerCAmelCase : Tuple = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowerCAmelCase : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
__lowerCAmelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__lowerCAmelCase : Optional[Any] = np.ones((7_68, 7_68) , dtype=np.floataa )
__lowerCAmelCase : Any = 0
__lowerCAmelCase : Tuple = """a hat"""
__lowerCAmelCase : str = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(_a )
__lowerCAmelCase : Dict = KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa )
__lowerCAmelCase : Optional[Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
__lowerCAmelCase : List[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase : str = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
__lowerCAmelCase : str = pipeline(
_a , image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , )
__lowerCAmelCase : Optional[int] = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(_a , _a )
| 651 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] ,_a : int ):
'''simple docstring'''
A_ : list[list[Edge]] = [[] for _ in range(_a )]
A_ : List[Any] = size
def __getitem__( self : int ,_a : int ):
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def _a ( self : str ):
'''simple docstring'''
return self._size
def _a ( self : str ,_a : int ,_a : int ,_a : int ):
'''simple docstring'''
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(_a ,_a ) )
def _a ( self : Dict ,_a : int ,_a : int ):
'''simple docstring'''
A_ : Tuple = deque([start_vertex] )
A_ : list[int | None] = [None] * self.size
A_ : Union[str, Any] = 0
while queue:
A_ : List[Any] = queue.popleft()
A_ : Tuple = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
A_ : Union[str, Any] = current_distance + edge.weight
A_ : Optional[Any] = distances[edge.destination_vertex]
if (
isinstance(_a ,_a )
and new_distance >= dest_vertex_distance
):
continue
A_ : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_lowerCamelCase ={
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
"""convert""": ["""export""", """validate_model_outputs"""],
"""features""": ["""FeaturesManager"""],
"""utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 681 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
A_ : Optional[Any] = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""")
| 665 | 0 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"""vocab_file""": """vocab.txt"""}
lowercase_ = {
"""vocab_file""": {
"""openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
},
}
lowercase_ = {
"""openbmb/cpm-ant-10b""": 1_024,
}
def lowerCamelCase ( __lowerCamelCase : Any ) ->List[Any]:
_SCREAMING_SNAKE_CASE = collections.OrderedDict()
with open(__lowerCamelCase , """r""" , encoding="""utf-8""" ) as reader:
_SCREAMING_SNAKE_CASE = reader.readlines()
for index, token in enumerate(__lowerCamelCase ):
_SCREAMING_SNAKE_CASE = token.rstrip("""\n""" )
_SCREAMING_SNAKE_CASE = index
return vocab
class a_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , A , A="<unk>" , A=200 ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = vocab
_SCREAMING_SNAKE_CASE = unk_token
_SCREAMING_SNAKE_CASE = max_input_chars_per_word
def snake_case_( self , A ) -> int:
_SCREAMING_SNAKE_CASE = list(_a )
if len(_a ) > self.max_input_chars_per_word:
return [self.unk_token]
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = []
while start < len(_a ):
_SCREAMING_SNAKE_CASE = len(_a )
_SCREAMING_SNAKE_CASE = None
while start < end:
_SCREAMING_SNAKE_CASE = """""".join(chars[start:end] )
if substr in self.vocab:
_SCREAMING_SNAKE_CASE = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(_a )
_SCREAMING_SNAKE_CASE = end
return sub_tokens
class a_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ['''input_ids''', '''attention_mask''']
UpperCamelCase = False
def __init__( self , A , A="<d>" , A="</d>" , A="<s>" , A="</s>" , A="<pad>" , A="<unk>" , A="</n>" , A="</_>" , A="left" , **A , ) -> Optional[int]:
requires_backends(self , ["""jieba"""] )
super().__init__(
bod_token=_a , eod_token=_a , bos_token=_a , eos_token=_a , pad_token=_a , unk_token=_a , line_token=_a , space_token=_a , padding_side=_a , **_a , )
_SCREAMING_SNAKE_CASE = bod_token
_SCREAMING_SNAKE_CASE = eod_token
_SCREAMING_SNAKE_CASE = load_vocab(_a )
_SCREAMING_SNAKE_CASE = self.encoder[space_token]
_SCREAMING_SNAKE_CASE = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
_SCREAMING_SNAKE_CASE = collections.OrderedDict(sorted(self.encoder.items() , key=lambda A : x[1] ) )
_SCREAMING_SNAKE_CASE = {v: k for k, v in self.encoder.items()}
_SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def snake_case_( self ) -> List[Any]:
return self.encoder[self.bod_token]
@property
def snake_case_( self ) -> List[Any]:
return self.encoder[self.eod_token]
@property
def snake_case_( self ) -> int:
return self.encoder["\n"]
@property
def snake_case_( self ) -> int:
return len(self.encoder )
def snake_case_( self ) -> Optional[Any]:
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case_( self , A ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = []
for x in jieba.cut(_a , cut_all=_a ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(_a ) )
return output_tokens
def snake_case_( self , A , **A ) -> Tuple:
_SCREAMING_SNAKE_CASE = [i for i in token_ids if i >= 0]
_SCREAMING_SNAKE_CASE = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(_a , **_a )
def snake_case_( self , A ) -> List[str]:
return token in self.encoder
def snake_case_( self , A ) -> Optional[Any]:
return "".join(_a )
def snake_case_( self , A ) -> Any:
return self.encoder.get(_a , self.encoder.get(self.unk_token ) )
def snake_case_( self , A ) -> int:
return self.decoder.get(_a , self.unk_token )
def snake_case_( self , A , A = None ) -> List[str]:
if os.path.isdir(_a ):
_SCREAMING_SNAKE_CASE = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
else:
_SCREAMING_SNAKE_CASE = (filename_prefix + """-""" if filename_prefix else """""") + save_directory
_SCREAMING_SNAKE_CASE = 0
if " " in self.encoder:
_SCREAMING_SNAKE_CASE = self.encoder[""" """]
del self.encoder[" "]
if "\n" in self.encoder:
_SCREAMING_SNAKE_CASE = self.encoder["""\n"""]
del self.encoder["\n"]
_SCREAMING_SNAKE_CASE = collections.OrderedDict(sorted(self.encoder.items() , key=lambda A : x[1] ) )
with open(_a , """w""" , encoding="""utf-8""" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
""" Please check that the vocabulary is not corrupted!""" )
_SCREAMING_SNAKE_CASE = token_index
writer.write(token + """\n""" )
index += 1
return (vocab_file,)
def snake_case_( self , A , A = None ) -> Optional[int]:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def snake_case_( self , A , A = None , A = False ) -> Optional[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a ))
return [1] + ([0] * len(_a ))
| 314 |
'''simple docstring'''
# Copyright 2021 The HuggingFace 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 argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def lowerCamelCase ( ):
A_ : Optional[int] = ArgumentParser("""Accelerate CLI tool""" , usage="""accelerate <command> [<args>]""" , allow_abbrev=lowerCamelCase)
A_ : Optional[int] = parser.add_subparsers(help="""accelerate command helpers""")
# Register commands
get_config_parser(subparsers=lowerCamelCase)
env_command_parser(subparsers=lowerCamelCase)
launch_command_parser(subparsers=lowerCamelCase)
tpu_command_parser(subparsers=lowerCamelCase)
test_command_parser(subparsers=lowerCamelCase)
# Let's go
A_ : Dict = parser.parse_args()
if not hasattr(lowerCamelCase , """func"""):
parser.print_help()
exit(1)
# Run
args.func(lowerCamelCase)
if __name__ == "__main__":
main()
| 665 | 0 |
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 UpperCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
__lowercase = TapasConfig.from_json_file(lowercase )
# set absolute/relative position embeddings parameter
__lowercase = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__lowercase = TapasForQuestionAnswering(config=lowercase )
elif task == "WTQ":
# run_task_main.py hparams
__lowercase = 4
__lowercase = True
# hparam_utils.py hparams
__lowercase = 0.664694
__lowercase = 0.207951
__lowercase = 0.121194
__lowercase = True
__lowercase = True
__lowercase = False
__lowercase = 0.0352513
__lowercase = TapasForQuestionAnswering(config=lowercase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__lowercase = 4
__lowercase = False
# hparam_utils.py hparams
__lowercase = 36.4519
__lowercase = 0.903421
__lowercase = 222.088
__lowercase = True
__lowercase = True
__lowercase = True
__lowercase = 0.763141
__lowercase = TapasForQuestionAnswering(config=lowercase )
elif task == "TABFACT":
__lowercase = TapasForSequenceClassification(config=lowercase )
elif task == "MLM":
__lowercase = TapasForMaskedLM(config=lowercase )
elif task == "INTERMEDIATE_PRETRAINING":
__lowercase = 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}" )
__lowercase = 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 : Optional[Any] = 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 : Dict = 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,
) | 534 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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 (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=1_3 , __lowercase=7 , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=9_9 , __lowercase=6_4 , __lowercase=3_2 , __lowercase=5 , __lowercase=4 , __lowercase=3_7 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=5_1_2 , __lowercase=1_6 , __lowercase=2 , __lowercase=0.0_2 , __lowercase=3 , __lowercase=4 , __lowercase=None , ) -> Optional[int]:
"""simple docstring"""
a__ : Dict = parent
a__ : Optional[Any] = batch_size
a__ : Dict = seq_length
a__ : List[str] = is_training
a__ : Optional[int] = use_input_mask
a__ : Dict = use_token_type_ids
a__ : Optional[Any] = use_labels
a__ : Dict = vocab_size
a__ : List[Any] = hidden_size
a__ : Union[str, Any] = embedding_size
a__ : List[Any] = num_hidden_layers
a__ : List[Any] = num_attention_heads
a__ : Union[str, Any] = intermediate_size
a__ : Union[str, Any] = hidden_act
a__ : int = hidden_dropout_prob
a__ : Union[str, Any] = attention_probs_dropout_prob
a__ : Tuple = max_position_embeddings
a__ : List[str] = type_vocab_size
a__ : List[Any] = type_sequence_label_size
a__ : List[str] = initializer_range
a__ : Any = num_labels
a__ : Dict = num_choices
a__ : Union[str, Any] = scope
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
a__ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Optional[int] = None
if self.use_input_mask:
a__ : str = random_attention_mask([self.batch_size, self.seq_length] )
a__ : List[str] = None
if self.use_token_type_ids:
a__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ : Any = None
a__ : List[Any] = None
a__ : int = None
if self.use_labels:
a__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
a__ : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
return MegatronBertConfig(
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 , embedding_size=self.embedding_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=_a , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
"""simple docstring"""
a__ : str = MegatronBertModel(config=_a )
model.to(_a )
model.eval()
a__ : List[str] = model(_a , attention_mask=_a , token_type_ids=_a )
a__ : List[Any] = model(_a , token_type_ids=_a )
a__ : Union[str, Any] = model(_a )
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 SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
"""simple docstring"""
a__ : Optional[Any] = MegatronBertForMaskedLM(config=_a )
model.to(_a )
model.eval()
a__ : List[str] = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Tuple:
"""simple docstring"""
a__ : Optional[Any] = MegatronBertForCausalLM(config=_a )
model.to(_a )
model.eval()
a__ : Dict = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
"""simple docstring"""
a__ : List[Any] = MegatronBertForNextSentencePrediction(config=_a )
model.to(_a )
model.eval()
a__ : Tuple = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Dict:
"""simple docstring"""
a__ : List[Any] = MegatronBertForPreTraining(config=_a )
model.to(_a )
model.eval()
a__ : List[str] = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , next_sentence_label=_a , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> List[str]:
"""simple docstring"""
a__ : Optional[Any] = MegatronBertForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
a__ : str = model(
_a , attention_mask=_a , token_type_ids=_a , start_positions=_a , end_positions=_a , )
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 SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[int]:
"""simple docstring"""
a__ : int = self.num_labels
a__ : List[Any] = MegatronBertForSequenceClassification(_a )
model.to(_a )
model.eval()
a__ : str = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Tuple:
"""simple docstring"""
a__ : Any = self.num_labels
a__ : List[Any] = MegatronBertForTokenClassification(config=_a )
model.to(_a )
model.eval()
a__ : Optional[int] = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Dict:
"""simple docstring"""
a__ : Optional[int] = self.num_choices
a__ : Optional[Any] = MegatronBertForMultipleChoice(config=_a )
model.to(_a )
model.eval()
a__ : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : Optional[Any] = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : Dict = self.prepare_config_and_inputs()
(
a__
) : List[Any] = config_and_inputs
a__ : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class snake_case__ (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :Dict = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
__lowerCAmelCase :List[Any] = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCAmelCase :int = True
# test_resize_embeddings = False
__lowerCAmelCase :Tuple = False
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase=False ) -> int:
"""simple docstring"""
a__ : str = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class in get_values(_a ):
a__ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_a )
a__ : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
return inputs_dict
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
a__ : str = MegatronBertModelTester(self )
a__ : Union[str, Any] = ConfigTester(self , config_class=_a , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*_a )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*_a )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*_a )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*_a )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
a__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*_a )
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*_a )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*_a )
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
a__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*_a )
def lowerCAmelCase_ ( _lowercase : Tuple) -> List[Any]:
"""simple docstring"""
return torch.tensor(
_lowercase , dtype=torch.long , device=_lowercase , )
_lowercase : int =1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip("""Model is not available.""" )
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Dict = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
a__ : int = os.path.join(os.environ["""MYDIR"""] , _a )
a__ : List[str] = MegatronBertModel.from_pretrained(_a )
model.to(_a )
model.half()
a__ : Any = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
with torch.no_grad():
a__ : Optional[Any] = model(_a )[0]
a__ : List[Any] = torch.Size((1, 9, 1_0_2_4) )
self.assertEqual(output.shape , _a )
a__ : Dict = [-0.6_0_4_0, -0.2_5_1_7, -0.1_0_2_5, 0.3_4_2_0, -0.6_7_5_8, -0.0_0_1_7, -0.1_0_8_9, -0.1_9_9_0, 0.5_7_2_8]
for ii in range(3 ):
for jj in range(3 ):
a__ : List[Any] = output[0, ii, jj]
a__ : Optional[Any] = expected[3 * ii + jj]
a__ : Any = """ii={} jj={} a={} b={}""".format(_a , _a , _a , _a )
self.assertTrue(math.isclose(_a , _a , rel_tol=_a , abs_tol=_a ) , msg=_a )
| 136 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['YolosFeatureExtractor']
__magic_name__ = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
'YolosForObjectDetection',
'YolosModel',
'YolosPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class a__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Any = '''wavlm'''
def __init__( self : List[Any] , lowerCAmelCase_ : Dict=32 , lowerCAmelCase_ : List[Any]=768 , lowerCAmelCase_ : Tuple=12 , lowerCAmelCase_ : Union[str, Any]=12 , lowerCAmelCase_ : Dict=3_072 , lowerCAmelCase_ : int="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : int=0.1 , lowerCAmelCase_ : Optional[int]=0.0 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : Union[str, Any]=0.1 , lowerCAmelCase_ : Tuple=0.02 , lowerCAmelCase_ : Union[str, Any]=1E-5 , lowerCAmelCase_ : Any="group" , lowerCAmelCase_ : Optional[int]="gelu" , lowerCAmelCase_ : Dict=(512, 512, 512, 512, 512, 512, 512) , lowerCAmelCase_ : str=(5, 2, 2, 2, 2, 2, 2) , lowerCAmelCase_ : List[str]=(10, 3, 3, 3, 3, 2, 2) , lowerCAmelCase_ : Optional[int]=False , lowerCAmelCase_ : Union[str, Any]=128 , lowerCAmelCase_ : List[str]=16 , lowerCAmelCase_ : List[str]=320 , lowerCAmelCase_ : Tuple=800 , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[int]=0.05 , lowerCAmelCase_ : Union[str, Any]=10 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : List[Any]=0.0 , lowerCAmelCase_ : int=10 , lowerCAmelCase_ : Optional[int]=320 , lowerCAmelCase_ : int=2 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : Dict=100 , lowerCAmelCase_ : List[Any]=256 , lowerCAmelCase_ : List[str]=256 , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : Any="mean" , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : int=256 , lowerCAmelCase_ : Union[str, Any]=(512, 512, 512, 512, 1_500) , lowerCAmelCase_ : Optional[Any]=(5, 3, 3, 1, 1) , lowerCAmelCase_ : Union[str, Any]=(1, 2, 3, 1, 1) , lowerCAmelCase_ : str=512 , lowerCAmelCase_ : int=80 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : Any=2 , lowerCAmelCase_ : int=False , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : Any=3 , lowerCAmelCase_ : Tuple=None , **lowerCAmelCase_ : Any , ) -> Optional[int]:
super().__init__(**_a , pad_token_id=_a , bos_token_id=_a , eos_token_id=_a )
__A= hidden_size
__A= feat_extract_norm
__A= feat_extract_activation
__A= list(_a )
__A= list(_a )
__A= list(_a )
__A= conv_bias
__A= num_buckets
__A= max_bucket_distance
__A= num_conv_pos_embeddings
__A= num_conv_pos_embedding_groups
__A= len(self.conv_dim )
__A= num_hidden_layers
__A= intermediate_size
__A= hidden_act
__A= num_attention_heads
__A= hidden_dropout
__A= attention_dropout
__A= activation_dropout
__A= feat_proj_dropout
__A= final_dropout
__A= layerdrop
__A= layer_norm_eps
__A= initializer_range
__A= num_ctc_classes
__A= vocab_size
__A= do_stable_layer_norm
__A= use_weighted_layer_sum
__A= classifier_proj_size
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
__A= apply_spec_augment
__A= mask_time_prob
__A= mask_time_length
__A= mask_time_min_masks
__A= mask_feature_prob
__A= mask_feature_length
# parameters for pretraining with codevector quantized representations
__A= num_codevectors_per_group
__A= num_codevector_groups
__A= contrastive_logits_temperature
__A= num_negatives
__A= codevector_dim
__A= proj_codevector_dim
__A= diversity_loss_weight
# ctc loss
__A= ctc_loss_reduction
__A= ctc_zero_infinity
# adapter
__A= add_adapter
__A= adapter_kernel_size
__A= adapter_stride
__A= num_adapter_layers
__A= output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__A= classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__A= list(_a )
__A= list(_a )
__A= list(_a )
__A= xvector_output_dim
@property
def lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 186 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a_ :
def __init__( self , UpperCAmelCase , UpperCAmelCase=3 , UpperCAmelCase=32 , UpperCAmelCase=3 , UpperCAmelCase=10 , UpperCAmelCase=[10, 20, 30, 40] , UpperCAmelCase=[1, 1, 2, 1] , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase="relu" , UpperCAmelCase=3 , UpperCAmelCase=None , ):
a_ = parent
a_ = batch_size
a_ = image_size
a_ = num_channels
a_ = embeddings_size
a_ = hidden_sizes
a_ = depths
a_ = is_training
a_ = use_labels
a_ = hidden_act
a_ = num_labels
a_ = scope
a_ = len(_a )
def lowerCAmelCase__ ( self ):
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a_ = None
if self.use_labels:
a_ = ids_tensor([self.batch_size] , self.num_labels )
a_ = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self ):
return RegNetConfig(
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 , )
def lowerCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
a_ = TFRegNetModel(config=_a )
a_ = model(_a , training=_a )
# expected last hidden states: B, C, H // 32, W // 32
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 ):
a_ = self.num_labels
a_ = TFRegNetForImageClassification(_a )
a_ = model(_a , labels=_a , training=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ = config_and_inputs
a_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class a_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
lowerCamelCase__ : Optional[Any] = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowerCamelCase__ : Dict = (
{'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowerCamelCase__ : List[str] = False
lowerCamelCase__ : str = False
lowerCamelCase__ : int = False
lowerCamelCase__ : int = False
lowerCamelCase__ : int = False
def lowerCAmelCase__ ( self ):
a_ = TFRegNetModelTester(self )
a_ = ConfigTester(self , config_class=_a , has_text_modality=_a )
def lowerCAmelCase__ ( self ):
return
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def lowerCAmelCase__ ( self ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def lowerCAmelCase__ ( self ):
super().test_keras_fit()
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(_a )
a_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a_ = [*signature.parameters.keys()]
a_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def lowerCAmelCase__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def lowerCAmelCase__ ( self ):
def check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
a_ = model_class(_a )
a_ = model(**self._prepare_for_class(_a , _a ) , training=_a )
a_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a_ = self.model_tester.num_stages
self.assertEqual(len(_a ) , expected_num_stages + 1 )
# RegNet'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 // 2, self.model_tester.image_size // 2] , )
a_ = self.model_tester.prepare_config_and_inputs_for_common()
a_ = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
a_ = layer_type
a_ = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a_ = True
check_hidden_states_output(_a , _a , _a )
def lowerCAmelCase__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase={} ):
a_ = model(_a , return_dict=_a , **_a )
a_ = model(_a , return_dict=_a , **_a ).to_tuple()
def recursive_check(UpperCAmelCase , UpperCAmelCase ):
if isinstance(_a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_a , _a ):
recursive_check(_a , _a )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(_a , _a ) ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'''
) , )
recursive_check(_a , _a )
for model_class in self.all_model_classes:
a_ = model_class(_a )
a_ = self._prepare_for_class(_a , _a )
a_ = self._prepare_for_class(_a , _a )
check_equivalence(_a , _a , _a )
a_ = self._prepare_for_class(_a , _a , return_labels=_a )
a_ = self._prepare_for_class(_a , _a , return_labels=_a )
check_equivalence(_a , _a , _a )
a_ = self._prepare_for_class(_a , _a )
a_ = self._prepare_for_class(_a , _a )
check_equivalence(_a , _a , _a , {"""output_hidden_states""": True} )
a_ = self._prepare_for_class(_a , _a , return_labels=_a )
a_ = self._prepare_for_class(_a , _a , return_labels=_a )
check_equivalence(_a , _a , _a , {"""output_hidden_states""": True} )
def lowerCAmelCase__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def lowerCAmelCase__ ( self ):
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = TFRegNetModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def UpperCamelCase_ ( ):
a_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def lowerCAmelCase__ ( self ):
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCAmelCase__ ( self ):
a_ = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
a_ = self.default_image_processor
a_ = prepare_img()
a_ = image_processor(images=_a , return_tensors="""tf""" )
# forward pass
a_ = model(**_a , training=_a )
# verify the logits
a_ = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , _a )
a_ = tf.constant([-0.41_80, -1.50_51, -3.48_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , _a , atol=1e-4 )
| 263 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCamelCase)):
if indegree[i] == 0:
queue.append(lowerCamelCase)
while queue:
A_ : Any = queue.pop(0)
cnt += 1
topo.append(lowerCamelCase)
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(lowerCamelCase)
if cnt != len(lowerCamelCase):
print("""Cycle exists""")
else:
print(lowerCamelCase)
# Adjacency List of Graph
__magic_name__ = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 665 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
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 torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class lowercase_ :
def __init__( self , __A , __A=13 , __A=10 , __A=3 , __A=2 , __A=2 , __A=True , __A=True , __A=32 , __A=5 , __A=4 , __A=37 , __A="gelu" , __A=0.1 , __A=0.1 , __A=10 , __A=0.02 , __A="divided_space_time" , __A=None , ) -> str:
SCREAMING_SNAKE_CASE_ : Dict =parent
SCREAMING_SNAKE_CASE_ : Dict =batch_size
SCREAMING_SNAKE_CASE_ : str =image_size
SCREAMING_SNAKE_CASE_ : Dict =num_channels
SCREAMING_SNAKE_CASE_ : int =patch_size
SCREAMING_SNAKE_CASE_ : Any =num_frames
SCREAMING_SNAKE_CASE_ : Any =is_training
SCREAMING_SNAKE_CASE_ : Tuple =use_labels
SCREAMING_SNAKE_CASE_ : List[Any] =hidden_size
SCREAMING_SNAKE_CASE_ : List[Any] =num_hidden_layers
SCREAMING_SNAKE_CASE_ : Any =num_attention_heads
SCREAMING_SNAKE_CASE_ : List[str] =intermediate_size
SCREAMING_SNAKE_CASE_ : List[str] =hidden_act
SCREAMING_SNAKE_CASE_ : Union[str, Any] =hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : Optional[int] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : Any =attention_type
SCREAMING_SNAKE_CASE_ : Optional[Any] =initializer_range
SCREAMING_SNAKE_CASE_ : str =scope
SCREAMING_SNAKE_CASE_ : List[str] =num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
SCREAMING_SNAKE_CASE_ : Any =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE_ : Dict =(num_frames) * self.num_patches_per_frame + 1
def _snake_case ( self ) -> str:
SCREAMING_SNAKE_CASE_ : int =floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ : List[Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE_ : Tuple =ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE_ : Tuple =self.get_config()
return config, pixel_values, labels
def _snake_case ( self ) -> str:
SCREAMING_SNAKE_CASE_ : int =TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
SCREAMING_SNAKE_CASE_ : int =self.num_labels
return config
def _snake_case ( self , __A , __A , __A ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ : List[str] =TimesformerModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE_ : str =model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self , __A , __A , __A ) -> Tuple:
SCREAMING_SNAKE_CASE_ : Tuple =TimesformerForVideoClassification(_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE_ : str =model(_a )
# verify the logits shape
SCREAMING_SNAKE_CASE_ : Tuple =torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , _a )
def _snake_case ( self ) -> Dict:
SCREAMING_SNAKE_CASE_ : Tuple =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ : Tuple =config_and_inputs
SCREAMING_SNAKE_CASE_ : Any ={"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowerCamelCase = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
__lowerCamelCase = (
{"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def _snake_case ( self ) -> Dict:
SCREAMING_SNAKE_CASE_ : Tuple =TimesformerModelTester(self )
SCREAMING_SNAKE_CASE_ : List[str] =ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def _snake_case ( self , __A , __A , __A=False ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ : List[Any] =copy.deepcopy(_a )
if return_labels:
if model_class in get_values(_a ):
SCREAMING_SNAKE_CASE_ : Optional[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
return inputs_dict
def _snake_case ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def _snake_case ( self ) -> Tuple:
pass
def _snake_case ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ : List[str] =model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE_ : str =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def _snake_case ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : str =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ : Dict =model_class(_a )
SCREAMING_SNAKE_CASE_ : Dict =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE_ : str =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE_ : str =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def _snake_case ( self ) -> List[str]:
SCREAMING_SNAKE_CASE_ : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _snake_case ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*_a )
@slow
def _snake_case ( self ) -> Dict:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ : Dict =TimesformerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def _snake_case ( self ) -> List[str]:
if not self.has_attentions:
pass
else:
SCREAMING_SNAKE_CASE_ : Dict =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ : List[str] =True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ : Tuple =self.model_tester.seq_length
SCREAMING_SNAKE_CASE_ : List[Any] =self.model_tester.num_frames
SCREAMING_SNAKE_CASE_ : Optional[Any] =True
SCREAMING_SNAKE_CASE_ : str =False
SCREAMING_SNAKE_CASE_ : Any =True
SCREAMING_SNAKE_CASE_ : List[Any] =model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : List[Any] =model(**self._prepare_for_class(_a , _a ) )
SCREAMING_SNAKE_CASE_ : int =outputs.attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE_ : Dict =True
SCREAMING_SNAKE_CASE_ : Optional[Any] =model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : Optional[int] =model(**self._prepare_for_class(_a , _a ) )
SCREAMING_SNAKE_CASE_ : List[str] =outputs.attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
SCREAMING_SNAKE_CASE_ : int =len(_a )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE_ : Optional[int] =True
SCREAMING_SNAKE_CASE_ : Any =True
SCREAMING_SNAKE_CASE_ : Union[str, Any] =model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : List[str] =model(**self._prepare_for_class(_a , _a ) )
self.assertEqual(out_len + 1 , len(_a ) )
SCREAMING_SNAKE_CASE_ : Optional[Any] =outputs.attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def _snake_case ( self ) -> List[Any]:
def check_hidden_states_output(__A , __A , __A ):
SCREAMING_SNAKE_CASE_ : Optional[int] =model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : Union[str, Any] =model(**self._prepare_for_class(_a , _a ) )
SCREAMING_SNAKE_CASE_ : str =outputs.hidden_states
SCREAMING_SNAKE_CASE_ : Dict =self.model_tester.num_hidden_layers + 1
self.assertEqual(len(_a ) , _a )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ : Optional[Any] =True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE_ : Optional[int] =True
check_hidden_states_output(_a , _a , _a )
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
SCREAMING_SNAKE_CASE_ : List[Any] =hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
SCREAMING_SNAKE_CASE_ : int =np.load(UpperCAmelCase_ )
return list(UpperCAmelCase_ )
@require_torch
@require_vision
class lowercase_ ( unittest.TestCase ):
@cached_property
def _snake_case ( self ) -> Union[str, Any]:
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def _snake_case ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ : Any =TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
_a )
SCREAMING_SNAKE_CASE_ : Optional[Any] =self.default_image_processor
SCREAMING_SNAKE_CASE_ : Dict =prepare_video()
SCREAMING_SNAKE_CASE_ : Dict =image_processor(video[:8] , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : Optional[int] =model(**_a )
# verify the logits
SCREAMING_SNAKE_CASE_ : Optional[Any] =torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , _a )
SCREAMING_SNAKE_CASE_ : Dict =torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 443 |
'''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 __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : List[Any] ,_a : Dict=13 ,_a : List[str]=7 ,_a : Dict=True ,_a : List[Any]=True ,_a : Dict=False ,_a : Optional[int]=True ,_a : List[Any]=99 ,_a : Any=32 ,_a : Optional[int]=5 ,_a : List[Any]=4 ,_a : int=37 ,_a : List[Any]="gelu" ,_a : List[str]=0.1 ,_a : Union[str, Any]=0.1 ,_a : Any=512 ,_a : int=16 ,_a : Optional[int]=2 ,_a : Any=0.02 ,_a : Any=3 ,_a : Any=4 ,_a : List[str]=None ,):
'''simple docstring'''
A_ : List[str] = parent
A_ : Any = batch_size
A_ : Tuple = seq_length
A_ : List[str] = is_training
A_ : Tuple = use_input_mask
A_ : Dict = use_token_type_ids
A_ : List[Any] = use_labels
A_ : Union[str, Any] = vocab_size
A_ : Any = hidden_size
A_ : str = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : str = intermediate_size
A_ : Tuple = hidden_act
A_ : Any = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : List[str] = max_position_embeddings
A_ : int = type_vocab_size
A_ : Union[str, Any] = type_sequence_label_size
A_ : Any = initializer_range
A_ : List[Any] = num_labels
A_ : Optional[Any] = num_choices
A_ : List[Any] = scope
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : int = None
if self.use_input_mask:
A_ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
A_ : Dict = None
if self.use_token_type_ids:
A_ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : str = None
A_ : Any = None
A_ : str = None
if self.use_labels:
A_ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : Any = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Optional[int] = ids_tensor([self.batch_size] ,self.num_choices )
A_ : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ):
'''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=_a ,initializer_range=self.initializer_range ,)
def _a ( self : Union[str, Any] ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Any ,_a : Any ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Tuple ):
'''simple docstring'''
A_ : Any = LlamaModel(config=_a )
model.to(_a )
model.eval()
A_ : Optional[Any] = model(_a ,attention_mask=_a )
A_ : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Optional[int] ,_a : int ,_a : List[str] ,_a : Any ,_a : Any ,_a : Dict ,_a : List[str] ,_a : Optional[int] ,_a : Any ,_a : List[str] ,):
'''simple docstring'''
A_ : List[str] = True
A_ : Union[str, Any] = LlamaModel(_a )
model.to(_a )
model.eval()
A_ : Tuple = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,)
A_ : List[Any] = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,)
A_ : int = model(_a ,attention_mask=_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Any ,_a : Any ,_a : Optional[int] ,_a : List[Any] ,_a : List[Any] ,_a : Dict ,_a : Tuple ,_a : Optional[int] ,_a : List[Any] ,_a : Union[str, Any] ,):
'''simple docstring'''
A_ : List[Any] = LlamaForCausalLM(config=_a )
model.to(_a )
model.eval()
A_ : Dict = model(_a ,attention_mask=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : str ,_a : List[Any] ,_a : Dict ,_a : str ,_a : Tuple ,_a : Tuple ,_a : Tuple ,_a : Optional[Any] ,_a : Dict ,_a : Union[str, Any] ,):
'''simple docstring'''
A_ : Optional[Any] = True
A_ : Any = True
A_ : Tuple = LlamaForCausalLM(config=_a )
model.to(_a )
model.eval()
# first forward pass
A_ : Optional[int] = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,use_cache=_a ,)
A_ : Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
A_ : int = ids_tensor((self.batch_size, 3) ,config.vocab_size )
A_ : List[Any] = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
A_ : Tuple = torch.cat([input_ids, next_tokens] ,dim=-1 )
A_ : int = torch.cat([input_mask, next_mask] ,dim=-1 )
A_ : List[str] = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,output_hidden_states=_a ,)["""hidden_states"""][0]
A_ : Any = model(
_a ,attention_mask=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,past_key_values=_a ,output_hidden_states=_a ,)["""hidden_states"""][0]
# select random slice
A_ : List[str] = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
A_ : str = output_from_no_past[:, -3:, random_slice_idx].detach()
A_ : int = 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(_a ,_a ,atol=1e-3 ) )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Any = config_and_inputs
A_ : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , 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 _a ( self : List[Any] ):
'''simple docstring'''
A_ : Union[str, Any] = LlamaModelTester(self )
A_ : List[str] = ConfigTester(self ,config_class=_a ,hidden_size=37 )
def _a ( self : Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A_ : Dict = type
self.model_tester.create_and_check_model(*_a )
def _a ( self : List[Any] ):
'''simple docstring'''
A_ , A_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
A_ : List[str] = 3
A_ : Any = input_dict["""input_ids"""]
A_ : Union[str, Any] = input_ids.ne(1 ).to(_a )
A_ : Union[str, Any] = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
A_ : List[Any] = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : int = model(_a ,attention_mask=_a ,labels=_a )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self : Dict ):
'''simple docstring'''
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : str = 3
A_ : Union[str, Any] = """single_label_classification"""
A_ : Union[str, Any] = input_dict["""input_ids"""]
A_ : List[Any] = input_ids.ne(1 ).to(_a )
A_ : Dict = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
A_ : List[Any] = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : List[str] = model(_a ,attention_mask=_a ,labels=_a )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Dict = 3
A_ : Dict = """multi_label_classification"""
A_ : Any = input_dict["""input_ids"""]
A_ : Optional[Any] = input_ids.ne(1 ).to(_a )
A_ : List[str] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size ).to(torch.float )
A_ : Optional[int] = LlamaForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,attention_mask=_a ,labels=_a )
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 _a ( self : Any ):
'''simple docstring'''
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def _a ( self : Optional[Any] ,_a : List[Any] ):
'''simple docstring'''
A_ , A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = ids_tensor([1, 10] ,config.vocab_size )
A_ : Union[str, Any] = 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
A_ : int = LlamaModel(_a )
original_model.to(_a )
original_model.eval()
A_ : Tuple = original_model(_a ).last_hidden_state
A_ : Union[str, Any] = original_model(_a ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
A_ : Tuple = {"""type""": scaling_type, """factor""": 10.0}
A_ : int = LlamaModel(_a )
scaled_model.to(_a )
scaled_model.eval()
A_ : List[Any] = scaled_model(_a ).last_hidden_state
A_ : Any = scaled_model(_a ).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(_a ,_a ,atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(_a ,_a ,atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(_a ,_a ,atol=1e-5 ) )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Any = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : List[str] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf""" ,device_map="""auto""" )
A_ : str = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
A_ : Union[str, Any] = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
A_ : str = 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] ,_a ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def _a ( self : str ):
'''simple docstring'''
A_ : Dict = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : Optional[int] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf""" ,device_map="""auto""" )
A_ : Tuple = model(torch.tensor(_a ) )
# Expected mean on dim = -1
A_ : str = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
A_ : str = 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] ,_a ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!""" )
@slow
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Union[str, Any] = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : Optional[int] = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" ,device_map="""auto""" )
A_ : int = model(torch.tensor(_a ) )
# Expected mean on dim = -1
A_ : Union[str, Any] = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
A_ : Optional[int] = 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 ) ,_a ,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 _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = [1, 306, 4658, 278, 6593, 310, 2834, 338]
A_ : str = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf""" ,device_map="""auto""" )
A_ : Tuple = model(torch.tensor(_a ) )
A_ : Dict = 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 ) ,_a ,atol=1e-2 ,rtol=1e-2 )
# fmt: off
A_ : List[str] = 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] ,_a ,atol=1e-5 ,rtol=1e-5 )
@unittest.skip("""Model is curently gated""" )
@slow
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = """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"""
A_ : List[str] = """Simply put, the theory of relativity states that """
A_ : Any = LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" )
A_ : Union[str, Any] = tokenizer.encode(_a ,return_tensors="""pt""" )
A_ : List[str] = LlamaForCausalLM.from_pretrained(
"""meta-llama/Llama-2-13b-chat-hf""" ,device_map="""sequential""" ,use_safetensors=_a )
# greedy generation outputs
A_ : str = model.generate(_a ,max_new_tokens=64 ,top_p=_a ,temperature=1 ,do_sample=_a )
A_ : Optional[Any] = tokenizer.decode(generated_ids[0] ,skip_special_tokens=_a )
self.assertEqual(_a ,_a )
| 665 | 0 |
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 ConditionalDetrImageProcessor
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=7 , lowerCamelCase__=3 , lowerCamelCase__=30 , lowerCamelCase__=400 , lowerCamelCase__=True , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__=[0.5, 0.5, 0.5] , lowerCamelCase__=[0.5, 0.5, 0.5] , lowerCamelCase__=True , lowerCamelCase__=1 / 255 , lowerCamelCase__=True , ):
lowerCAmelCase_: Any = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
lowerCAmelCase_: Optional[int] = parent
lowerCAmelCase_: str = batch_size
lowerCAmelCase_: Optional[Any] = num_channels
lowerCAmelCase_: Optional[int] = min_resolution
lowerCAmelCase_: List[str] = max_resolution
lowerCAmelCase_: Union[str, Any] = do_resize
lowerCAmelCase_: Tuple = size
lowerCAmelCase_: Any = do_normalize
lowerCAmelCase_: int = image_mean
lowerCAmelCase_: Optional[int] = image_std
lowerCAmelCase_: int = do_rescale
lowerCAmelCase_: Optional[Any] = rescale_factor
lowerCAmelCase_: Optional[Any] = do_pad
def _a ( self ):
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 _a ( self , lowerCamelCase__ , lowerCamelCase__=False ):
if not batched:
lowerCAmelCase_: Optional[Any] = image_inputs[0]
if isinstance(_a , Image.Image ):
lowerCAmelCase_: List[Any] = image.size
else:
lowerCAmelCase_: Dict = image.shape[1], image.shape[2]
if w < h:
lowerCAmelCase_: List[Any] = int(self.size["shortest_edge"] * h / w )
lowerCAmelCase_: str = self.size["""shortest_edge"""]
elif w > h:
lowerCAmelCase_: Optional[int] = self.size["""shortest_edge"""]
lowerCAmelCase_: Optional[Any] = int(self.size["shortest_edge"] * w / h )
else:
lowerCAmelCase_: str = self.size["""shortest_edge"""]
lowerCAmelCase_: Union[str, Any] = self.size["""shortest_edge"""]
else:
lowerCAmelCase_: Any = []
for image in image_inputs:
lowerCAmelCase_: Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCAmelCase_: List[str] = max(_a , key=lambda lowerCamelCase__ : item[0] )[0]
lowerCAmelCase_: Tuple = max(_a , key=lambda lowerCamelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _lowercase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: List[str] = ConditionalDetrImageProcessor if is_vision_available() else None
def _a ( self ):
lowerCAmelCase_: Dict = ConditionalDetrImageProcessingTester(self )
@property
def _a ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , "image_mean" ) )
self.assertTrue(hasattr(_a , "image_std" ) )
self.assertTrue(hasattr(_a , "do_normalize" ) )
self.assertTrue(hasattr(_a , "do_resize" ) )
self.assertTrue(hasattr(_a , "size" ) )
def _a ( self ):
lowerCAmelCase_: int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , _a )
lowerCAmelCase_: List[str] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_a )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , _a )
def _a ( self ):
pass
def _a ( self ):
lowerCAmelCase_: Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_: List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
lowerCAmelCase_: Any = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_: List[Any] = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_: str = self.image_processor_tester.get_expected_values(_a , batched=_a )
lowerCAmelCase_: Tuple = image_processing(_a , 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 _a ( self ):
lowerCAmelCase_: int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_: Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
lowerCAmelCase_: Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_: Any = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_: Any = image_processing(_a , return_tensors="pt" ).pixel_values
lowerCAmelCase_: List[Any] = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ):
lowerCAmelCase_: str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_: int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
lowerCAmelCase_: Tuple = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_: str = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_: Dict = image_processing(_a , return_tensors="pt" ).pixel_values
lowerCAmelCase_: Union[str, Any] = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _a ( self ):
lowerCAmelCase_: List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
lowerCAmelCase_: str = json.loads(f.read() )
lowerCAmelCase_: Any = {"""image_id""": 39_769, """annotations""": target}
# encode them
lowerCAmelCase_: Optional[Any] = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
lowerCAmelCase_: Any = image_processing(images=_a , annotations=_a , return_tensors="pt" )
# verify pixel values
lowerCAmelCase_: Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , _a )
lowerCAmelCase_: Union[str, Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _a , atol=1E-4 ) )
# verify area
lowerCAmelCase_: Optional[int] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _a ) )
# verify boxes
lowerCAmelCase_: Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , _a )
lowerCAmelCase_: Dict = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _a , atol=1E-3 ) )
# verify image_id
lowerCAmelCase_: List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _a ) )
# verify is_crowd
lowerCAmelCase_: Optional[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _a ) )
# verify class_labels
lowerCAmelCase_: Any = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _a ) )
# verify orig_size
lowerCAmelCase_: int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _a ) )
# verify size
lowerCAmelCase_: Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _a ) )
@slow
def _a ( self ):
lowerCAmelCase_: Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
lowerCAmelCase_: int = json.loads(f.read() )
lowerCAmelCase_: str = {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}
lowerCAmelCase_: str = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
lowerCAmelCase_: str = ConditionalDetrImageProcessor(format="coco_panoptic" )
lowerCAmelCase_: str = image_processing(images=_a , annotations=_a , masks_path=_a , return_tensors="pt" )
# verify pixel values
lowerCAmelCase_: Any = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , _a )
lowerCAmelCase_: List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _a , atol=1E-4 ) )
# verify area
lowerCAmelCase_: Optional[Any] = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _a ) )
# verify boxes
lowerCAmelCase_: Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , _a )
lowerCAmelCase_: Dict = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _a , atol=1E-3 ) )
# verify image_id
lowerCAmelCase_: Optional[Any] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _a ) )
# verify is_crowd
lowerCAmelCase_: Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _a ) )
# verify class_labels
lowerCAmelCase_: List[str] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _a ) )
# verify masks
lowerCAmelCase_: Dict = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , _a )
# verify orig_size
lowerCAmelCase_: List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _a ) )
# verify size
lowerCAmelCase_: int = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _a ) ) | 613 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n'
__magic_name__ = '\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'
__magic_name__ = r'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n >>> metric = datasets.load_metric("competition_math")\n >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n >>> print(results)\n {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
'''simple docstring'''
def _a ( self : Optional[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ),
"""references""": datasets.Value("""string""" ),
} ) ,homepage="""https://github.com/hendrycks/math""" ,codebase_urls=["""https://github.com/hendrycks/math"""] ,)
def _a ( self : List[Any] ,_a : Union[str, Any] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Union[str, Any] = 0.0
for i, j in zip(_a ,_a ):
n_correct += 1.0 if math_equivalence.is_equiv(_a ,_a ) else 0.0
A_ : List[str] = n_correct / len(_a )
return {
"accuracy": accuracy,
}
| 665 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
UpperCAmelCase : Optional[Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCAmelCase : str = 128022
UpperCAmelCase : int = 128028
@require_sentencepiece
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase):
_lowercase : Tuple = MaMaaaTokenizer
_lowercase : int = False
_lowercase : Optional[Any] = False
_lowercase : int = True
def _lowercase ( self ) -> Dict:
'''simple docstring'''
super().setUp()
a__ : Optional[int] =["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
a__ : Dict =dict(zip(_a , range(len(_a ) ) ) )
a__ : List[Any] =Path(self.tmpdirname )
save_json(_a , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(_a , save_dir / VOCAB_FILES_NAMES["spm_file"] )
a__ : Any =MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self , **lowerCAmelCase__ ) -> Any:
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **_a )
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : List[str] ="""</s>"""
a__ : int =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Optional[Any] =self.get_tokenizer()
a__ : str =list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "</s>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "<s>" )
self.assertEqual(len(_a ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip("Skip this test while all models are still to be uploaded." )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
pass
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Any =self.get_tokenizer()
a__ : Dict =tokenizer.tokenize("This is a test" )
self.assertListEqual(_a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [2, 3, 4, 5, 6] , )
a__ : Dict =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(_a , ["▁This", "▁is", "▁a", "▁t", "est"] )
a__ : Union[str, Any] =tokenizer.convert_tokens_to_string(_a )
self.assertEqual(_a , "This is a test" )
@slow
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Union[str, Any] ={"""input_ids""": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 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_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="facebook/m2m100_418M" , revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase):
_lowercase : str = """facebook/m2m100_418M"""
_lowercase : Optional[Any] = [
"""In my opinion, there are two levels of response from the French government.""",
"""NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
]
_lowercase : Tuple = [
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
]
# fmt: off
_lowercase : List[Any] = [EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]
@classmethod
def _lowercase ( cls ) -> List[Any]:
'''simple docstring'''
a__ : MaMaaaTokenizer =MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en" , tgt_lang="fr" )
a__ : Optional[Any] =1
return cls
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id("ar" ) , 1_2_8_0_0_6 )
self.assertEqual(self.tokenizer.get_lang_id("en" ) , 1_2_8_0_2_2 )
self.assertEqual(self.tokenizer.get_lang_id("ro" ) , 1_2_8_0_7_6 )
self.assertEqual(self.tokenizer.get_lang_id("mr" ) , 1_2_8_0_6_3 )
def _lowercase ( self ) -> int:
'''simple docstring'''
a__ : Any =self.tokenizer.get_vocab()
self.assertEqual(len(_a ) , self.tokenizer.vocab_size )
self.assertEqual(vocab["<unk>"] , 3 )
self.assertIn(self.tokenizer.get_lang_token("en" ) , _a )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : str ="""en"""
a__ : Optional[Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _a )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
self.assertIn(_a , self.tokenizer.all_special_ids )
# fmt: off
a__ : Tuple =[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
# fmt: on
a__ : Any =self.tokenizer.decode(_a , skip_special_tokens=_a )
a__ : str =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_a )
self.assertEqual(_a , _a )
self.assertNotIn(self.tokenizer.eos_token , _a )
def _lowercase ( self ) -> Any:
'''simple docstring'''
a__ : Union[str, Any] =tempfile.mkdtemp()
a__ : List[Any] =self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(_a )
a__ : Union[str, Any] =MaMaaaTokenizer.from_pretrained(_a )
self.assertDictEqual(new_tok.lang_token_to_id , _a )
@require_torch
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
a__ : List[Any] ="""en"""
a__ : Union[str, Any] ="""fr"""
a__ : int =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_a , return_tensors="pt" )
a__ : List[str] =shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
a__ : Dict =batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def _lowercase ( self ) -> int:
'''simple docstring'''
a__ : List[Any] ="""mr"""
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
a__ : str ="""zh"""
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def _lowercase ( self ) -> int:
'''simple docstring'''
a__ : Optional[int] ="""mr"""
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
a__ : Optional[int] ="""zh"""
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Union[str, Any] =self.tokenizer._build_translation_inputs("A test" , return_tensors="pt" , src_lang="en" , tgt_lang="ar" )
self.assertEqual(
nested_simplify(_a ) , {
# en_XX, A, test, EOS
"input_ids": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 1_2_8_0_0_6,
} , )
| 563 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """retribert"""
def __init__( self : int ,_a : Dict=30522 ,_a : List[Any]=768 ,_a : Optional[Any]=8 ,_a : str=12 ,_a : str=3072 ,_a : Tuple="gelu" ,_a : Optional[int]=0.1 ,_a : Dict=0.1 ,_a : List[Any]=512 ,_a : Union[str, Any]=2 ,_a : Tuple=0.02 ,_a : List[str]=1e-12 ,_a : Dict=True ,_a : Tuple=128 ,_a : Optional[int]=0 ,**_a : Tuple ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,**_a )
A_ : Dict = vocab_size
A_ : int = hidden_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : int = intermediate_size
A_ : Tuple = hidden_dropout_prob
A_ : Optional[int] = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : Optional[int] = initializer_range
A_ : Dict = layer_norm_eps
A_ : str = share_encoders
A_ : List[Any] = projection_dim
| 665 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class snake_case ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_a = """audio-spectrogram-transformer"""
def __init__( self, _lowercase=768, _lowercase=12, _lowercase=12, _lowercase=3072, _lowercase="gelu", _lowercase=0.0, _lowercase=0.0, _lowercase=0.02, _lowercase=1E-12, _lowercase=16, _lowercase=True, _lowercase=10, _lowercase=10, _lowercase=1024, _lowercase=128, **_lowercase, ) -> Any:
super().__init__(**_a )
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = patch_size
SCREAMING_SNAKE_CASE_ = qkv_bias
SCREAMING_SNAKE_CASE_ = frequency_stride
SCREAMING_SNAKE_CASE_ = time_stride
SCREAMING_SNAKE_CASE_ = max_length
SCREAMING_SNAKE_CASE_ = num_mel_bins
| 294 |
'''simple docstring'''
import os
import re
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
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'vocab_file': 'spiece.model'}
__magic_name__ = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
__magic_name__ = {
'google/bigbird-roberta-base': 4_096,
'google/bigbird-roberta-large': 4_096,
'google/bigbird-base-trivia-itc': 4_096,
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
a_ = []
def __init__( self : Optional[int] ,_a : int ,_a : Optional[Any]="<unk>" ,_a : int="<s>" ,_a : str="</s>" ,_a : Optional[Any]="<pad>" ,_a : Tuple="[SEP]" ,_a : Tuple="[MASK]" ,_a : Union[str, Any]="[CLS]" ,_a : Optional[Dict[str, Any]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : Dict = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else bos_token
A_ : Union[str, Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else eos_token
A_ : Optional[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else unk_token
A_ : Union[str, Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else pad_token
A_ : Any = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else cls_token
A_ : Optional[int] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
A_ : List[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else mask_token
A_ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_a ,eos_token=_a ,unk_token=_a ,pad_token=_a ,sep_token=_a ,mask_token=_a ,cls_token=_a ,sp_model_kwargs=self.sp_model_kwargs ,**_a ,)
A_ : Optional[int] = vocab_file
A_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def _a ( self : Union[str, Any] ):
'''simple docstring'''
return self.sp_model.get_piece_size()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : Tuple = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[Any] ):
'''simple docstring'''
A_ : Union[str, Any] = self.__dict__.copy()
A_ : Union[str, Any] = None
return state
def __setstate__( self : List[Any] ,_a : Any ):
'''simple docstring'''
A_ : Tuple = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
A_ : Tuple = {}
A_ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self : Union[str, Any] ,_a : str ):
'''simple docstring'''
return self.sp_model.encode(_a ,out_type=_a )
def _a ( self : Optional[int] ,_a : str ):
'''simple docstring'''
return self.sp_model.piece_to_id(_a )
def _a ( self : int ,_a : Optional[int] ):
'''simple docstring'''
A_ : List[str] = self.sp_model.IdToPiece(_a )
return token
def _a ( self : Dict ,_a : int ):
'''simple docstring'''
A_ : int = []
A_ : Any = """"""
A_ : str = 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(_a ) + token
A_ : Dict = True
A_ : Union[str, Any] = []
else:
current_sub_tokens.append(_a )
A_ : str = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def _a ( self : int ,_a : List[int] ,_a : bool = False ,_a : bool = None ,_a : bool = True ,**_a : str ,):
'''simple docstring'''
A_ : Any = kwargs.pop("""use_source_tokenizer""" ,_a )
A_ : Union[str, Any] = self.convert_ids_to_tokens(_a ,skip_special_tokens=_a )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
A_ : str = []
A_ : int = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_a ) )
A_ : List[str] = []
sub_texts.append(_a )
else:
current_sub_text.append(_a )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_a ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
A_ : Optional[int] = re.sub(r""" (\[(MASK|SEP)\])""" ,r"""\1""" ,""" """.join(_a ) )
else:
A_ : Tuple = """""".join(_a )
A_ : str = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
A_ : Optional[Any] = self.clean_up_tokenization(_a )
return clean_text
else:
return text
def _a ( self : int ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A_ : int = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_a )
elif not os.path.isfile(self.vocab_file ):
with open(_a ,"""wb""" ) as fi:
A_ : str = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
def _a ( self : Optional[Any] ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A_ : List[Any] = [self.cls_token_id]
A_ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def _a ( self : Optional[int] ,_a : List[int] ,_a : Optional[List[int]] = None ,_a : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a ,token_ids_a=_a ,already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
def _a ( self : Tuple ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : Tuple = [self.sep_token_id]
A_ : Optional[int] = [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]
| 665 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
# See all BART models at https://huggingface.co/models?filter=bart
__UpperCAmelCase = {
"""vocab_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
},
"""merges_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
},
}
__UpperCAmelCase = {
"""facebook/bart-base""": 1_024,
"""facebook/bart-large""": 1_024,
"""facebook/bart-large-mnli""": 1_024,
"""facebook/bart-large-cnn""": 1_024,
"""facebook/bart-large-xsum""": 1_024,
"""yjernite/bart_eli5""": 1_024,
}
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase : List[Any] =VOCAB_FILES_NAMES
lowerCamelCase : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : List[str] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Any =["input_ids", "attention_mask"]
lowerCamelCase : Tuple =BartTokenizer
def __init__( self : str , lowerCAmelCase : Any=None , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : int=None , lowerCAmelCase : Optional[int]="replace" , lowerCAmelCase : Dict="<s>" , lowerCAmelCase : Optional[Any]="</s>" , lowerCAmelCase : Dict="</s>" , lowerCAmelCase : Tuple="<s>" , lowerCAmelCase : Optional[Any]="<unk>" , lowerCAmelCase : List[str]="<pad>" , lowerCAmelCase : int="<mask>" , lowerCAmelCase : str=False , lowerCAmelCase : List[str]=True , **lowerCAmelCase : Dict , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
_a , _a , tokenizer_file=_a , errors=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , add_prefix_space=_a , trim_offsets=_a , **_a , )
__lowerCAmelCase : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , _a ) != add_prefix_space:
__lowerCAmelCase : List[str] = getattr(_a , pre_tok_state.pop("""type""" ) )
__lowerCAmelCase : Optional[int] = add_prefix_space
__lowerCAmelCase : int = pre_tok_class(**_a )
__lowerCAmelCase : str = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__lowerCAmelCase : str = """post_processor"""
__lowerCAmelCase : List[Any] = getattr(self.backend_tokenizer , _a , _a )
if tokenizer_component_instance:
__lowerCAmelCase : Tuple = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__lowerCAmelCase : Tuple = tuple(state["""sep"""] )
if "cls" in state:
__lowerCAmelCase : Tuple = tuple(state["""cls"""] )
__lowerCAmelCase : List[str] = False
if state.get("""add_prefix_space""" , _a ) != add_prefix_space:
__lowerCAmelCase : Dict = add_prefix_space
__lowerCAmelCase : Any = True
if state.get("""trim_offsets""" , _a ) != trim_offsets:
__lowerCAmelCase : Union[str, Any] = trim_offsets
__lowerCAmelCase : List[Any] = True
if changes_to_apply:
__lowerCAmelCase : Optional[int] = getattr(_a , state.pop("""type""" ) )
__lowerCAmelCase : Tuple = component_class(**_a )
setattr(self.backend_tokenizer , _a , _a )
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : int = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else value
__lowerCAmelCase : List[Any] = value
def SCREAMING_SNAKE_CASE ( self : str , *lowerCAmelCase : str , **lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = kwargs.get("""is_split_into_words""" , _a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*_a , **_a )
def SCREAMING_SNAKE_CASE ( self : str , *lowerCAmelCase : List[Any] , **lowerCAmelCase : str ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = kwargs.get("""is_split_into_words""" , _a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*_a , **_a )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : str , lowerCAmelCase : Optional[str] = None ) -> int:
"""simple docstring"""
__lowerCAmelCase : str = self._tokenizer.model.save(_a , name=_a )
return tuple(_a )
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : Optional[int] , lowerCAmelCase : int=None ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Dict = [self.sep_token_id]
__lowerCAmelCase : Any = [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 + sep + token_ids_a + sep ) * [0]
| 651 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
a_ = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
a_ = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def _a ( self : List[str] ,_a : int ,_a : Any ,_a : int ):
'''simple docstring'''
A_ : Dict = TextaTextGenerationPipeline(model=_a ,tokenizer=_a )
return generator, ["Something to write", "Something else"]
def _a ( self : str ,_a : Union[str, Any] ,_a : int ):
'''simple docstring'''
A_ : Any = generator("""Something there""" )
self.assertEqual(_a ,[{"""generated_text""": ANY(_a )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )
A_ : List[Any] = generator(["""This is great !""", """Something else"""] ,num_return_sequences=2 ,do_sample=_a )
self.assertEqual(
_a ,[
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
] ,)
A_ : List[str] = generator(
["""This is great !""", """Something else"""] ,num_return_sequences=2 ,batch_size=2 ,do_sample=_a )
self.assertEqual(
_a ,[
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
[{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
] ,)
with self.assertRaises(_a ):
generator(4 )
@require_torch
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : int = pipeline("""text2text-generation""" ,model="""patrickvonplaten/t5-tiny-random""" ,framework="""pt""" )
# do_sample=False necessary for reproducibility
A_ : Tuple = generator("""Something there""" ,do_sample=_a )
self.assertEqual(_a ,[{"""generated_text""": """"""}] )
A_ : Optional[int] = 3
A_ : Tuple = generator(
"""Something there""" ,num_return_sequences=_a ,num_beams=_a ,)
A_ : Optional[Any] = [
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """"""},
]
self.assertEqual(_a ,_a )
A_ : Optional[int] = generator("""This is a test""" ,do_sample=_a ,num_return_sequences=2 ,return_tensors=_a )
self.assertEqual(
_a ,[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
] ,)
A_ : Dict = generator.model.config.eos_token_id
A_ : Optional[int] = """<pad>"""
A_ : List[Any] = generator(
["""This is a test""", """This is a second test"""] ,do_sample=_a ,num_return_sequences=2 ,batch_size=2 ,return_tensors=_a ,)
self.assertEqual(
_a ,[
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
] ,)
@require_tf
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = pipeline("""text2text-generation""" ,model="""patrickvonplaten/t5-tiny-random""" ,framework="""tf""" )
# do_sample=False necessary for reproducibility
A_ : Dict = generator("""Something there""" ,do_sample=_a )
self.assertEqual(_a ,[{"""generated_text""": """"""}] )
| 665 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Optional[Any] = 42
@flax_register_to_config
class A__ ( nn.Module , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Union[str, Any] = 32
_UpperCAmelCase : Optional[int] = 4
_UpperCAmelCase : Any = 4
_UpperCAmelCase : Dict = (
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""DownBlock2D""",
)
_UpperCAmelCase : Any = ("""UpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""")
_UpperCAmelCase : Optional[Any] = False
_UpperCAmelCase : Tuple = (320, 640, 1280, 1280)
_UpperCAmelCase : str = 2
_UpperCAmelCase : List[Any] = 8
_UpperCAmelCase : Dict = None
_UpperCAmelCase : int = 1280
_UpperCAmelCase : Union[str, Any] = 0.0
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : Optional[int] = jnp.floataa
_UpperCAmelCase : int = True
_UpperCAmelCase : Union[str, Any] = 0
_UpperCAmelCase : str = False
def UpperCamelCase__ ( self , __magic_name__ ):
lowerCamelCase : Union[str, Any] = (1, self.in_channels, self.sample_size, self.sample_size)
lowerCamelCase : Optional[Any] = jnp.zeros(_a , dtype=jnp.floataa )
lowerCamelCase : Union[str, Any] = jnp.ones((1,) , dtype=jnp.intaa )
lowerCamelCase : str = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowerCamelCase : Optional[Any] = jax.random.split(_a )
lowerCamelCase : Optional[Any] = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(_a , _a , _a , _a )["params"]
def UpperCamelCase__ ( self ):
lowerCamelCase : Dict = self.block_out_channels
lowerCamelCase : str = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
"""At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""" )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowerCamelCase : Optional[Any] = self.num_attention_heads or self.attention_head_dim
# input
lowerCamelCase : int = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowerCamelCase : Any = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowerCamelCase : List[str] = FlaxTimestepEmbedding(_a , dtype=self.dtype )
lowerCamelCase : Optional[int] = self.only_cross_attention
if isinstance(_a , _a ):
lowerCamelCase : str = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_a , _a ):
lowerCamelCase : List[Any] = (num_attention_heads,) * len(self.down_block_types )
# down
lowerCamelCase : Optional[Any] = []
lowerCamelCase : Dict = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
lowerCamelCase : Tuple = output_channel
lowerCamelCase : List[Any] = block_out_channels[i]
lowerCamelCase : Optional[int] = i == len(_a ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowerCamelCase : Tuple = FlaxCrossAttnDownBlockaD(
in_channels=_a , out_channels=_a , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
lowerCamelCase : str = FlaxDownBlockaD(
in_channels=_a , out_channels=_a , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(_a )
lowerCamelCase : Union[str, Any] = down_blocks
# mid
lowerCamelCase : Optional[Any] = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
lowerCamelCase : Dict = []
lowerCamelCase : List[str] = list(reversed(_a ) )
lowerCamelCase : Any = list(reversed(_a ) )
lowerCamelCase : Union[str, Any] = list(reversed(_a ) )
lowerCamelCase : Any = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
lowerCamelCase : Dict = output_channel
lowerCamelCase : Optional[int] = reversed_block_out_channels[i]
lowerCamelCase : Optional[int] = reversed_block_out_channels[min(i + 1 , len(_a ) - 1 )]
lowerCamelCase : Dict = i == len(_a ) - 1
if up_block_type == "CrossAttnUpBlock2D":
lowerCamelCase : Any = FlaxCrossAttnUpBlockaD(
in_channels=_a , out_channels=_a , prev_output_channel=_a , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
lowerCamelCase : Dict = FlaxUpBlockaD(
in_channels=_a , out_channels=_a , prev_output_channel=_a , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(_a )
lowerCamelCase : int = output_channel
lowerCamelCase : Dict = up_blocks
# out
lowerCamelCase : int = nn.GroupNorm(num_groups=3_2 , epsilon=1e-5 )
lowerCamelCase : str = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__=None , __magic_name__ = True , __magic_name__ = False , ):
if not isinstance(_a , jnp.ndarray ):
lowerCamelCase : Any = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(_a , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowerCamelCase : int = timesteps.astype(dtype=jnp.floataa )
lowerCamelCase : List[str] = jnp.expand_dims(_a , 0 )
lowerCamelCase : int = self.time_proj(_a )
lowerCamelCase : Union[str, Any] = self.time_embedding(_a )
# 2. pre-process
lowerCamelCase : str = jnp.transpose(_a , (0, 2, 3, 1) )
lowerCamelCase : List[str] = self.conv_in(_a )
# 3. down
lowerCamelCase : Tuple = (sample,)
for down_block in self.down_blocks:
if isinstance(_a , _a ):
lowerCamelCase : Union[str, Any] = down_block(_a , _a , _a , deterministic=not train )
else:
lowerCamelCase : str = down_block(_a , _a , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
lowerCamelCase : List[Any] = ()
for down_block_res_sample, down_block_additional_residual in zip(
_a , _a ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
lowerCamelCase : Union[str, Any] = new_down_block_res_samples
# 4. mid
lowerCamelCase : List[Any] = self.mid_block(_a , _a , _a , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
lowerCamelCase : Union[str, Any] = down_block_res_samples[-(self.layers_per_block + 1) :]
lowerCamelCase : Any = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(_a , _a ):
lowerCamelCase : List[str] = up_block(
_a , temb=_a , encoder_hidden_states=_a , res_hidden_states_tuple=_a , deterministic=not train , )
else:
lowerCamelCase : Optional[int] = up_block(_a , temb=_a , res_hidden_states_tuple=_a , deterministic=not train )
# 6. post-process
lowerCamelCase : Optional[Any] = self.conv_norm_out(_a )
lowerCamelCase : Tuple = nn.silu(_a )
lowerCamelCase : Optional[Any] = self.conv_out(_a )
lowerCamelCase : List[str] = jnp.transpose(_a , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=_a )
| 681 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """gpt_bigcode"""
a_ = ["""past_key_values"""]
a_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Optional[int] ,_a : Optional[int]=50257 ,_a : Dict=1024 ,_a : Union[str, Any]=768 ,_a : Union[str, Any]=12 ,_a : Union[str, Any]=12 ,_a : Tuple=None ,_a : int="gelu_pytorch_tanh" ,_a : Optional[Any]=0.1 ,_a : List[str]=0.1 ,_a : Union[str, Any]=0.1 ,_a : List[Any]=1e-5 ,_a : List[str]=0.02 ,_a : Any=True ,_a : Union[str, Any]=True ,_a : Tuple=50256 ,_a : Optional[int]=50256 ,_a : int=True ,_a : Optional[int]=True ,_a : Optional[int]=True ,**_a : List[str] ,):
'''simple docstring'''
A_ : Optional[Any] = vocab_size
A_ : int = n_positions
A_ : Union[str, Any] = n_embd
A_ : int = n_layer
A_ : Optional[int] = n_head
A_ : Union[str, Any] = n_inner
A_ : List[Any] = activation_function
A_ : Dict = resid_pdrop
A_ : int = embd_pdrop
A_ : Optional[int] = attn_pdrop
A_ : Union[str, Any] = layer_norm_epsilon
A_ : int = initializer_range
A_ : Union[str, Any] = scale_attn_weights
A_ : List[str] = use_cache
A_ : Tuple = attention_softmax_in_fpaa
A_ : List[str] = scale_attention_softmax_in_fpaa
A_ : Union[str, Any] = multi_query
A_ : Any = bos_token_id
A_ : Optional[int] = eos_token_id
super().__init__(bos_token_id=_a ,eos_token_id=_a ,**_a )
| 665 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class a_ :
'''simple docstring'''
pass
| 314 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
__magic_name__ = {
'vocab_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
},
'merges_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
},
}
__magic_name__ = {
'allenai/longformer-base-4096': 4_096,
'allenai/longformer-large-4096': 4_096,
'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase ( ):
A_ : Union[str, Any] = (
list(range(ord("""!""") , ord("""~""") + 1)) + list(range(ord("""¡""") , ord("""¬""") + 1)) + list(range(ord("""®""") , ord("""ÿ""") + 1))
)
A_ : Optional[Any] = bs[:]
A_ : List[str] = 0
for b in range(2**8):
if b not in bs:
bs.append(lowerCamelCase)
cs.append(2**8 + n)
n += 1
A_ : List[Any] = [chr(lowerCamelCase) for n in cs]
return dict(zip(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int):
A_ : int = set()
A_ : int = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
A_ : List[str] = char
return pairs
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self : int ,_a : Tuple ,_a : Union[str, Any] ,_a : Optional[Any]="replace" ,_a : Union[str, Any]="<s>" ,_a : Union[str, Any]="</s>" ,_a : int="</s>" ,_a : List[str]="<s>" ,_a : List[Any]="<unk>" ,_a : Any="<pad>" ,_a : Dict="<mask>" ,_a : Optional[int]=False ,**_a : List[Any] ,):
'''simple docstring'''
A_ : Dict = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else bos_token
A_ : Optional[int] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else eos_token
A_ : Optional[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else sep_token
A_ : int = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else cls_token
A_ : int = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else unk_token
A_ : Optional[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
A_ : Any = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else mask_token
super().__init__(
errors=_a ,bos_token=_a ,eos_token=_a ,unk_token=_a ,sep_token=_a ,cls_token=_a ,pad_token=_a ,mask_token=_a ,add_prefix_space=_a ,**_a ,)
with open(_a ,encoding="""utf-8""" ) as vocab_handle:
A_ : str = json.load(_a )
A_ : Optional[int] = {v: k for k, v in self.encoder.items()}
A_ : List[str] = errors # how to handle errors in decoding
A_ : List[str] = bytes_to_unicode()
A_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(_a ,encoding="""utf-8""" ) as merges_handle:
A_ : Any = merges_handle.read().split("""\n""" )[1:-1]
A_ : str = [tuple(merge.split() ) for merge in bpe_merges]
A_ : int = dict(zip(_a ,range(len(_a ) ) ) )
A_ : List[Any] = {}
A_ : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
A_ : Optional[Any] = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
def _a ( self : Any ):
'''simple docstring'''
return len(self.encoder )
def _a ( self : str ):
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def _a ( self : int ,_a : int ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
A_ : Optional[int] = tuple(_a )
A_ : Any = get_pairs(_a )
if not pairs:
return token
while True:
A_ : Optional[Any] = min(_a ,key=lambda _a : self.bpe_ranks.get(_a ,float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
A_ , A_ : Dict = bigram
A_ : int = []
A_ : Optional[Any] = 0
while i < len(_a ):
try:
A_ : List[str] = word.index(_a ,_a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
A_ : Tuple = j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
A_ : str = tuple(_a )
A_ : str = new_word
if len(_a ) == 1:
break
else:
A_ : int = get_pairs(_a )
A_ : Optional[int] = """ """.join(_a )
A_ : List[str] = word
return word
def _a ( self : Dict ,_a : Optional[int] ):
'''simple docstring'''
A_ : Any = []
for token in re.findall(self.pat ,_a ):
A_ : Any = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_a ).split(""" """ ) )
return bpe_tokens
def _a ( self : Union[str, Any] ,_a : Optional[int] ):
'''simple docstring'''
return self.encoder.get(_a ,self.encoder.get(self.unk_token ) )
def _a ( self : int ,_a : Dict ):
'''simple docstring'''
return self.decoder.get(_a )
def _a ( self : Optional[int] ,_a : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = """""".join(_a )
A_ : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" ,errors=self.errors )
return text
def _a ( self : int ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A_ : int = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
A_ : int = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_a ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=_a ,ensure_ascii=_a ) + """\n""" )
A_ : int = 0
with open(_a ,"""w""" ,encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda _a : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
A_ : Dict = token_index
writer.write(""" """.join(_a ) + """\n""" )
index += 1
return vocab_file, merge_file
def _a ( self : List[str] ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A_ : int = [self.cls_token_id]
A_ : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self : int ,_a : List[int] ,_a : Optional[List[int]] = None ,_a : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a ,token_ids_a=_a ,already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def _a ( self : Any ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : Union[str, Any] = [self.sep_token_id]
A_ : Union[str, Any] = [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 + sep + token_ids_a + sep ) * [0]
def _a ( self : str ,_a : Optional[int] ,_a : Union[str, Any]=False ,**_a : Dict ):
'''simple docstring'''
A_ : Any = kwargs.pop("""add_prefix_space""" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_a ) > 0 and not text[0].isspace()):
A_ : Optional[int] = """ """ + text
return (text, kwargs)
| 665 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig",
],
"processing_x_clip": ["XCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"XCLIPModel",
"XCLIPPreTrainedModel",
"XCLIPTextModel",
"XCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Union[str, Any] , A__ : VQModel , A__ : UNetaDModel , A__ : DDIMScheduler ) -> List[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=A__ , unet=A__ , scheduler=A__ )
@torch.no_grad()
def __call__( self : str , A__ : int = 1 , A__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , A__ : float = 0.0 , A__ : int = 50 , A__ : Optional[str] = "pil" , A__ : bool = True , **A__ : Optional[Any] , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
snake_case_ : Optional[int] = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=A__ , )
snake_case_ : List[Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
snake_case_ : Any = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(A__ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
snake_case_ : Union[str, Any] = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case_ : List[Any] = {}
if accepts_eta:
snake_case_ : int = eta
for t in self.progress_bar(self.scheduler.timesteps ):
snake_case_ : Union[str, Any] = self.scheduler.scale_model_input(A__ , A__ )
# predict the noise residual
snake_case_ : Dict = self.unet(A__ , A__ ).sample
# compute the previous noisy sample x_t -> x_t-1
snake_case_ : Union[str, Any] = self.scheduler.step(A__ , A__ , A__ , **A__ ).prev_sample
# decode the image latents with the VAE
snake_case_ : int = self.vqvae.decode(A__ ).sample
snake_case_ : Dict = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ : Optional[int] = self.numpy_to_pil(A__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A__ )
| 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: list[str] | None = None , lowerCAmelCase_: dict[str, float] | None = None , lowerCAmelCase_: bool = False , ):
snake_case_ : List[str] = cipher_alphabet or [chr(lowerCAmelCase_ ) for i in range(9_7 , 1_2_3 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
snake_case_ : List[str] = {
"a": 0.0_8_4_9_7,
"b": 0.0_1_4_9_2,
"c": 0.0_2_2_0_2,
"d": 0.0_4_2_5_3,
"e": 0.1_1_1_6_2,
"f": 0.0_2_2_2_8,
"g": 0.0_2_0_1_5,
"h": 0.0_6_0_9_4,
"i": 0.0_7_5_4_6,
"j": 0.0_0_1_5_3,
"k": 0.0_1_2_9_2,
"l": 0.0_4_0_2_5,
"m": 0.0_2_4_0_6,
"n": 0.0_6_7_4_9,
"o": 0.0_7_5_0_7,
"p": 0.0_1_9_2_9,
"q": 0.0_0_0_9_5,
"r": 0.0_7_5_8_7,
"s": 0.0_6_3_2_7,
"t": 0.0_9_3_5_6,
"u": 0.0_2_7_5_8,
"v": 0.0_0_9_7_8,
"w": 0.0_2_5_6_0,
"x": 0.0_0_1_5_0,
"y": 0.0_1_9_9_4,
"z": 0.0_0_0_7_7,
}
else:
# Custom frequencies dictionary
snake_case_ : str = frequencies_dict
if not case_sensitive:
snake_case_ : Union[str, Any] = ciphertext.lower()
# Chi squared statistic values
snake_case_ : dict[int, tuple[float, str]] = {}
# cycle through all of the shifts
for shift in range(len(lowerCAmelCase_ ) ):
snake_case_ : Tuple = ""
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
snake_case_ : str = (alphabet_letters.index(letter.lower() ) - shift) % len(
lowerCAmelCase_ )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
snake_case_ : List[Any] = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
snake_case_ : Optional[Any] = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
snake_case_ : Optional[Any] = decrypted_with_shift.lower().count(lowerCAmelCase_ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
snake_case_ : Union[str, Any] = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
snake_case_ : List[str] = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
snake_case_ : Any = decrypted_with_shift.count(lowerCAmelCase_ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
snake_case_ : Tuple = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
snake_case_ : Dict = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
snake_case_ : Tuple = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(lowerCAmelCase_: int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
snake_case_ : int = min(
lowerCAmelCase_ , key=lowerCAmelCase_ , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
snake_case_
) ,(
snake_case_
) ,
) : str = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
snake_case_ : List[str] = precision
snake_case_ : Union[str, Any] = ceil(precision / 1_4 )
snake_case_ : List[str] = 4_2_6_8_8_0 * Decimal(1_0_0_0_5 ).sqrt()
snake_case_ : str = 1
snake_case_ : List[str] = 1_3_5_9_1_4_0_9
snake_case_ : str = Decimal(lowerCAmelCase_ )
for k in range(1 , lowerCAmelCase_ ):
snake_case_ : Tuple = factorial(6 * k ) // (factorial(3 * k ) * factorial(lowerCAmelCase_ ) ** 3)
linear_term += 5_4_5_1_4_0_1_3_4
exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
UpperCAmelCase = 5_0
print(F"The first {n} digits of pi is: {pi(n)}")
| 666 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Optional[Any] , A__ : CLIPSegForImageSegmentation , A__ : CLIPSegProcessor , A__ : AutoencoderKL , A__ : CLIPTextModel , A__ : CLIPTokenizer , A__ : UNetaDConditionModel , A__ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , A__ : StableDiffusionSafetyChecker , A__ : CLIPImageProcessor , ) -> Optional[int]:
'''simple docstring'''
super().__init__()
if hasattr(scheduler.config , "steps_offset" ) and scheduler.config.steps_offset != 1:
snake_case_ : Union[str, Any] = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1" , "1.0.0" , A__ , standard_warn=A__ )
snake_case_ : int = dict(scheduler.config )
snake_case_ : str = 1
snake_case_ : int = FrozenDict(A__ )
if hasattr(scheduler.config , "skip_prk_steps" ) and scheduler.config.skip_prk_steps is False:
snake_case_ : Optional[Any] = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate("skip_prk_steps not set" , "1.0.0" , A__ , standard_warn=A__ )
snake_case_ : Dict = dict(scheduler.config )
snake_case_ : Any = True
snake_case_ : str = FrozenDict(A__ )
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )
self.register_modules(
segmentation_model=A__ , segmentation_processor=A__ , vae=A__ , text_encoder=A__ , tokenizer=A__ , unet=A__ , scheduler=A__ , safety_checker=A__ , feature_extractor=A__ , )
def UpperCAmelCase__ ( self : Tuple , A__ : Optional[Union[str, int]] = "auto" ) -> Optional[int]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
snake_case_ : int = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A__ )
def UpperCAmelCase__ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
self.enable_attention_slicing(A__ )
def UpperCAmelCase__ ( self : Tuple ) -> Any:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
snake_case_ : Optional[Any] = torch.device("cuda" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(A__ , A__ )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase__ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
if self.device != torch.device("meta" ) or not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(A__ , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self : str , A__ : Union[str, List[str]] , A__ : Union[torch.FloatTensor, PIL.Image.Image] , A__ : str , A__ : int = 5_12 , A__ : int = 5_12 , A__ : int = 50 , A__ : float = 7.5 , A__ : Optional[Union[str, List[str]]] = None , A__ : Optional[int] = 1 , A__ : float = 0.0 , A__ : Optional[torch.Generator] = None , A__ : Optional[torch.FloatTensor] = None , A__ : Optional[str] = "pil" , A__ : bool = True , A__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A__ : int = 1 , **A__ : List[Any] , ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.segmentation_processor(
text=[text] , images=[image] , padding="max_length" , return_tensors="pt" ).to(self.device )
snake_case_ : Optional[int] = self.segmentation_model(**A__ )
snake_case_ : Any = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
snake_case_ : Optional[Any] = self.numpy_to_pil(A__ )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
snake_case_ : int = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=A__ , image=A__ , mask_image=A__ , height=A__ , width=A__ , num_inference_steps=A__ , guidance_scale=A__ , negative_prompt=A__ , num_images_per_prompt=A__ , eta=A__ , generator=A__ , latents=A__ , output_type=A__ , return_dict=A__ , callback=A__ , callback_steps=A__ , )
| 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = fa + fa
snake_case_ ,snake_case_ : str = fa, f
index += 1
for _ in str(lowerCAmelCase_ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 666 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class snake_case__ ( unittest.TestCase ):
def __init__( self : List[Any] , A__ : int , A__ : Any=7 , A__ : Dict=3 , A__ : str=18 , A__ : List[str]=30 , A__ : Tuple=4_00 , A__ : int=True , A__ : Dict=None , A__ : List[str]=True , ) -> Any:
'''simple docstring'''
snake_case_ : Any = size if size is not None else {"height": 18, "width": 18}
snake_case_ : Optional[Any] = parent
snake_case_ : Tuple = batch_size
snake_case_ : Union[str, Any] = num_channels
snake_case_ : Any = image_size
snake_case_ : Tuple = min_resolution
snake_case_ : Optional[int] = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : Optional[int] = size
snake_case_ : str = apply_ocr
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class snake_case__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Any = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def UpperCAmelCase__ ( self : List[str] ) -> str:
'''simple docstring'''
snake_case_ : Any = LayoutLMvaImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : int ) -> Optional[int]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Any ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A__ , "do_resize" ) )
self.assertTrue(hasattr(A__ , "size" ) )
self.assertTrue(hasattr(A__ , "apply_ocr" ) )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 18} )
snake_case_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def UpperCAmelCase__ ( self : str ) -> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : int ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case_ : Any = image_processing(image_inputs[0] , return_tensors="pt" )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
self.assertIsInstance(encoding.words , A__ )
self.assertIsInstance(encoding.boxes , A__ )
# Test batched
snake_case_ : Optional[int] = image_processing(A__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , numpify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , np.ndarray )
# Test not batched input
snake_case_ : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(A__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , torchify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , torch.Tensor )
# Test not batched input
snake_case_ : Any = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
snake_case_ : Any = image_processing(A__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def UpperCAmelCase__ ( self : str ) -> int:
'''simple docstring'''
snake_case_ : Optional[Any] = LayoutLMvaImageProcessor()
from datasets import load_dataset
snake_case_ : Union[str, Any] = load_dataset("hf-internal-testing/fixtures_docvqa" , split="test" )
snake_case_ : Optional[int] = Image.open(ds[0]["file"] ).convert("RGB" )
snake_case_ : Tuple = image_processing(A__ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
snake_case_ : Optional[Any] = [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]] # noqa: E231
snake_case_ : Tuple = [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , A__ )
self.assertListEqual(encoding.boxes , A__ )
# with apply_OCR = False
snake_case_ : Dict = LayoutLMvaImageProcessor(apply_ocr=A__ )
snake_case_ : int = image_processing(A__ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
| 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(lowerCAmelCase_ )
or left < -len(lowerCAmelCase_ )
or right >= len(lowerCAmelCase_ )
or right < -len(lowerCAmelCase_ )
):
raise IndexError("list index out of range" )
if left == right:
return nums[left]
snake_case_ : List[Any] = (left + right) >> 1 # the middle
snake_case_ : Dict = find_max(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # find max in range[left, mid]
snake_case_ : int = find_max(lowerCAmelCase_ , mid + 1 , lowerCAmelCase_ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 666 | 1 |
UpperCAmelCase = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 666 | import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase = {
"vocab_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
),
},
"merges_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
),
},
"tokenizer_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
"roberta-base-openai-detector": (
"https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
),
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase = {
"roberta-base": 5_1_2,
"roberta-large": 5_1_2,
"roberta-large-mnli": 5_1_2,
"distilroberta-base": 5_1_2,
"roberta-base-openai-detector": 5_1_2,
"roberta-large-openai-detector": 5_1_2,
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE : int = ["input_ids", "attention_mask"]
_SCREAMING_SNAKE_CASE : List[str] = RobertaTokenizer
def __init__( self : Optional[int] , A__ : List[Any]=None , A__ : Optional[int]=None , A__ : List[str]=None , A__ : Dict="replace" , A__ : List[str]="<s>" , A__ : Optional[Any]="</s>" , A__ : List[str]="</s>" , A__ : List[Any]="<s>" , A__ : int="<unk>" , A__ : int="<pad>" , A__ : List[Any]="<mask>" , A__ : Any=False , A__ : Optional[int]=True , **A__ : Union[str, Any] , ) -> int:
'''simple docstring'''
super().__init__(
A__ , A__ , tokenizer_file=A__ , errors=A__ , bos_token=A__ , eos_token=A__ , sep_token=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , add_prefix_space=A__ , trim_offsets=A__ , **A__ , )
snake_case_ : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , A__ ) != add_prefix_space:
snake_case_ : List[Any] = getattr(A__ , pre_tok_state.pop("type" ) )
snake_case_ : Any = add_prefix_space
snake_case_ : List[Any] = pre_tok_class(**A__ )
snake_case_ : Optional[int] = add_prefix_space
snake_case_ : List[str] = "post_processor"
snake_case_ : Tuple = getattr(self.backend_tokenizer , A__ , A__ )
if tokenizer_component_instance:
snake_case_ : List[str] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
snake_case_ : str = tuple(state["sep"] )
if "cls" in state:
snake_case_ : Tuple = tuple(state["cls"] )
snake_case_ : Tuple = False
if state.get("add_prefix_space" , A__ ) != add_prefix_space:
snake_case_ : Optional[Any] = add_prefix_space
snake_case_ : str = True
if state.get("trim_offsets" , A__ ) != trim_offsets:
snake_case_ : Optional[int] = trim_offsets
snake_case_ : List[Any] = True
if changes_to_apply:
snake_case_ : int = getattr(A__ , state.pop("type" ) )
snake_case_ : List[Any] = component_class(**A__ )
setattr(self.backend_tokenizer , A__ , A__ )
@property
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Tuple , A__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else value
snake_case_ : Any = value
def UpperCAmelCase__ ( self : int , *A__ : Optional[Any] , **A__ : int ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Optional[Any] = kwargs.get("is_split_into_words" , A__ )
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(*A__ , **A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , *A__ : Any , **A__ : List[Any] ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Optional[int] = kwargs.get("is_split_into_words" , A__ )
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(*A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : str , A__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
snake_case_ : Optional[Any] = self._tokenizer.model.save(A__ , name=A__ )
return tuple(A__ )
def UpperCAmelCase__ ( self : int , A__ : List[str] , A__ : Union[str, Any]=None ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Dict , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case_ : str = [self.sep_token_id]
snake_case_ : List[Any] = [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 + sep + token_ids_a + sep ) * [0]
| 666 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCAmelCase = logging.get_logger(__name__)
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = ["pixel_values"]
def __init__( self : Dict , A__ : bool = True , A__ : int = 32 , A__ : Union[str, Any]=PILImageResampling.BILINEAR , A__ : bool = True , **A__ : Any , ) -> None:
'''simple docstring'''
snake_case_ : List[Any] = do_resize
snake_case_ : Tuple = do_rescale
snake_case_ : Optional[Any] = size_divisor
snake_case_ : List[Any] = resample
super().__init__(**A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : np.ndarray , A__ : int , A__ : List[Any] , A__ : Optional[ChannelDimension] = None , **A__ : List[Any] ) -> np.ndarray:
'''simple docstring'''
snake_case_ ,snake_case_ : Union[str, Any] = get_image_size(A__ )
# Rounds the height and width down to the closest multiple of size_divisor
snake_case_ : Optional[int] = height // size_divisor * size_divisor
snake_case_ : Dict = width // size_divisor * size_divisor
snake_case_ : Union[str, Any] = resize(A__ , (new_h, new_w) , resample=A__ , data_format=A__ , **A__ )
return image
def UpperCAmelCase__ ( self : str , A__ : np.ndarray , A__ : float , A__ : Optional[ChannelDimension] = None , **A__ : Union[str, Any] ) -> np.ndarray:
'''simple docstring'''
return rescale(image=A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , A__ : Optional[bool] = None , A__ : Optional[int] = None , A__ : Tuple=None , A__ : Optional[bool] = None , A__ : Optional[Union[TensorType, str]] = None , A__ : ChannelDimension = ChannelDimension.FIRST , **A__ : Optional[int] , ) -> BatchFeature:
'''simple docstring'''
snake_case_ : str = do_resize if do_resize is not None else self.do_resize
snake_case_ : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : List[str] = size_divisor if size_divisor is not None else self.size_divisor
snake_case_ : Any = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("size_divisor is required for resizing" )
snake_case_ : List[Any] = make_list_of_images(A__ )
if not valid_images(A__ ):
raise ValueError("Invalid image(s)" )
# All transformations expect numpy arrays.
snake_case_ : List[str] = [to_numpy_array(A__ ) for img in images]
if do_resize:
snake_case_ : List[str] = [self.resize(A__ , size_divisor=A__ , resample=A__ ) for image in images]
if do_rescale:
snake_case_ : str = [self.rescale(A__ , scale=1 / 2_55 ) for image in images]
snake_case_ : Tuple = [to_channel_dimension_format(A__ , A__ ) for image in images]
snake_case_ : Dict = {"pixel_values": images}
return BatchFeature(data=A__ , tensor_type=A__ )
| 666 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class snake_case__ ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Dict = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base" )
snake_case_ : Any = {
"input_ids": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
snake_case_ : List[str] = model(A__ )["last_hidden_state"]
snake_case_ : str = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , A__ )
# compare the actual values for a slice.
snake_case_ : List[str] = tf.convert_to_tensor(
[
[
[0.068_1762, 0.1089_4451, 0.0677_2504],
[-0.0642_3668, 0.0236_6615, 0.0432_9344],
[-0.0605_7295, 0.0997_4135, -0.0007_0584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
snake_case_ : str = len(lowerCAmelCase_ )
snake_case_ : Optional[int] = len(lowerCAmelCase_ )
snake_case_ : Any = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
snake_case_ : Union[str, Any] = True
for i in range(lowerCAmelCase_ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
snake_case_ : Optional[Any] = True
if a[i].islower():
snake_case_ : Optional[Any] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCAmelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = ["pixel_values"]
def __init__( self : Union[str, Any] , A__ : bool = True , A__ : Dict[str, int] = None , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : bool = True , A__ : Dict[str, int] = None , A__ : bool = True , A__ : Union[int, float] = 1 / 2_55 , A__ : bool = True , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : bool = True , **A__ : Optional[int] , ) -> None:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : str = size if size is not None else {"shortest_edge": 2_24}
snake_case_ : Union[str, Any] = get_size_dict(A__ , default_to_square=A__ )
snake_case_ : List[Any] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
snake_case_ : Dict = get_size_dict(A__ , default_to_square=A__ , param_name="crop_size" )
snake_case_ : str = do_resize
snake_case_ : str = size
snake_case_ : Optional[Any] = resample
snake_case_ : Any = do_center_crop
snake_case_ : Any = crop_size
snake_case_ : str = do_rescale
snake_case_ : Optional[Any] = rescale_factor
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
snake_case_ : List[str] = image_std if image_std is not None else OPENAI_CLIP_STD
snake_case_ : int = do_convert_rgb
def UpperCAmelCase__ ( self : Optional[int] , A__ : np.ndarray , A__ : Dict[str, int] , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[str] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : str = get_size_dict(A__ , default_to_square=A__ )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
snake_case_ : str = get_resize_output_image_size(A__ , size=size["shortest_edge"] , default_to_square=A__ )
return resize(A__ , size=A__ , resample=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : np.ndarray , A__ : Dict[str, int] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[Any] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Optional[int] = get_size_dict(A__ )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
return center_crop(A__ , size=(size["height"], size["width"]) , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Optional[Any] , A__ : np.ndarray , A__ : Union[int, float] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[str] , ) -> str:
'''simple docstring'''
return rescale(A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Any , A__ : np.ndarray , A__ : Union[float, List[float]] , A__ : Union[float, List[float]] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Any , ) -> np.ndarray:
'''simple docstring'''
return normalize(A__ , mean=A__ , std=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : List[Any] , A__ : ImageInput , A__ : bool = None , A__ : Dict[str, int] = None , A__ : PILImageResampling = None , A__ : bool = None , A__ : int = None , A__ : bool = None , A__ : float = None , A__ : bool = None , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : bool = None , A__ : Optional[Union[str, TensorType]] = None , A__ : Optional[ChannelDimension] = ChannelDimension.FIRST , **A__ : Optional[Any] , ) -> PIL.Image.Image:
'''simple docstring'''
snake_case_ : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case_ : Union[str, Any] = size if size is not None else self.size
snake_case_ : Any = get_size_dict(A__ , param_name="size" , default_to_square=A__ )
snake_case_ : Optional[int] = resample if resample is not None else self.resample
snake_case_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ : List[str] = crop_size if crop_size is not None else self.crop_size
snake_case_ : Tuple = get_size_dict(A__ , param_name="crop_size" , default_to_square=A__ )
snake_case_ : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ : Any = image_mean if image_mean is not None else self.image_mean
snake_case_ : List[str] = image_std if image_std is not None else self.image_std
snake_case_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
snake_case_ : List[Any] = make_list_of_images(A__ )
if not valid_images(A__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
snake_case_ : Dict = [convert_to_rgb(A__ ) for image in images]
# All transformations expect numpy arrays.
snake_case_ : Dict = [to_numpy_array(A__ ) for image in images]
if do_resize:
snake_case_ : Dict = [self.resize(image=A__ , size=A__ , resample=A__ ) for image in images]
if do_center_crop:
snake_case_ : Tuple = [self.center_crop(image=A__ , size=A__ ) for image in images]
if do_rescale:
snake_case_ : str = [self.rescale(image=A__ , scale=A__ ) for image in images]
if do_normalize:
snake_case_ : int = [self.normalize(image=A__ , mean=A__ , std=A__ ) for image in images]
snake_case_ : List[Any] = [to_channel_dimension_format(A__ , A__ ) for image in images]
snake_case_ : Tuple = {"pixel_values": images}
return BatchFeature(data=A__ , tensor_type=A__ )
| 666 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Union[str, Any] ):
snake_case_ : Any = SwinConfig(
embed_dim=1_9_2 , depths=(2, 2, 1_8, 2) , num_heads=(6, 1_2, 2_4, 4_8) , window_size=1_2 , out_features=["stage2", "stage3", "stage4"] , )
snake_case_ : Tuple = DetaConfig(
backbone_config=lowerCAmelCase_ , num_queries=9_0_0 , encoder_ffn_dim=2_0_4_8 , decoder_ffn_dim=2_0_4_8 , num_feature_levels=5 , assign_first_stage=lowerCAmelCase_ , with_box_refine=lowerCAmelCase_ , two_stage=lowerCAmelCase_ , )
# set labels
snake_case_ : int = "huggingface/label-files"
if "o365" in model_name:
snake_case_ : List[Any] = 3_6_6
snake_case_ : int = "object365-id2label.json"
else:
snake_case_ : Optional[Any] = 9_1
snake_case_ : Any = "coco-detection-id2label.json"
snake_case_ : str = num_labels
snake_case_ : Union[str, Any] = json.load(open(cached_download(hf_hub_url(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="dataset" ) ) , "r" ) )
snake_case_ : int = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
snake_case_ : str = idalabel
snake_case_ : Any = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Dict = []
# stem
# fmt: off
rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight") )
rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias") )
if i < 3:
rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight") )
rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias") )
rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight") )
rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias") )
rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight") )
rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias") )
rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight") )
rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias") )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias") )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias") )
# fmt: on
return rename_keys
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: List[str] ):
snake_case_ : Dict = dct.pop(lowerCAmelCase_ )
snake_case_ : Dict = val
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: List[Any] ):
snake_case_ : int = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
snake_case_ : str = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
snake_case_ : Optional[Any] = state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight" )
snake_case_ : str = state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : str = in_proj_weight[:dim, :]
snake_case_ : List[str] = in_proj_bias[: dim]
snake_case_ : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
snake_case_ : Any = in_proj_bias[
dim : dim * 2
]
snake_case_ : Optional[Any] = in_proj_weight[
-dim :, :
]
snake_case_ : Optional[Any] = in_proj_bias[-dim :]
# fmt: on
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Dict , lowerCAmelCase_: Union[str, Any] ):
# transformer decoder self-attention layers
snake_case_ : str = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
snake_case_ : List[Any] = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
snake_case_ : str = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Optional[Any] = in_proj_weight[:hidden_size, :]
snake_case_ : Optional[Any] = in_proj_bias[:hidden_size]
snake_case_ : Optional[Any] = in_proj_weight[
hidden_size : hidden_size * 2, :
]
snake_case_ : Union[str, Any] = in_proj_bias[hidden_size : hidden_size * 2]
snake_case_ : Any = in_proj_weight[-hidden_size:, :]
snake_case_ : Optional[Any] = in_proj_bias[-hidden_size:]
def SCREAMING_SNAKE_CASE_ ( ):
snake_case_ : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
snake_case_ : Tuple = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: int , lowerCAmelCase_: Optional[Any] ):
snake_case_ : Any = get_deta_config(lowerCAmelCase_ )
# load original state dict
if model_name == "deta-swin-large":
snake_case_ : int = hf_hub_download(repo_id="nielsr/deta-checkpoints" , filename="adet_swin_ft.pth" )
elif model_name == "deta-swin-large-o365":
snake_case_ : Dict = hf_hub_download(repo_id="jozhang97/deta-swin-l-o365" , filename="deta_swin_pt_o365.pth" )
else:
raise ValueError(f"Model name {model_name} not supported" )
snake_case_ : int = torch.load(lowerCAmelCase_ , map_location="cpu" )["model"]
# original state dict
for name, param in state_dict.items():
print(lowerCAmelCase_ , param.shape )
# rename keys
snake_case_ : str = create_rename_keys(lowerCAmelCase_ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
read_in_swin_q_k_v(lowerCAmelCase_ , config.backbone_config )
read_in_decoder_q_k_v(lowerCAmelCase_ , lowerCAmelCase_ )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
snake_case_ : Dict = state_dict.pop(lowerCAmelCase_ )
snake_case_ : Optional[int] = val
if "input_proj" in key:
snake_case_ : Optional[Any] = state_dict.pop(lowerCAmelCase_ )
snake_case_ : Dict = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
snake_case_ : Union[str, Any] = state_dict.pop(lowerCAmelCase_ )
snake_case_ : str = val
# finally, create HuggingFace model and load state dict
snake_case_ : int = DetaForObjectDetection(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
model.eval()
snake_case_ : Any = "cuda" if torch.cuda.is_available() else "cpu"
model.to(lowerCAmelCase_ )
# load image processor
snake_case_ : List[str] = DetaImageProcessor(format="coco_detection" )
# verify our conversion on image
snake_case_ : Optional[Any] = prepare_img()
snake_case_ : int = processor(images=lowerCAmelCase_ , return_tensors="pt" )
snake_case_ : List[Any] = encoding["pixel_values"]
snake_case_ : List[Any] = model(pixel_values.to(lowerCAmelCase_ ) )
# verify logits
print("Logits:" , outputs.logits[0, :3, :3] )
print("Boxes:" , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
snake_case_ : Any = torch.tensor(
[[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]] )
snake_case_ : Any = torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]] )
elif model_name == "deta-swin-large-o365":
snake_case_ : List[str] = torch.tensor(
[[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]] )
snake_case_ : str = torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(lowerCAmelCase_ ) , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(lowerCAmelCase_ ) , atol=1e-4 )
print("Everything ok!" )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..." )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
# Push to hub
if push_to_hub:
print("Pushing model and processor to hub..." )
model.push_to_hub(f"jozhang97/{model_name}" )
processor.push_to_hub(f"jozhang97/{model_name}" )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
type=str,
default="deta-swin-large",
choices=["deta-swin-large", "deta-swin-large-o365"],
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
help="Path to the folder to output PyTorch model.",
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCAmelCase = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
snake_case_ : Union[str, Any] = []
for position in positions:
snake_case_ ,snake_case_ : Union[str, Any] = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(lowerCAmelCase_ )
return permissible_positions
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[list[int]] ):
return not any(elem == 0 for row in board for elem in row )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[list[int]] , lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
if is_complete(lowerCAmelCase_ ):
return True
for position in get_valid_pos(lowerCAmelCase_ , len(lowerCAmelCase_ ) ):
snake_case_ ,snake_case_ : Dict = position
if board[y][x] == 0:
snake_case_ : List[str] = curr + 1
if open_knight_tour_helper(lowerCAmelCase_ , lowerCAmelCase_ , curr + 1 ):
return True
snake_case_ : Dict = 0
return False
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = [[0 for i in range(lowerCAmelCase_ )] for j in range(lowerCAmelCase_ )]
for i in range(lowerCAmelCase_ ):
for j in range(lowerCAmelCase_ ):
snake_case_ : Optional[Any] = 1
if open_knight_tour_helper(lowerCAmelCase_ , (i, j) , 1 ):
return board
snake_case_ : Dict = 0
snake_case_ : str = f"Open Kight Tour cannot be performed on a board of size {n}"
raise ValueError(lowerCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | 1 |
from math import loga
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , A__ : Any=24 , A__ : List[Any]=16 , A__ : List[Any]=40_96 , A__ : int="gelu" , A__ : List[str]=0.1 , A__ : List[str]=0.1 , A__ : str=5_12 , A__ : int=0.02 , A__ : Any=1E-12 , A__ : Optional[Any]=0 , A__ : List[str]=2 , A__ : Optional[int]=1 , A__ : str="absolute" , A__ : Any=True , **A__ : Optional[Any] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
snake_case_ : str = vocab_size
snake_case_ : int = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = intermediate_size
snake_case_ : str = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : str = max_position_embeddings
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Dict = use_cache
| 666 | 1 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
# See all BART models at https://huggingface.co/models?filter=bart
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = "bart"
_SCREAMING_SNAKE_CASE : Optional[Any] = ["past_key_values"]
_SCREAMING_SNAKE_CASE : List[Any] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Optional[int] , A__ : Tuple=5_02_65 , A__ : Dict=10_24 , A__ : Optional[int]=12 , A__ : Tuple=40_96 , A__ : str=16 , A__ : Any=12 , A__ : List[str]=40_96 , A__ : int=16 , A__ : str=0.0 , A__ : Union[str, Any]=0.0 , A__ : List[str]="gelu" , A__ : Optional[Any]=10_24 , A__ : Optional[int]=0.1 , A__ : Optional[Any]=0.0 , A__ : List[str]=0.0 , A__ : List[Any]=0.02 , A__ : Dict=0.0 , A__ : Optional[Any]=False , A__ : Optional[int]=True , A__ : Optional[Any]=3 , A__ : List[Any]=1 , A__ : str=0 , A__ : List[Any]=2 , A__ : Any=True , A__ : Optional[Any]=2 , A__ : Dict=2 , **A__ : Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = vocab_size
snake_case_ : int = max_position_embeddings
snake_case_ : List[Any] = d_model
snake_case_ : Dict = encoder_ffn_dim
snake_case_ : Optional[int] = encoder_layers
snake_case_ : Any = encoder_attention_heads
snake_case_ : Any = decoder_ffn_dim
snake_case_ : Dict = decoder_layers
snake_case_ : int = decoder_attention_heads
snake_case_ : Union[str, Any] = dropout
snake_case_ : str = attention_dropout
snake_case_ : List[str] = activation_dropout
snake_case_ : int = activation_function
snake_case_ : Optional[Any] = init_std
snake_case_ : List[str] = encoder_layerdrop
snake_case_ : Tuple = decoder_layerdrop
snake_case_ : Dict = classifier_dropout
snake_case_ : List[str] = use_cache
snake_case_ : Optional[int] = encoder_layers
snake_case_ : Any = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=A__ , pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , is_encoder_decoder=A__ , decoder_start_token_id=A__ , forced_eos_token_id=A__ , **A__ , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" , A__ ):
snake_case_ : Any = self.bos_token_id
warnings.warn(
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
"The config can simply be saved and uploaded again to be fixed." )
class snake_case__ ( _UpperCamelCase ):
@property
def UpperCAmelCase__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case_ : str = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
snake_case_ : List[str] = {0: "batch"}
snake_case_ : int = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
snake_case_ : Any = {0: "batch", 1: "decoder_sequence"}
snake_case_ : List[Any] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(A__ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case_ : Tuple = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
snake_case_ ,snake_case_ : str = self.num_layers
for i in range(A__ ):
snake_case_ : Any = {0: "batch", 2: "past_sequence + sequence"}
snake_case_ : Union[str, Any] = {0: "batch", 2: "past_sequence + sequence"}
else:
snake_case_ : str = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
def UpperCAmelCase__ ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case_ : Optional[int] = super().outputs
else:
snake_case_ : str = super(A__ , self ).outputs
if self.use_past:
snake_case_ ,snake_case_ : int = self.num_layers
for i in range(A__ ):
snake_case_ : List[str] = {0: "batch", 2: "past_sequence + sequence"}
snake_case_ : Tuple = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def UpperCAmelCase__ ( self : Tuple , A__ : PreTrainedTokenizer , A__ : int = -1 , A__ : int = -1 , A__ : bool = False , A__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A__ , A__ , A__ , A__ , A__ )
# Generate decoder inputs
snake_case_ : Optional[Any] = seq_length if not self.use_past else 1
snake_case_ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A__ , A__ , A__ , A__ , A__ )
snake_case_ : List[str] = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
snake_case_ : Dict = dict(**A__ , **A__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
snake_case_ ,snake_case_ : Optional[Any] = common_inputs["input_ids"].shape
snake_case_ : List[Any] = common_inputs["decoder_input_ids"].shape[1]
snake_case_ ,snake_case_ : int = self.num_attention_heads
snake_case_ : int = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ : int = decoder_seq_length + 3
snake_case_ : List[Any] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case_ : List[str] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(A__ , A__ )] , dim=1 )
snake_case_ : str = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case_ ,snake_case_ : List[Any] = self.num_layers
snake_case_ : str = min(A__ , A__ )
snake_case_ : List[str] = max(A__ , A__ ) - min_num_layers
snake_case_ : Dict = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(A__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(A__ ),
torch.zeros(A__ ),
torch.zeros(A__ ),
torch.zeros(A__ ),
) )
# TODO: test this.
snake_case_ : Dict = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(A__ , A__ ):
common_inputs["past_key_values"].append((torch.zeros(A__ ), torch.zeros(A__ )) )
return common_inputs
def UpperCAmelCase__ ( self : Optional[Any] , A__ : PreTrainedTokenizer , A__ : int = -1 , A__ : int = -1 , A__ : bool = False , A__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : List[str] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A__ , A__ , A__ , A__ , A__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
snake_case_ ,snake_case_ : List[str] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
snake_case_ : Optional[Any] = seqlen + 2
snake_case_ ,snake_case_ : List[str] = self.num_layers
snake_case_ ,snake_case_ : List[Any] = self.num_attention_heads
snake_case_ : Optional[int] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ : List[str] = common_inputs["attention_mask"].dtype
snake_case_ : int = torch.cat(
[common_inputs["attention_mask"], torch.ones(A__ , A__ , dtype=A__ )] , dim=1 )
snake_case_ : Dict = [
(torch.zeros(A__ ), torch.zeros(A__ )) for _ in range(A__ )
]
return common_inputs
def UpperCAmelCase__ ( self : List[str] , A__ : PreTrainedTokenizer , A__ : int = -1 , A__ : int = -1 , A__ : bool = False , A__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = compute_effective_axis_dimension(
A__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ : List[Any] = tokenizer.num_special_tokens_to_add(A__ )
snake_case_ : Any = compute_effective_axis_dimension(
A__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=A__ )
# Generate dummy inputs according to compute batch and sequence
snake_case_ : Union[str, Any] = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case_ : Dict = dict(tokenizer(A__ , return_tensors=A__ ) )
return common_inputs
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : PreTrainedTokenizer , A__ : int = -1 , A__ : int = -1 , A__ : bool = False , A__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case_ : Union[str, Any] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
A__ , batch_size=A__ , seq_length=A__ , is_pair=A__ , framework=A__ )
elif self.task == "causal-lm":
snake_case_ : List[str] = self._generate_dummy_inputs_for_causal_lm(
A__ , batch_size=A__ , seq_length=A__ , is_pair=A__ , framework=A__ )
else:
snake_case_ : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
A__ , batch_size=A__ , seq_length=A__ , is_pair=A__ , framework=A__ )
return common_inputs
def UpperCAmelCase__ ( self : Optional[Any] , A__ : Any , A__ : Optional[int] , A__ : Optional[int] , A__ : List[str] ) -> List[str]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
snake_case_ : Any = super()._flatten_past_key_values_(A__ , A__ , A__ , A__ )
else:
snake_case_ : Optional[Any] = super(A__ , self )._flatten_past_key_values_(
A__ , A__ , A__ , A__ )
| 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : str = [True] * (end + 1)
snake_case_ : Any = []
while start <= end:
if temp[start] is True:
in_prime.append(lowerCAmelCase_ )
for i in range(start * start , end + 1 , lowerCAmelCase_ ):
snake_case_ : Union[str, Any] = False
start += 1
prime += in_prime
snake_case_ : Dict = end + 1
snake_case_ : Dict = min(2 * end , lowerCAmelCase_ )
while low <= n:
snake_case_ : Any = [True] * (high - low + 1)
for each in in_prime:
snake_case_ : Optional[Any] = math.floor(low / each ) * each
if t < low:
t += each
for j in range(lowerCAmelCase_ , high + 1 , lowerCAmelCase_ ):
snake_case_ : List[Any] = False
for j in range(len(lowerCAmelCase_ ) ):
if temp[j] is True:
prime.append(j + low )
snake_case_ : int = high + 1
snake_case_ : Union[str, Any] = min(high + end , lowerCAmelCase_ )
return prime
print(sieve(1_0**6))
| 666 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Union[str, Any] , A__ : VQModel , A__ : UNetaDModel , A__ : DDIMScheduler ) -> List[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=A__ , unet=A__ , scheduler=A__ )
@torch.no_grad()
def __call__( self : str , A__ : int = 1 , A__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , A__ : float = 0.0 , A__ : int = 50 , A__ : Optional[str] = "pil" , A__ : bool = True , **A__ : Optional[Any] , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
snake_case_ : Optional[int] = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=A__ , )
snake_case_ : List[Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
snake_case_ : Any = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(A__ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
snake_case_ : Union[str, Any] = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case_ : List[Any] = {}
if accepts_eta:
snake_case_ : int = eta
for t in self.progress_bar(self.scheduler.timesteps ):
snake_case_ : Union[str, Any] = self.scheduler.scale_model_input(A__ , A__ )
# predict the noise residual
snake_case_ : Dict = self.unet(A__ , A__ ).sample
# compute the previous noisy sample x_t -> x_t-1
snake_case_ : Union[str, Any] = self.scheduler.step(A__ , A__ , A__ , **A__ ).prev_sample
# decode the image latents with the VAE
snake_case_ : int = self.vqvae.decode(A__ ).sample
snake_case_ : Dict = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ : Optional[int] = self.numpy_to_pil(A__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A__ )
| 666 | 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 ConditionalDetrImageProcessor
class snake_case__ ( unittest.TestCase ):
def __init__( self : List[str] , A__ : List[Any] , A__ : int=7 , A__ : Union[str, Any]=3 , A__ : List[str]=30 , A__ : Optional[int]=4_00 , A__ : Optional[Any]=True , A__ : Optional[int]=None , A__ : Optional[Any]=True , A__ : Any=[0.5, 0.5, 0.5] , A__ : int=[0.5, 0.5, 0.5] , A__ : Any=True , A__ : int=1 / 2_55 , A__ : List[str]=True , ) -> Dict:
'''simple docstring'''
snake_case_ : int = size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
snake_case_ : Any = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : List[Any] = max_resolution
snake_case_ : Tuple = do_resize
snake_case_ : Dict = size
snake_case_ : Optional[Any] = do_normalize
snake_case_ : int = image_mean
snake_case_ : List[Any] = image_std
snake_case_ : Tuple = do_rescale
snake_case_ : Any = rescale_factor
snake_case_ : Optional[int] = do_pad
def UpperCAmelCase__ ( self : int ) -> List[str]:
'''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 UpperCAmelCase__ ( self : Optional[int] , A__ : Optional[int] , A__ : Any=False ) -> Optional[Any]:
'''simple docstring'''
if not batched:
snake_case_ : Any = image_inputs[0]
if isinstance(A__ , Image.Image ):
snake_case_ ,snake_case_ : Dict = image.size
else:
snake_case_ ,snake_case_ : int = image.shape[1], image.shape[2]
if w < h:
snake_case_ : Dict = int(self.size["shortest_edge"] * h / w )
snake_case_ : Optional[int] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : List[Any] = self.size["shortest_edge"]
else:
snake_case_ : str = []
for image in image_inputs:
snake_case_ ,snake_case_ : Tuple = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : List[Any] = max(A__ , key=lambda A__ : item[0] )[0]
snake_case_ : int = max(A__ , key=lambda A__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = ConditionalDetrImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = ConditionalDetrImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Any ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A__ , "image_mean" ) )
self.assertTrue(hasattr(A__ , "image_std" ) )
self.assertTrue(hasattr(A__ , "do_normalize" ) )
self.assertTrue(hasattr(A__ , "do_resize" ) )
self.assertTrue(hasattr(A__ , "size" ) )
def UpperCAmelCase__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = 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 , A__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , A__ )
def UpperCAmelCase__ ( self : str ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : Optional[Any] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ ,snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
snake_case_ : int = image_processing(A__ , 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 UpperCAmelCase__ ( self : int ) -> Any:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , numpify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , np.ndarray )
# Test not batched input
snake_case_ : int = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : List[str] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Optional[int] = image_processing(A__ , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : Dict = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self : Tuple ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , torchify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , torch.Tensor )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Any = image_processing(A__ , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : int = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : Optional[Any] = json.loads(f.read() )
snake_case_ : int = {"image_id": 3_97_69, "annotations": target}
# encode them
snake_case_ : Optional[int] = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
snake_case_ : Any = image_processing(images=A__ , annotations=A__ , return_tensors="pt" )
# verify pixel values
snake_case_ : List[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , A__ )
snake_case_ : List[str] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , A__ , atol=1E-4 ) )
# verify area
snake_case_ : Tuple = 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"] , A__ ) )
# verify boxes
snake_case_ : Any = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , A__ )
snake_case_ : str = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , A__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , A__ ) )
# verify is_crowd
snake_case_ : Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , A__ ) )
# verify class_labels
snake_case_ : Any = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , A__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , A__ ) )
# verify size
snake_case_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , A__ ) )
@slow
def UpperCAmelCase__ ( self : int ) -> str:
'''simple docstring'''
snake_case_ : Tuple = 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_ : Any = json.loads(f.read() )
snake_case_ : Optional[Any] = {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}
snake_case_ : int = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : Union[str, Any] = ConditionalDetrImageProcessor(format="coco_panoptic" )
snake_case_ : str = image_processing(images=A__ , annotations=A__ , masks_path=A__ , return_tensors="pt" )
# verify pixel values
snake_case_ : int = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , A__ )
snake_case_ : str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , A__ , atol=1E-4 ) )
# verify area
snake_case_ : Optional[int] = 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"] , A__ ) )
# verify boxes
snake_case_ : str = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , A__ )
snake_case_ : str = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , A__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , A__ ) )
# verify is_crowd
snake_case_ : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , A__ ) )
# verify class_labels
snake_case_ : Optional[int] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , A__ ) )
# verify masks
snake_case_ : Union[str, Any] = 82_28_73
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , A__ )
# verify orig_size
snake_case_ : Dict = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , A__ ) )
# verify size
snake_case_ : str = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , A__ ) )
| 666 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple , lowerCAmelCase_: Optional[int] , lowerCAmelCase_: int ):
hf_model.apply_weight_norm()
snake_case_ : Any = checkpoint["input_conv.weight_g"]
snake_case_ : Union[str, Any] = checkpoint["input_conv.weight_v"]
snake_case_ : List[Any] = checkpoint["input_conv.bias"]
for i in range(len(config.upsample_rates ) ):
snake_case_ : str = checkpoint[f"upsamples.{i}.1.weight_g"]
snake_case_ : List[Any] = checkpoint[f"upsamples.{i}.1.weight_v"]
snake_case_ : Optional[int] = checkpoint[f"upsamples.{i}.1.bias"]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
snake_case_ : Tuple = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
snake_case_ : Dict = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
snake_case_ : Dict = checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]
snake_case_ : Union[str, Any] = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
snake_case_ : int = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
snake_case_ : Dict = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]
snake_case_ : Any = checkpoint["output_conv.1.weight_g"]
snake_case_ : Optional[int] = checkpoint["output_conv.1.weight_v"]
snake_case_ : str = checkpoint["output_conv.1.bias"]
hf_model.remove_weight_norm()
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] , lowerCAmelCase_: Dict , lowerCAmelCase_: Optional[int] , lowerCAmelCase_: Dict=None , lowerCAmelCase_: Tuple=None , ):
if config_path is not None:
snake_case_ : int = SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase_ )
else:
snake_case_ : Tuple = SpeechTaHifiGanConfig()
snake_case_ : Dict = SpeechTaHifiGan(lowerCAmelCase_ )
snake_case_ : int = torch.load(lowerCAmelCase_ )
load_weights(orig_checkpoint["model"]["generator"] , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : str = np.load(lowerCAmelCase_ )
snake_case_ : int = stats[0].reshape(-1 )
snake_case_ : Optional[Any] = stats[1].reshape(-1 )
snake_case_ : List[str] = torch.from_numpy(lowerCAmelCase_ ).float()
snake_case_ : Union[str, Any] = torch.from_numpy(lowerCAmelCase_ ).float()
model.save_pretrained(lowerCAmelCase_ )
if repo_id:
print("Pushing to the hub..." )
model.push_to_hub(lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
UpperCAmelCase = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class snake_case__ :
_SCREAMING_SNAKE_CASE : str = field(
default=_UpperCamelCase , metadata={"help": "Model type selected in the list: " + ", ".join(_UpperCamelCase )} )
_SCREAMING_SNAKE_CASE : str = field(
default=_UpperCamelCase , metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
_SCREAMING_SNAKE_CASE : int = field(
default=1_2_8 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_SCREAMING_SNAKE_CASE : int = field(
default=1_2_8 , metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."} , )
_SCREAMING_SNAKE_CASE : int = field(
default=6_4 , metadata={
"help": (
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
)
} , )
_SCREAMING_SNAKE_CASE : int = field(
default=3_0 , metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
} , )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
_SCREAMING_SNAKE_CASE : float = field(
default=0.0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
_SCREAMING_SNAKE_CASE : int = field(
default=2_0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
_SCREAMING_SNAKE_CASE : int = field(
default=0 , metadata={
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
} , )
_SCREAMING_SNAKE_CASE : int = field(default=1 , metadata={"help": "multiple threads for converting example to features"} )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Tuple = "train"
_SCREAMING_SNAKE_CASE : Any = "dev"
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : SquadDataTrainingArguments
_SCREAMING_SNAKE_CASE : List[SquadFeatures]
_SCREAMING_SNAKE_CASE : Split
_SCREAMING_SNAKE_CASE : bool
def __init__( self : str , A__ : SquadDataTrainingArguments , A__ : PreTrainedTokenizer , A__ : Optional[int] = None , A__ : Union[str, Split] = Split.train , A__ : Optional[bool] = False , A__ : Optional[str] = None , A__ : Optional[str] = "pt" , ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = args
snake_case_ : int = is_language_sensitive
snake_case_ : int = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(A__ , A__ ):
try:
snake_case_ : List[str] = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
snake_case_ : Tuple = mode
# Load data features from cache or dataset file
snake_case_ : Dict = "v2" if args.version_2_with_negative else "v1"
snake_case_ : List[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case_ : List[Any] = cached_features_file + ".lock"
with FileLock(A__ ):
if os.path.exists(A__ ) and not args.overwrite_cache:
snake_case_ : int = time.time()
snake_case_ : List[Any] = torch.load(A__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case_ : Tuple = self.old_features["features"]
snake_case_ : List[str] = self.old_features.get("dataset" , A__ )
snake_case_ : Tuple = self.old_features.get("examples" , A__ )
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
" future run" )
else:
if mode == Split.dev:
snake_case_ : Tuple = self.processor.get_dev_examples(args.data_dir )
else:
snake_case_ : Tuple = self.processor.get_train_examples(args.data_dir )
snake_case_ ,snake_case_ : Optional[Any] = squad_convert_examples_to_features(
examples=self.examples , tokenizer=A__ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=A__ , )
snake_case_ : Any = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , A__ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )
def __len__( self : str ) -> Dict:
'''simple docstring'''
return len(self.features )
def __getitem__( self : Optional[int] , A__ : Optional[int] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
snake_case_ : Any = self.features[i]
snake_case_ : Optional[int] = torch.tensor(feature.input_ids , dtype=torch.long )
snake_case_ : Union[str, Any] = torch.tensor(feature.attention_mask , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.token_type_ids , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.cls_index , dtype=torch.long )
snake_case_ : str = torch.tensor(feature.p_mask , dtype=torch.float )
snake_case_ : str = torch.tensor(feature.is_impossible , dtype=torch.float )
snake_case_ : Optional[int] = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case_ : Any = torch.tensor(feature.start_position , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 666 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
] , )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: Any ):
snake_case_ : Any = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json" , "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
snake_case_ : str = DatasetInfosDict.from_directory(lowerCAmelCase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"dataset_info" , [
DatasetInfo(),
DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=4_2 , ),
] , )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: DatasetInfo ):
snake_case_ : Optional[Any] = str(lowerCAmelCase_ )
dataset_info.write_to_directory(lowerCAmelCase_ )
snake_case_ : List[str] = DatasetInfo.from_directory(lowerCAmelCase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowerCAmelCase_ , "dataset_info.json" ) )
def SCREAMING_SNAKE_CASE_ ( ):
snake_case_ : Union[str, Any] = DatasetInfo(
description="foo" , citation="bar" , homepage="https://foo.bar" , license="CC0" , features=Features({"a": Value("int32" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train", "num_examples": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
snake_case_ : Any = dataset_info._to_yaml_dict()
assert sorted(lowerCAmelCase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
snake_case_ : int = yaml.safe_dump(lowerCAmelCase_ )
snake_case_ : int = yaml.safe_load(lowerCAmelCase_ )
assert dataset_info_yaml_dict == reloaded
def SCREAMING_SNAKE_CASE_ ( ):
snake_case_ : Union[str, Any] = DatasetInfo()
snake_case_ : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict" , [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=4_2 ),
"v2": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: DatasetInfosDict ):
snake_case_ : Tuple = str(lowerCAmelCase_ )
dataset_infos_dict.write_to_directory(lowerCAmelCase_ )
snake_case_ : Any = DatasetInfosDict.from_directory(lowerCAmelCase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
snake_case_ : Union[str, Any] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
snake_case_ : List[str] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowerCAmelCase_ , "README.md" ) )
| 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "git_vision_model"
def __init__( self : int , A__ : Union[str, Any]=7_68 , A__ : List[Any]=30_72 , A__ : Tuple=12 , A__ : Optional[Any]=12 , A__ : Optional[int]=3 , A__ : List[str]=2_24 , A__ : Dict=16 , A__ : int="quick_gelu" , A__ : Any=1E-5 , A__ : Tuple=0.0 , A__ : Optional[int]=0.02 , **A__ : List[str] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : str = intermediate_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = patch_size
snake_case_ : List[str] = image_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Any = attention_dropout
snake_case_ : Any = layer_norm_eps
snake_case_ : int = hidden_act
@classmethod
def UpperCAmelCase__ ( cls : List[Any] , A__ : Union[str, os.PathLike] , **A__ : Optional[int] ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(A__ )
snake_case_ ,snake_case_ : Tuple = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
snake_case_ : Any = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(A__ , **A__ )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = "git"
def __init__( self : Any , A__ : List[str]=None , A__ : List[str]=3_05_22 , A__ : Tuple=7_68 , A__ : Tuple=6 , A__ : str=12 , A__ : Any=30_72 , A__ : List[str]="gelu" , A__ : int=0.1 , A__ : Dict=0.1 , A__ : Any=10_24 , A__ : Optional[Any]=0.02 , A__ : Optional[Any]=1E-12 , A__ : Dict=0 , A__ : Any="absolute" , A__ : Tuple=True , A__ : Any=False , A__ : Tuple=1_01 , A__ : Tuple=1_02 , A__ : List[Any]=None , **A__ : List[str] , ) -> int:
'''simple docstring'''
super().__init__(bos_token_id=A__ , eos_token_id=A__ , pad_token_id=A__ , **A__ )
if vision_config is None:
snake_case_ : int = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
snake_case_ : str = GitVisionConfig(**A__ )
snake_case_ : int = vocab_size
snake_case_ : List[Any] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = hidden_act
snake_case_ : Dict = intermediate_size
snake_case_ : Any = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[str] = initializer_range
snake_case_ : List[str] = layer_norm_eps
snake_case_ : Any = position_embedding_type
snake_case_ : Union[str, Any] = use_cache
snake_case_ : str = tie_word_embeddings
snake_case_ : List[Any] = num_image_with_embedding
snake_case_ : Dict = bos_token_id
snake_case_ : int = eos_token_id
def UpperCAmelCase__ ( self : Any ) -> int:
'''simple docstring'''
snake_case_ : Tuple = copy.deepcopy(self.__dict__ )
snake_case_ : Optional[int] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
| 666 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class snake_case__ :
_SCREAMING_SNAKE_CASE : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
_SCREAMING_SNAKE_CASE : bool = field(default=_UpperCamelCase , metadata={"help": "Whether tp freeze the encoder."} )
_SCREAMING_SNAKE_CASE : bool = field(default=_UpperCamelCase , metadata={"help": "Whether to freeze the embeddings."} )
@dataclass
class snake_case__ :
_SCREAMING_SNAKE_CASE : str = field(
metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default="summarization" , metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"} , )
_SCREAMING_SNAKE_CASE : Optional[int] = field(
default=1_0_2_4 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_SCREAMING_SNAKE_CASE : Optional[int] = field(
default=1_2_8 , metadata={
"help": (
"The maximum total sequence length for target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_SCREAMING_SNAKE_CASE : Optional[int] = field(
default=1_4_2 , metadata={
"help": (
"The maximum total sequence length for validation target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded. "
"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
"during ``evaluate`` and ``predict``."
)
} , )
_SCREAMING_SNAKE_CASE : Optional[int] = field(
default=1_4_2 , metadata={
"help": (
"The maximum total sequence length for test target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_SCREAMING_SNAKE_CASE : Optional[int] = field(default=-1 , metadata={"help": "# training examples. -1 means use all."} )
_SCREAMING_SNAKE_CASE : Optional[int] = field(default=-1 , metadata={"help": "# validation examples. -1 means use all."} )
_SCREAMING_SNAKE_CASE : Optional[int] = field(default=-1 , metadata={"help": "# test examples. -1 means use all."} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(default=_UpperCamelCase , metadata={"help": "Source language id for translation."} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(default=_UpperCamelCase , metadata={"help": "Target language id for translation."} )
_SCREAMING_SNAKE_CASE : Optional[int] = field(default=_UpperCamelCase , metadata={"help": "# num_beams to use for evaluation."} )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."} , )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: Optional[int] ):
logger.info(f"***** {split} metrics *****" )
for key in sorted(metrics.keys() ):
logger.info(f" {key} = {metrics[key]}" )
save_json(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , f"{split}_results.json" ) )
def SCREAMING_SNAKE_CASE_ ( ):
# 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_ : str = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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_ : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ ,snake_case_ ,snake_case_ : Dict = parser.parse_args_into_dataclasses()
check_output_dir(lowerCAmelCase_ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , lowerCAmelCase_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ : Optional[Any] = 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 , )
snake_case_ : str = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
assert hasattr(lowerCAmelCase_ , lowerCAmelCase_ ), f"({config.__class__.__name__}) doesn't have a `{p}` attribute"
setattr(lowerCAmelCase_ , lowerCAmelCase_ , getattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
snake_case_ : Dict = 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 , )
snake_case_ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=lowerCAmelCase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(lowerCAmelCase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
snake_case_ : Union[str, Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowerCAmelCase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
snake_case_ : Optional[Any] = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
snake_case_ : Any = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowerCAmelCase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
snake_case_ : str = SeqaSeqDataset
# Get datasets
snake_case_ : Dict = (
dataset_class(
lowerCAmelCase_ , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
snake_case_ : Tuple = (
dataset_class(
lowerCAmelCase_ , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
snake_case_ : int = (
dataset_class(
lowerCAmelCase_ , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
snake_case_ : List[Any] = (
build_compute_metrics_fn(data_args.task , lowerCAmelCase_ ) if training_args.predict_with_generate else None
)
snake_case_ : int = SeqaSeqTrainer(
model=lowerCAmelCase_ , args=lowerCAmelCase_ , data_args=lowerCAmelCase_ , train_dataset=lowerCAmelCase_ , eval_dataset=lowerCAmelCase_ , data_collator=SeqaSeqDataCollator(
lowerCAmelCase_ , lowerCAmelCase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ , )
snake_case_ : Tuple = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
snake_case_ : Dict = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
snake_case_ : Union[str, Any] = train_result.metrics
snake_case_ : Dict = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , lowerCAmelCase_ , training_args.output_dir )
all_metrics.update(lowerCAmelCase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
snake_case_ : int = trainer.evaluate(metric_key_prefix="val" )
snake_case_ : Dict = data_args.n_val
snake_case_ : Union[str, Any] = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , lowerCAmelCase_ , training_args.output_dir )
all_metrics.update(lowerCAmelCase_ )
if training_args.do_predict:
logger.info("*** Predict ***" )
snake_case_ : Any = trainer.predict(test_dataset=lowerCAmelCase_ , metric_key_prefix="test" )
snake_case_ : Dict = test_output.metrics
snake_case_ : str = data_args.n_test
if trainer.is_world_process_zero():
snake_case_ : Dict = round(metrics["test_loss"] , 4 )
handle_metrics("test" , lowerCAmelCase_ , training_args.output_dir )
all_metrics.update(lowerCAmelCase_ )
if training_args.predict_with_generate:
snake_case_ : int = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=lowerCAmelCase_ , clean_up_tokenization_spaces=lowerCAmelCase_ )
snake_case_ : str = lmap(str.strip , lowerCAmelCase_ )
write_txt_file(lowerCAmelCase_ , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(lowerCAmelCase_ , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ : str = int(max(0 , i - limit ) )
snake_case_ : Optional[int] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCAmelCase_ )
snake_case_ : List[Any] = f"{_stra[0:_stra.index(lowerCAmelCase_ )]} {_stra[_stra.index(lowerCAmelCase_ ) + 1:]}"
return "".join(lowerCAmelCase_ )
# matching characters
snake_case_ : List[Any] = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : int = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = len(lowerCAmelCase_ )
# transposition
snake_case_ : List[str] = (
len([(ca, ca) for ca, ca in zip(lowerCAmelCase_ , lowerCAmelCase_ ) if ca != ca] ) // 2
)
if not match_count:
snake_case_ : str = 0.0
else:
snake_case_ : Optional[Any] = (
1
/ 3
* (
match_count / len(lowerCAmelCase_ )
+ match_count / len(lowerCAmelCase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
snake_case_ : Optional[Any] = 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"))
| 666 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[List[PIL.Image.Image], np.ndarray]
_SCREAMING_SNAKE_CASE : Optional[List[bool]]
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 .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version(">=", "0.0.12")
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : np.ndarray
_SCREAMING_SNAKE_CASE : List[bool]
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
set_seed(7_7_0)
UpperCAmelCase = {
"c_attn": "att_proj",
"c_proj": "out_proj",
"c_fc": "in_proj",
"transformer.": "",
"h.": "layers.",
"ln_1": "layernorm_1",
"ln_2": "layernorm_2",
"ln_f": "layernorm_final",
"wpe": "position_embeds_layer",
"wte": "input_embeds_layer",
}
UpperCAmelCase = {
"text_small": {
"repo_id": "suno/bark",
"file_name": "text.pt",
},
"coarse_small": {
"repo_id": "suno/bark",
"file_name": "coarse.pt",
},
"fine_small": {
"repo_id": "suno/bark",
"file_name": "fine.pt",
},
"text": {
"repo_id": "suno/bark",
"file_name": "text_2.pt",
},
"coarse": {
"repo_id": "suno/bark",
"file_name": "coarse_2.pt",
},
"fine": {
"repo_id": "suno/bark",
"file_name": "fine_2.pt",
},
}
UpperCAmelCase = os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase = os.path.join(os.path.expanduser("~"), ".cache")
UpperCAmelCase = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: List[str]=False ):
snake_case_ : Union[str, Any] = model_type
if use_small:
key += "_small"
return os.path.join(lowerCAmelCase_ , REMOTE_MODEL_PATHS[key]["file_name"] )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: List[str] ):
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
hf_hub_download(repo_id=lowerCAmelCase_ , filename=lowerCAmelCase_ , local_dir=lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: Dict , lowerCAmelCase_: List[str]=False , lowerCAmelCase_: Dict="text" ):
if model_type == "text":
snake_case_ : int = BarkSemanticModel
snake_case_ : str = BarkSemanticConfig
snake_case_ : Optional[Any] = BarkSemanticGenerationConfig
elif model_type == "coarse":
snake_case_ : str = BarkCoarseModel
snake_case_ : Optional[int] = BarkCoarseConfig
snake_case_ : Any = BarkCoarseGenerationConfig
elif model_type == "fine":
snake_case_ : Optional[int] = BarkFineModel
snake_case_ : Tuple = BarkFineConfig
snake_case_ : List[str] = BarkFineGenerationConfig
else:
raise NotImplementedError()
snake_case_ : Optional[Any] = f"{model_type}_small" if use_small else model_type
snake_case_ : Any = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(lowerCAmelCase_ ):
logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`." )
_download(model_info["repo_id"] , model_info["file_name"] )
snake_case_ : Any = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
# this is a hack
snake_case_ : Union[str, Any] = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
snake_case_ : str = model_args["vocab_size"]
snake_case_ : Union[str, Any] = model_args["vocab_size"]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
snake_case_ : Union[str, Any] = model_args.pop("n_head" )
snake_case_ : int = model_args.pop("n_embd" )
snake_case_ : Any = model_args.pop("n_layer" )
snake_case_ : List[str] = ConfigClass(**checkpoint["model_args"] )
snake_case_ : Optional[Any] = ModelClass(config=lowerCAmelCase_ )
snake_case_ : Tuple = GenerationConfigClass()
snake_case_ : List[str] = model_generation_config
snake_case_ : Optional[int] = checkpoint["model"]
# fixup checkpoint
snake_case_ : Optional[int] = "_orig_mod."
for k, v in list(state_dict.items() ):
if k.startswith(lowerCAmelCase_ ):
# replace part of the key with corresponding layer name in HF implementation
snake_case_ : Tuple = k[len(lowerCAmelCase_ ) :]
for old_layer_name in new_layer_name_dict:
snake_case_ : int = new_k.replace(lowerCAmelCase_ , new_layer_name_dict[old_layer_name] )
snake_case_ : int = state_dict.pop(lowerCAmelCase_ )
snake_case_ : Optional[int] = set(state_dict.keys() ) - set(model.state_dict().keys() )
snake_case_ : str = {k for k in extra_keys if not k.endswith(".attn.bias" )}
snake_case_ : Any = set(model.state_dict().keys() ) - set(state_dict.keys() )
snake_case_ : List[Any] = {k for k in missing_keys if not k.endswith(".attn.bias" )}
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"extra keys found: {extra_keys}" )
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"missing keys: {missing_keys}" )
model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
snake_case_ : str = model.num_parameters(exclude_embeddings=lowerCAmelCase_ )
snake_case_ : Union[str, Any] = checkpoint["best_val_loss"].item()
logger.info(f"model loaded: {round(n_params/1e6 , 1 )}M params, {round(lowerCAmelCase_ , 3 )} loss" )
model.eval()
model.to(lowerCAmelCase_ )
del checkpoint, state_dict
return model
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] , lowerCAmelCase_: str=False , lowerCAmelCase_: int="text" ):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
snake_case_ : int = "cpu" # do conversion on cpu
snake_case_ : Optional[Any] = _get_ckpt_path(lowerCAmelCase_ , use_small=lowerCAmelCase_ )
snake_case_ : Tuple = _load_model(lowerCAmelCase_ , lowerCAmelCase_ , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
# load bark initial model
snake_case_ : int = _bark_load_model(lowerCAmelCase_ , "cpu" , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
if model_type == "text":
snake_case_ : Union[str, Any] = bark_model["model"]
if model.num_parameters(exclude_embeddings=lowerCAmelCase_ ) != bark_model.get_num_params():
raise ValueError("initial and new models don't have the same number of parameters" )
# check if same output as the bark model
snake_case_ : Optional[Any] = 5
snake_case_ : Optional[int] = 1_0
if model_type in ["text", "coarse"]:
snake_case_ : Optional[Any] = torch.randint(2_5_6 , (batch_size, sequence_length) , dtype=torch.int )
snake_case_ : str = bark_model(lowerCAmelCase_ )[0]
snake_case_ : Tuple = model(lowerCAmelCase_ )
# take last logits
snake_case_ : List[str] = output_new_model_total.logits[:, [-1], :]
else:
snake_case_ : Optional[int] = 3
snake_case_ : str = 8
snake_case_ : List[str] = torch.randint(2_5_6 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
snake_case_ : Any = model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Union[str, Any] = bark_model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("initial and new outputs don't have the same shape" )
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("initial and new outputs are not equal" )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple , lowerCAmelCase_: List[str] , lowerCAmelCase_: Any , lowerCAmelCase_: List[Any] , lowerCAmelCase_: int , lowerCAmelCase_: Optional[Any] , ):
snake_case_ : Optional[Any] = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[str] = BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = EncodecConfig.from_pretrained("facebook/encodec_24khz" )
snake_case_ : List[str] = BarkSemanticModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkCoarseModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Tuple = BarkFineModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Union[str, Any] = EncodecModel.from_pretrained("facebook/encodec_24khz" )
snake_case_ : Tuple = BarkConfig.from_sub_model_configs(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : List[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
snake_case_ : Optional[int] = BarkModel(lowerCAmelCase_ )
snake_case_ : int = semantic
snake_case_ : List[str] = coarseAcoustic
snake_case_ : str = fineAcoustic
snake_case_ : Optional[Any] = codec
snake_case_ : Any = bark_generation_config
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
bark.save_pretrained(lowerCAmelCase_ , repo_id=lowerCAmelCase_ , push_to_hub=lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("model_type", type=str, help="text, coarse or fine.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
UpperCAmelCase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 666 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
UpperCAmelCase = tuple[int, int]
class snake_case__ :
def __init__( self : Optional[Any] , A__ : set[int] , A__ : Mapping[EdgeT, int] ) -> None:
'''simple docstring'''
snake_case_ : set[int] = vertices
snake_case_ : dict[EdgeT, int] = {
(min(A__ ), max(A__ )): weight for edge, weight in edges.items()
}
def UpperCAmelCase__ ( self : Dict , A__ : EdgeT , A__ : int ) -> None:
'''simple docstring'''
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
snake_case_ : Tuple = weight
def UpperCAmelCase__ ( self : List[str] ) -> Graph:
'''simple docstring'''
snake_case_ : Graph = Graph({min(self.vertices )} , {} )
snake_case_ : EdgeT
snake_case_ : int
snake_case_ : EdgeT
snake_case_ : int
while len(subgraph.vertices ) < len(self.vertices ):
snake_case_ : str = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
snake_case_ : str = edge
snake_case_ : Dict = weight
subgraph.add_edge(A__ , A__ )
return subgraph
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str = "p107_network.txt" ):
snake_case_ : str = os.path.abspath(os.path.dirname(lowerCAmelCase_ ) )
snake_case_ : str = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : dict[EdgeT, int] = {}
snake_case_ : list[str]
snake_case_ : int
snake_case_ : int
with open(lowerCAmelCase_ ) as f:
snake_case_ : List[str] = f.read().strip().split("\n" )
snake_case_ : Tuple = [line.split("," ) for line in data]
for edgea in range(1 , len(lowerCAmelCase_ ) ):
for edgea in range(lowerCAmelCase_ ):
if adjaceny_matrix[edgea][edgea] != "-":
snake_case_ : Dict = int(adjaceny_matrix[edgea][edgea] )
snake_case_ : Graph = Graph(set(range(len(lowerCAmelCase_ ) ) ) , lowerCAmelCase_ )
snake_case_ : Graph = graph.prims_algorithm()
snake_case_ : int = sum(graph.edges.values() )
snake_case_ : int = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"{solution() = }")
| 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
snake_case_ : Tuple = 1
snake_case_ : List[Any] = 3
snake_case_ : Union[str, Any] = (32, 32)
snake_case_ : List[Any] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(A__ )
return image
@property
def UpperCAmelCase__ ( self : List[str] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Any = 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 , )
return model
@property
def UpperCAmelCase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Dict = 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 , )
return model
@property
def UpperCAmelCase__ ( self : int ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[Any] = 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(A__ )
@property
def UpperCAmelCase__ ( self : int ) -> Optional[int]:
'''simple docstring'''
def extract(*A__ : Dict , **A__ : Optional[int] ):
class snake_case__ :
def __init__( self : List[str] ) -> str:
'''simple docstring'''
snake_case_ : str = torch.ones([0] )
def UpperCAmelCase__ ( self : Any , A__ : Dict ) -> List[Any]:
'''simple docstring'''
self.pixel_values.to(A__ )
return self
return Out()
return extract
def UpperCAmelCase__ ( self : Dict ) -> str:
'''simple docstring'''
snake_case_ : int = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case_ : Dict = self.dummy_cond_unet
snake_case_ : int = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=A__ , set_alpha_to_one=A__ , )
snake_case_ : str = self.dummy_vae
snake_case_ : Any = self.dummy_text_encoder
snake_case_ : str = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# make sure here that pndm scheduler skips prk
snake_case_ : Optional[Any] = StableDiffusionPipeline(
unet=A__ , scheduler=A__ , vae=A__ , text_encoder=A__ , tokenizer=A__ , safety_checker=A__ , feature_extractor=self.dummy_extractor , )
snake_case_ : Optional[Any] = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case_ : List[str] = "A painting of a squirrel eating a burger"
snake_case_ : int = torch.Generator(device=A__ ).manual_seed(0 )
snake_case_ : Tuple = sd_pipe([prompt] , generator=A__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" )
snake_case_ : Union[str, Any] = output.images
snake_case_ : Union[str, Any] = torch.Generator(device=A__ ).manual_seed(0 )
snake_case_ : List[Any] = sd_pipe(
[prompt] , generator=A__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , return_dict=A__ , )[0]
snake_case_ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ : List[str] = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self : List[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case_ : Optional[int] = self.dummy_cond_unet
snake_case_ : str = PNDMScheduler(skip_prk_steps=A__ )
snake_case_ : List[Any] = self.dummy_vae
snake_case_ : Tuple = self.dummy_text_encoder
snake_case_ : List[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# make sure here that pndm scheduler skips prk
snake_case_ : Union[str, Any] = StableDiffusionPipeline(
unet=A__ , scheduler=A__ , vae=A__ , text_encoder=A__ , tokenizer=A__ , safety_checker=A__ , feature_extractor=self.dummy_extractor , )
snake_case_ : Optional[Any] = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case_ : Union[str, Any] = "A painting of a squirrel eating a burger"
snake_case_ : Optional[Any] = torch.Generator(device=A__ ).manual_seed(0 )
snake_case_ : Tuple = sd_pipe([prompt] , generator=A__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" )
snake_case_ : Optional[Any] = output.images
snake_case_ : int = torch.Generator(device=A__ ).manual_seed(0 )
snake_case_ : Optional[Any] = sd_pipe(
[prompt] , generator=A__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , return_dict=A__ , )[0]
snake_case_ : Dict = image[0, -3:, -3:, -1]
snake_case_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ : Any = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-lms-pipe" , safety_checker=A__ )
assert isinstance(A__ , A__ )
assert isinstance(pipe.scheduler , A__ )
assert pipe.safety_checker is None
snake_case_ : Optional[Any] = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(A__ )
snake_case_ : List[str] = StableDiffusionPipeline.from_pretrained(A__ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
snake_case_ : List[str] = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def UpperCAmelCase__ ( self : str ) -> int:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.dummy_cond_unet
snake_case_ : List[Any] = PNDMScheduler(skip_prk_steps=A__ )
snake_case_ : str = self.dummy_vae
snake_case_ : Dict = self.dummy_text_encoder
snake_case_ : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# put models in fp16
snake_case_ : Dict = unet.half()
snake_case_ : List[str] = vae.half()
snake_case_ : str = bert.half()
# make sure here that pndm scheduler skips prk
snake_case_ : str = StableDiffusionPipeline(
unet=A__ , scheduler=A__ , vae=A__ , text_encoder=A__ , tokenizer=A__ , safety_checker=A__ , feature_extractor=self.dummy_extractor , )
snake_case_ : List[str] = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case_ : List[str] = "A painting of a squirrel eating a burger"
snake_case_ : Optional[Any] = sd_pipe([prompt] , num_inference_steps=2 , output_type="np" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Optional[int] ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" , safety_checker=A__ )
snake_case_ : Tuple = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
snake_case_ : Dict = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case_ : int = (
"portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
" children from bahnhof zoo, detailed "
)
snake_case_ : Tuple = 40_03_66_03_46
snake_case_ : Optional[int] = 7
# without safety guidance (sld_guidance_scale = 0)
snake_case_ : Optional[Any] = torch.manual_seed(A__ )
snake_case_ : Optional[Any] = sd_pipe(
[prompt] , generator=A__ , guidance_scale=A__ , num_inference_steps=50 , output_type="np" , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
snake_case_ : Optional[int] = output.images
snake_case_ : Optional[int] = image[0, -3:, -3:, -1]
snake_case_ : Dict = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
snake_case_ : int = torch.manual_seed(A__ )
snake_case_ : Any = sd_pipe(
[prompt] , generator=A__ , guidance_scale=A__ , num_inference_steps=50 , output_type="np" , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
snake_case_ : str = output.images
snake_case_ : Optional[int] = image[0, -3:, -3:, -1]
snake_case_ : Tuple = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[Any] = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" , safety_checker=A__ )
snake_case_ : Optional[Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
snake_case_ : Optional[int] = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case_ : int = "padme amidala taking a bath artwork, safe for work, no nudity"
snake_case_ : List[str] = 27_34_97_17_55
snake_case_ : str = 7
snake_case_ : Optional[int] = torch.manual_seed(A__ )
snake_case_ : Dict = sd_pipe(
[prompt] , generator=A__ , guidance_scale=A__ , num_inference_steps=50 , output_type="np" , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
snake_case_ : List[str] = output.images
snake_case_ : Optional[int] = image[0, -3:, -3:, -1]
snake_case_ : int = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
snake_case_ : Optional[Any] = torch.manual_seed(A__ )
snake_case_ : Union[str, Any] = sd_pipe(
[prompt] , generator=A__ , guidance_scale=A__ , num_inference_steps=50 , output_type="np" , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
snake_case_ : List[Any] = output.images
snake_case_ : Dict = image[0, -3:, -3:, -1]
snake_case_ : str = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self : int ) -> Tuple:
'''simple docstring'''
snake_case_ : int = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" )
snake_case_ : Optional[Any] = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case_ : List[Any] = (
"the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
" leyendecker"
)
snake_case_ : List[Any] = 10_44_35_52_34
snake_case_ : Tuple = 12
snake_case_ : List[str] = torch.manual_seed(A__ )
snake_case_ : Optional[Any] = sd_pipe(
[prompt] , generator=A__ , guidance_scale=A__ , num_inference_steps=50 , output_type="np" , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
snake_case_ : Optional[int] = output.images
snake_case_ : int = image[0, -3:, -3:, -1]
snake_case_ : str = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
snake_case_ : Dict = torch.manual_seed(A__ )
snake_case_ : int = sd_pipe(
[prompt] , generator=A__ , guidance_scale=A__ , num_inference_steps=50 , output_type="np" , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
snake_case_ : List[Any] = output.images
snake_case_ : Union[str, Any] = image[0, -3:, -3:, -1]
snake_case_ : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 666 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
UpperCAmelCase = logging.get_logger(__name__)
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : str = ["pixel_values"]
def __init__( self : List[Any] , A__ : bool = True , A__ : Optional[Dict[str, int]] = None , A__ : PILImageResampling = PILImageResampling.BILINEAR , A__ : bool = True , A__ : Dict[str, int] = None , A__ : bool = True , A__ : Union[int, float] = 1 / 2_55 , A__ : bool = True , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , **A__ : int , ) -> None:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : Optional[int] = size if size is not None else {"shortest_edge": 2_56}
snake_case_ : Dict = get_size_dict(A__ , default_to_square=A__ )
snake_case_ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
snake_case_ : Any = get_size_dict(A__ , param_name="crop_size" )
snake_case_ : int = do_resize
snake_case_ : Optional[Any] = size
snake_case_ : Optional[Any] = resample
snake_case_ : Optional[int] = do_center_crop
snake_case_ : List[Any] = crop_size
snake_case_ : List[Any] = do_rescale
snake_case_ : Optional[int] = rescale_factor
snake_case_ : Optional[Any] = do_normalize
snake_case_ : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case_ : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase__ ( self : List[str] , A__ : np.ndarray , A__ : Dict[str, int] , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : str , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Optional[Any] = get_size_dict(A__ , default_to_square=A__ )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
snake_case_ : Any = get_resize_output_image_size(A__ , size=size["shortest_edge"] , default_to_square=A__ )
return resize(A__ , size=A__ , resample=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : int , A__ : np.ndarray , A__ : Dict[str, int] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Union[str, Any] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Tuple = get_size_dict(A__ )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(A__ , size=(size["height"], size["width"]) , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : List[str] , A__ : np.ndarray , A__ : float , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Tuple ) -> np.ndarray:
'''simple docstring'''
return rescale(A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : np.ndarray , A__ : Union[float, List[float]] , A__ : Union[float, List[float]] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Dict , ) -> np.ndarray:
'''simple docstring'''
return normalize(A__ , mean=A__ , std=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : ImageInput , A__ : Optional[bool] = None , A__ : Dict[str, int] = None , A__ : PILImageResampling = None , A__ : bool = None , A__ : Dict[str, int] = None , A__ : Optional[bool] = None , A__ : Optional[float] = None , A__ : Optional[bool] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[str, TensorType]] = None , A__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **A__ : Union[str, Any] , ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
snake_case_ : Dict = size if size is not None else self.size
snake_case_ : Optional[Any] = get_size_dict(A__ , default_to_square=A__ )
snake_case_ : Tuple = resample if resample is not None else self.resample
snake_case_ : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ : str = crop_size if crop_size is not None else self.crop_size
snake_case_ : Tuple = get_size_dict(A__ , param_name="crop_size" )
snake_case_ : Dict = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : Any = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ : Any = image_mean if image_mean is not None else self.image_mean
snake_case_ : List[str] = image_std if image_std is not None else self.image_std
snake_case_ : Dict = make_list_of_images(A__ )
if not valid_images(A__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
snake_case_ : Tuple = [to_numpy_array(A__ ) for image in images]
if do_resize:
snake_case_ : Any = [self.resize(image=A__ , size=A__ , resample=A__ ) for image in images]
if do_center_crop:
snake_case_ : List[str] = [self.center_crop(image=A__ , size=A__ ) for image in images]
if do_rescale:
snake_case_ : Any = [self.rescale(image=A__ , scale=A__ ) for image in images]
if do_normalize:
snake_case_ : Union[str, Any] = [self.normalize(image=A__ , mean=A__ , std=A__ ) for image in images]
snake_case_ : Optional[Any] = [to_channel_dimension_format(A__ , A__ ) for image in images]
snake_case_ : Any = {"pixel_values": images}
return BatchFeature(data=A__ , tensor_type=A__ )
def UpperCAmelCase__ ( self : List[str] , A__ : Dict , A__ : List[Tuple] = None ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(A__ ) != len(A__ ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(A__ ):
snake_case_ : Dict = target_sizes.numpy()
snake_case_ : int = []
for idx in range(len(A__ ) ):
snake_case_ : List[str] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=A__ )
snake_case_ : int = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A__ )
else:
snake_case_ : List[Any] = logits.argmax(dim=1 )
snake_case_ : List[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 666 | 1 |
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 666 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotConfig",
"BlenderbotOnnxConfig",
],
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["BlenderbotTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
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
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
UpperCAmelCase = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class snake_case__ ( unittest.TestCase ):
def __init__( self : Dict , A__ : Optional[int] , A__ : List[Any]=7 , A__ : List[str]=3 , A__ : Union[str, Any]=18 , A__ : Union[str, Any]=30 , A__ : str=4_00 , A__ : Tuple=None , A__ : Union[str, Any]=True , A__ : Union[str, Any]=True , A__ : str=None , ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = size if size is not None else {"height": 20, "width": 20}
snake_case_ : Optional[int] = parent
snake_case_ : List[Any] = batch_size
snake_case_ : int = num_channels
snake_case_ : Any = image_size
snake_case_ : Dict = min_resolution
snake_case_ : Optional[Any] = max_resolution
snake_case_ : Optional[int] = size
snake_case_ : str = do_normalize
snake_case_ : List[Any] = do_convert_rgb
snake_case_ : Optional[Any] = [5_12, 10_24, 20_48, 40_96]
snake_case_ : str = patch_size if patch_size is not None else {"height": 16, "width": 16}
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def UpperCAmelCase__ ( self : str ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
snake_case_ : List[str] = Image.open(requests.get(A__ , stream=A__ ).raw ).convert("RGB" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." , )
@require_torch
@require_vision
class snake_case__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[Any] = PixaStructImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
snake_case_ : List[Any] = PixaStructImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A__ , "do_normalize" ) )
self.assertTrue(hasattr(A__ , "do_convert_rgb" ) )
def UpperCAmelCase__ ( self : int ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processor_tester.prepare_dummy_image()
snake_case_ : Tuple = self.image_processing_class(**self.image_processor_dict )
snake_case_ : Optional[Any] = 20_48
snake_case_ : Optional[int] = image_processor(A__ , return_tensors="pt" , max_patches=A__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1E-3 , rtol=1E-3 ) )
def UpperCAmelCase__ ( self : List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case_ : str = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
snake_case_ : Optional[Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
snake_case_ : int = image_processor(
A__ , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCAmelCase__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case_ : Dict = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
snake_case_ : str = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(A__ ):
snake_case_ : str = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=A__ ).flattened_patches
snake_case_ : Tuple = "Hello"
snake_case_ : Tuple = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=A__ , header_text=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
snake_case_ : Dict = image_processor(
A__ , return_tensors="pt" , max_patches=A__ , header_text=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCAmelCase__ ( self : int ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , numpify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , np.ndarray )
snake_case_ : str = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
snake_case_ : Tuple = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
snake_case_ : Optional[int] = image_processor(
A__ , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
snake_case_ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , torchify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , torch.Tensor )
# Test not batched input
snake_case_ : Optional[Any] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
snake_case_ : List[Any] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
snake_case_ : Optional[int] = image_processor(
A__ , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." , )
@require_torch
@require_vision
class snake_case__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Any = PixaStructImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : Dict = PixaStructImageProcessingTester(self , num_channels=4 )
snake_case_ : List[str] = 3
@property
def UpperCAmelCase__ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A__ , "do_normalize" ) )
self.assertTrue(hasattr(A__ , "do_convert_rgb" ) )
def UpperCAmelCase__ ( self : Tuple ) -> Any:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
snake_case_ : Optional[int] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
snake_case_ : Any = image_processor(
A__ , return_tensors="pt" , max_patches=A__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 666 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "tapas"
def __init__( self : List[Any] , A__ : str=3_05_22 , A__ : Tuple=7_68 , A__ : List[Any]=12 , A__ : Optional[Any]=12 , A__ : Union[str, Any]=30_72 , A__ : Dict="gelu" , A__ : List[Any]=0.1 , A__ : str=0.1 , A__ : List[Any]=10_24 , A__ : Optional[int]=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , A__ : Union[str, Any]=0.02 , A__ : Tuple=1E-12 , A__ : Tuple=0 , A__ : Any=10.0 , A__ : List[str]=0 , A__ : List[str]=1.0 , A__ : Optional[Any]=None , A__ : Tuple=1.0 , A__ : Union[str, Any]=False , A__ : Any=None , A__ : Union[str, Any]=1.0 , A__ : int=1.0 , A__ : str=False , A__ : int=False , A__ : Optional[Any]="ratio" , A__ : str=None , A__ : int=None , A__ : Dict=64 , A__ : int=32 , A__ : Optional[Any]=False , A__ : List[str]=True , A__ : List[Any]=False , A__ : str=False , A__ : Any=True , A__ : Tuple=False , A__ : str=None , A__ : str=None , **A__ : List[str] , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=A__ , **A__ )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
snake_case_ : int = vocab_size
snake_case_ : int = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = hidden_act
snake_case_ : Optional[int] = intermediate_size
snake_case_ : str = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : Any = max_position_embeddings
snake_case_ : List[Any] = type_vocab_sizes
snake_case_ : str = initializer_range
snake_case_ : Optional[Any] = layer_norm_eps
# Fine-tuning task hyperparameters
snake_case_ : Optional[int] = positive_label_weight
snake_case_ : Dict = num_aggregation_labels
snake_case_ : List[str] = aggregation_loss_weight
snake_case_ : str = use_answer_as_supervision
snake_case_ : int = answer_loss_importance
snake_case_ : Any = use_normalized_answer_loss
snake_case_ : int = huber_loss_delta
snake_case_ : List[Any] = temperature
snake_case_ : str = aggregation_temperature
snake_case_ : List[str] = use_gumbel_for_cells
snake_case_ : List[str] = use_gumbel_for_aggregation
snake_case_ : Dict = average_approximation_function
snake_case_ : List[str] = cell_selection_preference
snake_case_ : Dict = answer_loss_cutoff
snake_case_ : List[str] = max_num_rows
snake_case_ : Union[str, Any] = max_num_columns
snake_case_ : str = average_logits_per_cell
snake_case_ : Union[str, Any] = select_one_column
snake_case_ : Dict = allow_empty_column_selection
snake_case_ : List[Any] = init_cell_selection_weights_to_zero
snake_case_ : str = reset_position_index_per_cell
snake_case_ : List[Any] = disable_per_token_loss
# Aggregation hyperparameters
snake_case_ : List[str] = aggregation_labels
snake_case_ : Union[str, Any] = no_aggregation_label_index
if isinstance(self.aggregation_labels , A__ ):
snake_case_ : Optional[int] = {int(A__ ): v for k, v in aggregation_labels.items()}
| 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] ):
snake_case_ : str = len(lowerCAmelCase_ ) // 2
# choose the middle 3 elements
snake_case_ : int = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string" )} ) , supervised_keys=A__ , )
def UpperCAmelCase__ ( self : Optional[Any] , A__ : str , A__ : str ) -> Optional[int]:
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_dummy_examples()} )]
def UpperCAmelCase__ ( self : int , A__ : Optional[int] , A__ : Dict ) -> Optional[Any]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A__ )
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) , supervised_keys=A__ , )
def UpperCAmelCase__ ( self : Any , A__ : List[str] , A__ : str ) -> Optional[int]:
'''simple docstring'''
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_nested_examples()} )
]
def UpperCAmelCase__ ( self : List[Any] , A__ : List[str] , A__ : Optional[int] ) -> List[str]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A__ )
def SCREAMING_SNAKE_CASE_ ( ):
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"] )]
def SCREAMING_SNAKE_CASE_ ( ):
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"] )]
class snake_case__ ( _UpperCamelCase ):
@require_beam
def UpperCAmelCase__ ( self : str ) -> List[str]:
'''simple docstring'''
snake_case_ : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : Dict = DummyBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
snake_case_ : Optional[int] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
self.assertDictEqual(dset["train"][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
import apache_beam as beam
snake_case_ : Tuple = beam.io.parquetio.WriteToParquet
snake_case_ : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : List[Any] = DummyBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
snake_case_ : int = partial(A__ , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
snake_case_ : Optional[Any] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"] ) , sorted(["foo", "bar", "foobar"] ) )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : Tuple = DummyBeamDataset(cache_dir=A__ )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : List[str] = NestedBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
snake_case_ : int = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
self.assertDictEqual(dset["train"][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
| 666 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class snake_case__ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
snake_case_ : Tuple = 0
@slow
def UpperCAmelCase__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
snake_case_ : Dict = AutoTokenizer.from_pretrained(A__ )
self.assertIsNotNone(A__ )
self.assertIsInstance(A__ , (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(A__ ) , 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
snake_case_ : Optional[Any] = AutoTokenizer.from_pretrained(A__ )
self.assertIsNotNone(A__ )
self.assertIsInstance(A__ , (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(A__ ) , 0 )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = AutoTokenizer.from_pretrained(A__ )
self.assertIsInstance(A__ , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def UpperCAmelCase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = AutoTokenizer.from_pretrained(A__ )
self.assertIsInstance(A__ , (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 20 )
def UpperCAmelCase__ ( self : Tuple ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoConfig.from_pretrained(A__ )
self.assertIsInstance(A__ , A__ )
# Check that tokenizer_type ≠ model_type
snake_case_ : str = AutoTokenizer.from_pretrained(A__ , config=A__ )
self.assertIsInstance(A__ , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def UpperCAmelCase__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.txt" , os.path.join(A__ , "vocab.txt" ) )
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained(A__ , tokenizer_type="bert" , use_fast=A__ )
self.assertIsInstance(A__ , A__ )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.json" , os.path.join(A__ , "vocab.json" ) )
shutil.copy("./tests/fixtures/merges.txt" , os.path.join(A__ , "merges.txt" ) )
snake_case_ : Any = AutoTokenizer.from_pretrained(A__ , tokenizer_type="gpt2" , use_fast=A__ )
self.assertIsInstance(A__ , A__ )
@require_tokenizers
def UpperCAmelCase__ ( self : Optional[int] ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.txt" , os.path.join(A__ , "vocab.txt" ) )
snake_case_ : Any = AutoTokenizer.from_pretrained(A__ , tokenizer_type="bert" )
self.assertIsInstance(A__ , A__ )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.json" , os.path.join(A__ , "vocab.json" ) )
shutil.copy("./tests/fixtures/merges.txt" , os.path.join(A__ , "merges.txt" ) )
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained(A__ , tokenizer_type="gpt2" )
self.assertIsInstance(A__ , A__ )
def UpperCAmelCase__ ( self : List[str] ) -> Dict:
'''simple docstring'''
with pytest.raises(A__ ):
AutoTokenizer.from_pretrained("./" , tokenizer_type="xxx" )
@require_tokenizers
def UpperCAmelCase__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
snake_case_ : Optional[Any] = tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased" )
self.assertIsInstance(A__ , (BertTokenizer, BertTokenizerFast) )
if isinstance(A__ , A__ ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , A__ )
else:
self.assertEqual(tokenizer.do_lower_case , A__ )
self.assertEqual(tokenizer.model_max_length , 5_12 )
@require_tokenizers
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
A__ , "julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier" , ):
snake_case_ : str = tokenizer_class.from_pretrained("julien-c/herlolip-not-exists" )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : int = TOKENIZER_MAPPING.values()
snake_case_ : Any = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(A__ )
@require_tokenizers
def UpperCAmelCase__ ( self : Optional[int] ) -> int:
'''simple docstring'''
self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" , use_fast=A__ ) , A__ )
self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" ) , A__ )
@require_tokenizers
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = AutoTokenizer.from_pretrained("distilbert-base-uncased" , do_lower_case=A__ )
snake_case_ : List[str] = "Hello, world. How are you?"
snake_case_ : Optional[int] = tokenizer.tokenize(A__ )
self.assertEqual("[UNK]" , tokens[0] )
snake_case_ : List[str] = AutoTokenizer.from_pretrained("microsoft/mpnet-base" , do_lower_case=A__ )
snake_case_ : str = tokenizer.tokenize(A__ )
self.assertEqual("[UNK]" , tokens[0] )
@require_tokenizers
def UpperCAmelCase__ ( self : List[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config" )
self.assertEqual(type(A__ ) , A__ )
self.assertEqual(tokenizer.model_max_length , 5_12 )
self.assertEqual(tokenizer.vocab_size , 3_00_00 )
self.assertEqual(tokenizer.unk_token , "[UNK]" )
self.assertEqual(tokenizer.padding_side , "right" )
self.assertEqual(tokenizer.truncation_side , "right" )
def UpperCAmelCase__ ( self : str ) -> int:
'''simple docstring'''
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained(A__ )
self.assertIsInstance(A__ , (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(A__ )
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained(A__ )
self.assertIsInstance(A__ , tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size , 12 )
def UpperCAmelCase__ ( self : Any ) -> Tuple:
'''simple docstring'''
snake_case_ : int = AutoTokenizer.from_pretrained("ctrl" )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(A__ , A__ )
def UpperCAmelCase__ ( self : Dict ) -> str:
'''simple docstring'''
snake_case_ : Any = get_tokenizer_config("bert-base-cased" )
snake_case_ : Dict = config.pop("_commit_hash" , A__ )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(A__ , {"do_lower_case": False} )
# This model does not have a tokenizer_config so we get back an empty dict.
snake_case_ : Optional[int] = get_tokenizer_config(A__ )
self.assertDictEqual(A__ , {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
snake_case_ : Any = AutoTokenizer.from_pretrained(A__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(A__ )
snake_case_ : Tuple = get_tokenizer_config(A__ )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config["tokenizer_class"] , "BertTokenizer" )
def UpperCAmelCase__ ( self : Any ) -> Any:
'''simple docstring'''
try:
AutoConfig.register("custom" , A__ )
AutoTokenizer.register(A__ , slow_tokenizer_class=A__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(A__ ):
AutoTokenizer.register(A__ , slow_tokenizer_class=A__ )
snake_case_ : Union[str, Any] = CustomTokenizer.from_pretrained(A__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(A__ )
snake_case_ : Tuple = AutoTokenizer.from_pretrained(A__ )
self.assertIsInstance(A__ , A__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def UpperCAmelCase__ ( self : str ) -> int:
'''simple docstring'''
try:
AutoConfig.register("custom" , A__ )
# Can register in two steps
AutoTokenizer.register(A__ , slow_tokenizer_class=A__ )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) )
AutoTokenizer.register(A__ , fast_tokenizer_class=A__ )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
A__ , slow_tokenizer_class=A__ , fast_tokenizer_class=A__ )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(A__ ):
AutoTokenizer.register(A__ , fast_tokenizer_class=A__ )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ : int = BertTokenizerFast.from_pretrained(A__ )
bert_tokenizer.save_pretrained(A__ )
snake_case_ : Dict = CustomTokenizerFast.from_pretrained(A__ )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(A__ )
snake_case_ : Tuple = AutoTokenizer.from_pretrained(A__ )
self.assertIsInstance(A__ , A__ )
snake_case_ : str = AutoTokenizer.from_pretrained(A__ , use_fast=A__ )
self.assertIsInstance(A__ , A__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase__ ( self : int ) -> Dict:
'''simple docstring'''
with self.assertRaises(A__ ):
snake_case_ : str = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(A__ ):
snake_case_ : List[str] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=A__ )
snake_case_ : str = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=A__ )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(A__ )
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained(A__ , trust_remote_code=A__ )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
snake_case_ : Any = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=A__ , use_fast=A__ )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(A__ )
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained(A__ , trust_remote_code=A__ , use_fast=A__ )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizer" )
@require_tokenizers
def UpperCAmelCase__ ( self : List[str] ) -> str:
'''simple docstring'''
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = False
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Any = NewTokenizer
_SCREAMING_SNAKE_CASE : Tuple = False
try:
AutoConfig.register("custom" , A__ )
AutoTokenizer.register(A__ , slow_tokenizer_class=A__ )
AutoTokenizer.register(A__ , fast_tokenizer_class=A__ )
# If remote code is not set, the default is to use local
snake_case_ : Optional[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertFalse(tokenizer.special_attribute_present )
snake_case_ : Any = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" , use_fast=A__ )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
snake_case_ : int = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=A__ )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertFalse(tokenizer.special_attribute_present )
snake_case_ : str = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=A__ , use_fast=A__ )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
snake_case_ : Optional[Any] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=A__ )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertTrue(tokenizer.special_attribute_present )
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=A__ , use_fast=A__ )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase__ ( self : Any ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer_legacy" , trust_remote_code=A__ )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
snake_case_ : List[Any] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer_legacy" , trust_remote_code=A__ , use_fast=A__ )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
def UpperCAmelCase__ ( self : int ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
A__ , "bert-base is not a local folder and is not a valid model identifier" ):
snake_case_ : List[str] = AutoTokenizer.from_pretrained("bert-base" )
def UpperCAmelCase__ ( self : Optional[int] ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
A__ , r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
snake_case_ : str = AutoTokenizer.from_pretrained(A__ , revision="aaaaaa" )
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
with RequestCounter() as counter:
snake_case_ : str = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 666 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple ):
class snake_case__ :
def __init__( self : Any , A__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = metric_id
class snake_case__ :
_SCREAMING_SNAKE_CASE : List[str] = [MetricMock(_UpperCamelCase ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def UpperCAmelCase__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple , lowerCAmelCase_: int , lowerCAmelCase_: List[Any] , lowerCAmelCase_: Any , lowerCAmelCase_: List[str] ):
if "tmp_path" in args:
snake_case_ : List[Any] = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(lowerCAmelCase_ , match="https://huggingface.co/docs/evaluate" ):
func(*lowerCAmelCase_ )
| 666 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Dict , lowerCAmelCase_: Dict ):
snake_case_ : int = int(lowerCAmelCase_ )
assert noofclusters < len(lowerCAmelCase_ )
# Find out the dimensionality
snake_case_ : Union[str, Any] = len(vectors[0] )
# Will help select random centroids from among the available vectors
snake_case_ : Optional[int] = list(range(len(lowerCAmelCase_ ) ) )
shuffle(lowerCAmelCase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
snake_case_ : Optional[Any] = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
snake_case_ : Tuple = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
snake_case_ : Union[str, Any] = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowerCAmelCase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
snake_case_ : Optional[Any] = tf.placeholder("float64" , [dim] )
snake_case_ : Union[str, Any] = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowerCAmelCase_ , lowerCAmelCase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
snake_case_ : Optional[Any] = [tf.Variable(0 ) for i in range(len(lowerCAmelCase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
snake_case_ : Tuple = tf.placeholder("int32" )
snake_case_ : Optional[int] = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowerCAmelCase_ , lowerCAmelCase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
snake_case_ : Union[str, Any] = tf.placeholder("float" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
snake_case_ : str = tf.reduce_mean(lowerCAmelCase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
snake_case_ : Union[str, Any] = tf.placeholder("float" , [dim] )
snake_case_ : Dict = tf.placeholder("float" , [dim] )
snake_case_ : Any = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCAmelCase_ , lowerCAmelCase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
snake_case_ : Tuple = tf.placeholder("float" , [noofclusters] )
snake_case_ : str = tf.argmin(lowerCAmelCase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
snake_case_ : str = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowerCAmelCase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
snake_case_ : int = 1_0_0
for _ in range(lowerCAmelCase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowerCAmelCase_ ) ):
snake_case_ : int = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
snake_case_ : int = [
sess.run(lowerCAmelCase_ , feed_dict={va: vect, va: sess.run(lowerCAmelCase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
snake_case_ : List[str] = sess.run(
lowerCAmelCase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowerCAmelCase_ ):
# Collect all the vectors assigned to this cluster
snake_case_ : Union[str, Any] = [
vectors[i]
for i in range(len(lowerCAmelCase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
snake_case_ : Optional[int] = sess.run(
lowerCAmelCase_ , feed_dict={mean_input: array(lowerCAmelCase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
snake_case_ : Dict = sess.run(lowerCAmelCase_ )
snake_case_ : Optional[Any] = sess.run(lowerCAmelCase_ )
return centroids, assignments
| 666 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCAmelCase_ )
while lo < hi:
snake_case_ : List[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
snake_case_ : Tuple = mid + 1
else:
snake_case_ : Dict = mid
return lo
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Optional[Any] = len(lowerCAmelCase_ )
while lo < hi:
snake_case_ : Union[str, Any] = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
snake_case_ : Optional[Any] = mid + 1
else:
snake_case_ : Tuple = mid
return lo
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
sorted_collection.insert(bisect_left(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
sorted_collection.insert(bisect_right(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int ):
snake_case_ : Dict = 0
snake_case_ : Tuple = len(lowerCAmelCase_ ) - 1
while left <= right:
snake_case_ : int = left + (right - left) // 2
snake_case_ : Optional[Any] = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
snake_case_ : Optional[Any] = midpoint - 1
else:
snake_case_ : Optional[int] = midpoint + 1
return None
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int ):
snake_case_ : Optional[int] = bisect.bisect_left(lowerCAmelCase_ , lowerCAmelCase_ )
if index != len(lowerCAmelCase_ ) and sorted_collection[index] == item:
return index
return None
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if right < left:
return None
snake_case_ : List[Any] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , midpoint - 1 )
else:
return binary_search_by_recursion(lowerCAmelCase_ , lowerCAmelCase_ , midpoint + 1 , lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = input("Enter numbers separated by comma:\n").strip()
UpperCAmelCase = sorted(int(item) for item in user_input.split(","))
UpperCAmelCase = int(input("Enter a single number to be found in the list:\n"))
UpperCAmelCase = binary_search(collection, target)
if result is None:
print(F"{target} was not found in {collection}.")
else:
print(F"{target} was found at position {result} in {collection}.")
| 666 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"openai/imagegpt-small": "",
"openai/imagegpt-medium": "",
"openai/imagegpt-large": "",
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : int = "imagegpt"
_SCREAMING_SNAKE_CASE : int = ["past_key_values"]
_SCREAMING_SNAKE_CASE : Optional[int] = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[Any] , A__ : Any=5_12 + 1 , A__ : Optional[int]=32 * 32 , A__ : Optional[Any]=5_12 , A__ : Dict=24 , A__ : Optional[int]=8 , A__ : Optional[int]=None , A__ : Optional[int]="quick_gelu" , A__ : Any=0.1 , A__ : Union[str, Any]=0.1 , A__ : Dict=0.1 , A__ : List[str]=1E-5 , A__ : int=0.02 , A__ : List[str]=True , A__ : Optional[Any]=True , A__ : Dict=False , A__ : List[str]=False , A__ : str=False , **A__ : Union[str, Any] , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = vocab_size
snake_case_ : Tuple = n_positions
snake_case_ : Tuple = n_embd
snake_case_ : str = n_layer
snake_case_ : Optional[Any] = n_head
snake_case_ : Any = n_inner
snake_case_ : Dict = activation_function
snake_case_ : List[Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : int = attn_pdrop
snake_case_ : List[str] = layer_norm_epsilon
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = scale_attn_weights
snake_case_ : str = use_cache
snake_case_ : str = scale_attn_by_inverse_layer_idx
snake_case_ : Dict = reorder_and_upcast_attn
snake_case_ : List[Any] = tie_word_embeddings
super().__init__(tie_word_embeddings=A__ , **A__ )
class snake_case__ ( _UpperCamelCase ):
@property
def UpperCAmelCase__ ( self : int ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def UpperCAmelCase__ ( self : int , A__ : "FeatureExtractionMixin" , A__ : int = 1 , A__ : int = -1 , A__ : bool = False , A__ : Optional["TensorType"] = None , A__ : int = 3 , A__ : int = 32 , A__ : int = 32 , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self._generate_dummy_images(A__ , A__ , A__ , A__ )
snake_case_ : Optional[int] = dict(preprocessor(images=A__ , return_tensors=A__ ) )
return inputs
| 666 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Union[str, Any] , A__ : VQModel , A__ : UNetaDModel , A__ : DDIMScheduler ) -> List[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=A__ , unet=A__ , scheduler=A__ )
@torch.no_grad()
def __call__( self : str , A__ : int = 1 , A__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , A__ : float = 0.0 , A__ : int = 50 , A__ : Optional[str] = "pil" , A__ : bool = True , **A__ : Optional[Any] , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
snake_case_ : Optional[int] = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=A__ , )
snake_case_ : List[Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
snake_case_ : Any = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(A__ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
snake_case_ : Union[str, Any] = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case_ : List[Any] = {}
if accepts_eta:
snake_case_ : int = eta
for t in self.progress_bar(self.scheduler.timesteps ):
snake_case_ : Union[str, Any] = self.scheduler.scale_model_input(A__ , A__ )
# predict the noise residual
snake_case_ : Dict = self.unet(A__ , A__ ).sample
# compute the previous noisy sample x_t -> x_t-1
snake_case_ : Union[str, Any] = self.scheduler.step(A__ , A__ , A__ , **A__ ).prev_sample
# decode the image latents with the VAE
snake_case_ : int = self.vqvae.decode(A__ ).sample
snake_case_ : Dict = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ : Optional[int] = self.numpy_to_pil(A__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A__ )
| 666 | 1 |
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
UpperCAmelCase = "\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {'f1': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results['f1'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results['f1'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n >>> print(round(results['f1'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'f1': array([0.8, 0. , 0. ])}\n"
UpperCAmelCase = "\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case__ ( datasets.Metric ):
def UpperCAmelCase__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"] , )
def UpperCAmelCase__ ( self : Optional[int] , A__ : Any , A__ : str , A__ : int=None , A__ : Optional[int]=1 , A__ : Any="binary" , A__ : int=None ) -> Any:
'''simple docstring'''
snake_case_ : Any = fa_score(
A__ , A__ , labels=A__ , pos_label=A__ , average=A__ , sample_weight=A__ )
return {"f1": float(A__ ) if score.size == 1 else score}
| 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
snake_case_ : List[str] = precision
snake_case_ : Union[str, Any] = ceil(precision / 1_4 )
snake_case_ : List[str] = 4_2_6_8_8_0 * Decimal(1_0_0_0_5 ).sqrt()
snake_case_ : str = 1
snake_case_ : List[str] = 1_3_5_9_1_4_0_9
snake_case_ : str = Decimal(lowerCAmelCase_ )
for k in range(1 , lowerCAmelCase_ ):
snake_case_ : Tuple = factorial(6 * k ) // (factorial(3 * k ) * factorial(lowerCAmelCase_ ) ** 3)
linear_term += 5_4_5_1_4_0_1_3_4
exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
UpperCAmelCase = 5_0
print(F"The first {n} digits of pi is: {pi(n)}")
| 666 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: NDArray[floataa] , lowerCAmelCase_: NDArray[floataa] , lowerCAmelCase_: list[int] , lowerCAmelCase_: int , ):
snake_case_ ,snake_case_ : Optional[int] = coefficient_matrix.shape
snake_case_ ,snake_case_ : str = constant_matrix.shape
if rowsa != colsa:
snake_case_ : Dict = f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(lowerCAmelCase_ )
if colsa != 1:
snake_case_ : Union[str, Any] = f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(lowerCAmelCase_ )
if rowsa != rowsa:
snake_case_ : str = (
"Coefficient and constant matrices dimensions must be nxn and nx1 but "
f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
)
raise ValueError(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) != rowsa:
snake_case_ : List[str] = (
"Number of initial values must be equal to number of rows in coefficient "
f"matrix but received {len(lowerCAmelCase_ )} and {rowsa}"
)
raise ValueError(lowerCAmelCase_ )
if iterations <= 0:
raise ValueError("Iterations must be at least 1" )
snake_case_ : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
snake_case_ ,snake_case_ : List[Any] = table.shape
strictly_diagonally_dominant(lowerCAmelCase_ )
# Iterates the whole matrix for given number of times
for _ in range(lowerCAmelCase_ ):
snake_case_ : str = []
for row in range(lowerCAmelCase_ ):
snake_case_ : Optional[Any] = 0
for col in range(lowerCAmelCase_ ):
if col == row:
snake_case_ : str = table[row][col]
elif col == cols - 1:
snake_case_ : List[str] = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
snake_case_ : Any = (temp + val) / denom
new_val.append(lowerCAmelCase_ )
snake_case_ : int = new_val
return [float(lowerCAmelCase_ ) for i in new_val]
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: NDArray[floataa] ):
snake_case_ ,snake_case_ : List[Any] = table.shape
snake_case_ : int = True
for i in range(0 , lowerCAmelCase_ ):
snake_case_ : List[str] = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("Coefficient matrix is not strictly diagonally dominant" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = fa + fa
snake_case_ ,snake_case_ : str = fa, f
index += 1
for _ in str(lowerCAmelCase_ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 666 | 1 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: dict ):
snake_case_ : List[Any] = BeautifulSoup(requests.get(lowerCAmelCase_ , params=lowerCAmelCase_ ).content , "html.parser" )
snake_case_ : List[Any] = soup.find("div" , attrs={"class": "gs_ri"} )
snake_case_ : Optional[Any] = div.find("div" , attrs={"class": "gs_fl"} ).find_all("a" )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 3_0,
"pages": "3979-3990",
"year": 2_0_1_8,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(lowerCAmelCase_ )
or left < -len(lowerCAmelCase_ )
or right >= len(lowerCAmelCase_ )
or right < -len(lowerCAmelCase_ )
):
raise IndexError("list index out of range" )
if left == right:
return nums[left]
snake_case_ : List[Any] = (left + right) >> 1 # the middle
snake_case_ : Dict = find_max(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # find max in range[left, mid]
snake_case_ : int = find_max(lowerCAmelCase_ , mid + 1 , lowerCAmelCase_ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: dict ):
snake_case_ : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case_ : set[int] = set()
return any(
node not in visited and depth_first_search(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
for node in graph )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: dict , lowerCAmelCase_: int , lowerCAmelCase_: set , lowerCAmelCase_: set ):
visited.add(lowerCAmelCase_ )
rec_stk.add(lowerCAmelCase_ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(lowerCAmelCase_ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 666 | import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase = {
"vocab_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
),
},
"merges_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
),
},
"tokenizer_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
"roberta-base-openai-detector": (
"https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
),
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase = {
"roberta-base": 5_1_2,
"roberta-large": 5_1_2,
"roberta-large-mnli": 5_1_2,
"distilroberta-base": 5_1_2,
"roberta-base-openai-detector": 5_1_2,
"roberta-large-openai-detector": 5_1_2,
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE : int = ["input_ids", "attention_mask"]
_SCREAMING_SNAKE_CASE : List[str] = RobertaTokenizer
def __init__( self : Optional[int] , A__ : List[Any]=None , A__ : Optional[int]=None , A__ : List[str]=None , A__ : Dict="replace" , A__ : List[str]="<s>" , A__ : Optional[Any]="</s>" , A__ : List[str]="</s>" , A__ : List[Any]="<s>" , A__ : int="<unk>" , A__ : int="<pad>" , A__ : List[Any]="<mask>" , A__ : Any=False , A__ : Optional[int]=True , **A__ : Union[str, Any] , ) -> int:
'''simple docstring'''
super().__init__(
A__ , A__ , tokenizer_file=A__ , errors=A__ , bos_token=A__ , eos_token=A__ , sep_token=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , add_prefix_space=A__ , trim_offsets=A__ , **A__ , )
snake_case_ : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , A__ ) != add_prefix_space:
snake_case_ : List[Any] = getattr(A__ , pre_tok_state.pop("type" ) )
snake_case_ : Any = add_prefix_space
snake_case_ : List[Any] = pre_tok_class(**A__ )
snake_case_ : Optional[int] = add_prefix_space
snake_case_ : List[str] = "post_processor"
snake_case_ : Tuple = getattr(self.backend_tokenizer , A__ , A__ )
if tokenizer_component_instance:
snake_case_ : List[str] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
snake_case_ : str = tuple(state["sep"] )
if "cls" in state:
snake_case_ : Tuple = tuple(state["cls"] )
snake_case_ : Tuple = False
if state.get("add_prefix_space" , A__ ) != add_prefix_space:
snake_case_ : Optional[Any] = add_prefix_space
snake_case_ : str = True
if state.get("trim_offsets" , A__ ) != trim_offsets:
snake_case_ : Optional[int] = trim_offsets
snake_case_ : List[Any] = True
if changes_to_apply:
snake_case_ : int = getattr(A__ , state.pop("type" ) )
snake_case_ : List[Any] = component_class(**A__ )
setattr(self.backend_tokenizer , A__ , A__ )
@property
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Tuple , A__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else value
snake_case_ : Any = value
def UpperCAmelCase__ ( self : int , *A__ : Optional[Any] , **A__ : int ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Optional[Any] = kwargs.get("is_split_into_words" , A__ )
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(*A__ , **A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , *A__ : Any , **A__ : List[Any] ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Optional[int] = kwargs.get("is_split_into_words" , A__ )
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(*A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : str , A__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
snake_case_ : Optional[Any] = self._tokenizer.model.save(A__ , name=A__ )
return tuple(A__ )
def UpperCAmelCase__ ( self : int , A__ : List[str] , A__ : Union[str, Any]=None ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Dict , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case_ : str = [self.sep_token_id]
snake_case_ : List[Any] = [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 + sep + token_ids_a + sep ) * [0]
| 666 | 1 |
import random
class snake_case__ :
@staticmethod
def UpperCAmelCase__ ( A__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
snake_case_ : Optional[Any] = [ord(A__ ) for i in text]
snake_case_ : Any = []
snake_case_ : List[Any] = []
for i in plain:
snake_case_ : Optional[int] = random.randint(1 , 3_00 )
snake_case_ : Dict = (i + k) * k
cipher.append(A__ )
key.append(A__ )
return cipher, key
@staticmethod
def UpperCAmelCase__ ( A__ : list[int] , A__ : list[int] ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = []
for i in range(len(A__ ) ):
snake_case_ : List[Any] = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(A__ ) )
return "".join(A__ )
if __name__ == "__main__":
UpperCAmelCase , UpperCAmelCase = Onepad().encrypt("Hello")
print(c, k)
print(Onepad().decrypt(c, k))
| 666 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class snake_case__ ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Dict = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base" )
snake_case_ : Any = {
"input_ids": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
snake_case_ : List[str] = model(A__ )["last_hidden_state"]
snake_case_ : str = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , A__ )
# compare the actual values for a slice.
snake_case_ : List[str] = tf.convert_to_tensor(
[
[
[0.068_1762, 0.1089_4451, 0.0677_2504],
[-0.0642_3668, 0.0236_6615, 0.0432_9344],
[-0.0605_7295, 0.0997_4135, -0.0007_0584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 666 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = "openai-gpt"
_SCREAMING_SNAKE_CASE : Tuple = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Optional[int] , A__ : Optional[Any]=4_04_78 , A__ : List[Any]=5_12 , A__ : Optional[int]=7_68 , A__ : Union[str, Any]=12 , A__ : int=12 , A__ : Optional[int]="gelu" , A__ : Dict=0.1 , A__ : List[str]=0.1 , A__ : str=0.1 , A__ : str=1E-5 , A__ : Union[str, Any]=0.02 , A__ : Dict="cls_index" , A__ : Dict=True , A__ : Dict=None , A__ : Union[str, Any]=True , A__ : List[str]=0.1 , **A__ : Optional[int] , ) -> int:
'''simple docstring'''
snake_case_ : Dict = vocab_size
snake_case_ : Dict = n_positions
snake_case_ : int = n_embd
snake_case_ : List[Any] = n_layer
snake_case_ : str = n_head
snake_case_ : Any = afn
snake_case_ : str = resid_pdrop
snake_case_ : Tuple = embd_pdrop
snake_case_ : Tuple = attn_pdrop
snake_case_ : str = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : Optional[Any] = summary_type
snake_case_ : List[str] = summary_use_proj
snake_case_ : Any = summary_activation
snake_case_ : Optional[Any] = summary_first_dropout
snake_case_ : List[str] = summary_proj_to_labels
super().__init__(**A__ )
| 666 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCAmelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = ["pixel_values"]
def __init__( self : Union[str, Any] , A__ : bool = True , A__ : Dict[str, int] = None , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : bool = True , A__ : Dict[str, int] = None , A__ : bool = True , A__ : Union[int, float] = 1 / 2_55 , A__ : bool = True , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : bool = True , **A__ : Optional[int] , ) -> None:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : str = size if size is not None else {"shortest_edge": 2_24}
snake_case_ : Union[str, Any] = get_size_dict(A__ , default_to_square=A__ )
snake_case_ : List[Any] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
snake_case_ : Dict = get_size_dict(A__ , default_to_square=A__ , param_name="crop_size" )
snake_case_ : str = do_resize
snake_case_ : str = size
snake_case_ : Optional[Any] = resample
snake_case_ : Any = do_center_crop
snake_case_ : Any = crop_size
snake_case_ : str = do_rescale
snake_case_ : Optional[Any] = rescale_factor
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
snake_case_ : List[str] = image_std if image_std is not None else OPENAI_CLIP_STD
snake_case_ : int = do_convert_rgb
def UpperCAmelCase__ ( self : Optional[int] , A__ : np.ndarray , A__ : Dict[str, int] , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[str] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : str = get_size_dict(A__ , default_to_square=A__ )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
snake_case_ : str = get_resize_output_image_size(A__ , size=size["shortest_edge"] , default_to_square=A__ )
return resize(A__ , size=A__ , resample=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : np.ndarray , A__ : Dict[str, int] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[Any] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Optional[int] = get_size_dict(A__ )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
return center_crop(A__ , size=(size["height"], size["width"]) , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Optional[Any] , A__ : np.ndarray , A__ : Union[int, float] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[str] , ) -> str:
'''simple docstring'''
return rescale(A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Any , A__ : np.ndarray , A__ : Union[float, List[float]] , A__ : Union[float, List[float]] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Any , ) -> np.ndarray:
'''simple docstring'''
return normalize(A__ , mean=A__ , std=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : List[Any] , A__ : ImageInput , A__ : bool = None , A__ : Dict[str, int] = None , A__ : PILImageResampling = None , A__ : bool = None , A__ : int = None , A__ : bool = None , A__ : float = None , A__ : bool = None , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : bool = None , A__ : Optional[Union[str, TensorType]] = None , A__ : Optional[ChannelDimension] = ChannelDimension.FIRST , **A__ : Optional[Any] , ) -> PIL.Image.Image:
'''simple docstring'''
snake_case_ : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case_ : Union[str, Any] = size if size is not None else self.size
snake_case_ : Any = get_size_dict(A__ , param_name="size" , default_to_square=A__ )
snake_case_ : Optional[int] = resample if resample is not None else self.resample
snake_case_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ : List[str] = crop_size if crop_size is not None else self.crop_size
snake_case_ : Tuple = get_size_dict(A__ , param_name="crop_size" , default_to_square=A__ )
snake_case_ : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ : Any = image_mean if image_mean is not None else self.image_mean
snake_case_ : List[str] = image_std if image_std is not None else self.image_std
snake_case_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
snake_case_ : List[Any] = make_list_of_images(A__ )
if not valid_images(A__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
snake_case_ : Dict = [convert_to_rgb(A__ ) for image in images]
# All transformations expect numpy arrays.
snake_case_ : Dict = [to_numpy_array(A__ ) for image in images]
if do_resize:
snake_case_ : Dict = [self.resize(image=A__ , size=A__ , resample=A__ ) for image in images]
if do_center_crop:
snake_case_ : Tuple = [self.center_crop(image=A__ , size=A__ ) for image in images]
if do_rescale:
snake_case_ : str = [self.rescale(image=A__ , scale=A__ ) for image in images]
if do_normalize:
snake_case_ : int = [self.normalize(image=A__ , mean=A__ , std=A__ ) for image in images]
snake_case_ : List[Any] = [to_channel_dimension_format(A__ , A__ ) for image in images]
snake_case_ : Tuple = {"pixel_values": images}
return BatchFeature(data=A__ , tensor_type=A__ )
| 666 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCAmelCase = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
UpperCAmelCase = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
UpperCAmelCase = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any ):
def remove_articles(lowerCAmelCase_: Dict ):
snake_case_ : Optional[int] = re.compile(R"\b(a|an|the)\b" , re.UNICODE )
return re.sub(lowerCAmelCase_ , " " , lowerCAmelCase_ )
def white_space_fix(lowerCAmelCase_: Tuple ):
return " ".join(text.split() )
def remove_punc(lowerCAmelCase_: Dict ):
snake_case_ : Optional[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCAmelCase_: int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_ ) ) ) )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: Union[str, Any] ):
return int(normalize_answer(lowerCAmelCase_ ) == normalize_answer(lowerCAmelCase_ ) )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[str] , lowerCAmelCase_: Optional[Any] ):
snake_case_ : List[str] = [any(compute_exact(lowerCAmelCase_ , lowerCAmelCase_ ) for ref in refs ) for pred, refs in zip(lowerCAmelCase_ , lowerCAmelCase_ )]
return (sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ )) * 1_0_0
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: List[Any] , lowerCAmelCase_: str , lowerCAmelCase_: List[Any] ):
snake_case_ : Dict = [rgram for rgrams in rgramslist for rgram in rgrams]
snake_case_ : Any = Counter(lowerCAmelCase_ )
snake_case_ : Optional[Any] = Counter(lowerCAmelCase_ )
snake_case_ : List[str] = Counter()
for sgram, scount in sgramcounter.items():
snake_case_ : Optional[Any] = scount * numref
snake_case_ : int = Counter(lowerCAmelCase_ )
snake_case_ : str = Counter()
for cgram, ccount in cgramcounter.items():
snake_case_ : Union[str, Any] = ccount * numref
# KEEP
snake_case_ : Dict = sgramcounter_rep & cgramcounter_rep
snake_case_ : Optional[int] = keepgramcounter_rep & rgramcounter
snake_case_ : List[str] = sgramcounter_rep & rgramcounter
snake_case_ : Any = 0
snake_case_ : Tuple = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
snake_case_ : List[Any] = 1
snake_case_ : Any = 1
if len(lowerCAmelCase_ ) > 0:
snake_case_ : List[Any] = keeptmpscorea / len(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
snake_case_ : int = keeptmpscorea / sum(keepgramcounterall_rep.values() )
snake_case_ : Any = 0
if keepscore_precision > 0 or keepscore_recall > 0:
snake_case_ : List[Any] = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
snake_case_ : Optional[Any] = sgramcounter_rep - cgramcounter_rep
snake_case_ : str = delgramcounter_rep - rgramcounter
snake_case_ : Optional[int] = sgramcounter_rep - rgramcounter
snake_case_ : Optional[int] = 0
snake_case_ : List[Any] = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
snake_case_ : str = 1
if len(lowerCAmelCase_ ) > 0:
snake_case_ : Union[str, Any] = deltmpscorea / len(lowerCAmelCase_ )
# ADDITION
snake_case_ : int = set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )
snake_case_ : str = set(lowerCAmelCase_ ) & set(lowerCAmelCase_ )
snake_case_ : int = set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )
snake_case_ : int = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
snake_case_ : Dict = 1
snake_case_ : List[Any] = 1
if len(lowerCAmelCase_ ) > 0:
snake_case_ : Union[str, Any] = addtmpscore / len(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
snake_case_ : Optional[int] = addtmpscore / len(lowerCAmelCase_ )
snake_case_ : List[str] = 0
if addscore_precision > 0 or addscore_recall > 0:
snake_case_ : Union[str, Any] = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[int] , lowerCAmelCase_: Dict , lowerCAmelCase_: List[Any] ):
snake_case_ : Union[str, Any] = len(lowerCAmelCase_ )
snake_case_ : List[Any] = ssent.split(" " )
snake_case_ : List[Any] = csent.split(" " )
snake_case_ : List[Any] = []
snake_case_ : Optional[int] = []
snake_case_ : Tuple = []
snake_case_ : List[str] = []
snake_case_ : Tuple = []
snake_case_ : Tuple = []
snake_case_ : Union[str, Any] = []
snake_case_ : Dict = []
snake_case_ : List[Any] = []
snake_case_ : Union[str, Any] = []
for rsent in rsents:
snake_case_ : Optional[Any] = rsent.split(" " )
snake_case_ : Dict = []
snake_case_ : Dict = []
snake_case_ : int = []
ragramslist.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
snake_case_ : Optional[int] = ragrams[i] + " " + ragrams[i + 1]
ragrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
snake_case_ : Dict = ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
ragrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
snake_case_ : Optional[int] = ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
ragrams.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
snake_case_ : List[Any] = sagrams[i] + " " + sagrams[i + 1]
sagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
snake_case_ : Optional[int] = sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
sagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
snake_case_ : Any = sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
sagrams.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
snake_case_ : Any = cagrams[i] + " " + cagrams[i + 1]
cagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
snake_case_ : Optional[int] = cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
cagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
snake_case_ : str = cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
cagrams.append(lowerCAmelCase_ )
((snake_case_) ,(snake_case_) ,(snake_case_)) : Any = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
((snake_case_) ,(snake_case_) ,(snake_case_)) : Union[str, Any] = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
((snake_case_) ,(snake_case_) ,(snake_case_)) : int = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
((snake_case_) ,(snake_case_) ,(snake_case_)) : Optional[Any] = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : List[Any] = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
snake_case_ : Optional[Any] = sum([delascore, delascore, delascore, delascore] ) / 4
snake_case_ : List[str] = sum([addascore, addascore, addascore, addascore] ) / 4
snake_case_ : List[str] = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: bool = True , lowerCAmelCase_: str = "13a" , lowerCAmelCase_: bool = True ):
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
snake_case_ : int = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
snake_case_ : str = sacrebleu.metrics.bleu._get_tokenizer(lowerCAmelCase_ )()(lowerCAmelCase_ )
else:
snake_case_ : Tuple = sacrebleu.TOKENIZERS[tokenizer]()(lowerCAmelCase_ )
elif tokenizer == "moses":
snake_case_ : Optional[Any] = sacremoses.MosesTokenizer().tokenize(lowerCAmelCase_ , return_str=lowerCAmelCase_ , escape=lowerCAmelCase_ )
elif tokenizer == "penn":
snake_case_ : Tuple = sacremoses.MosesTokenizer().penn_tokenize(lowerCAmelCase_ , return_str=lowerCAmelCase_ )
else:
snake_case_ : int = sentence
if not return_str:
snake_case_ : Optional[Any] = normalized_sent.split()
return normalized_sent
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: Optional[Any] ):
if not (len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )):
raise ValueError("Sources length must match predictions and references lengths." )
snake_case_ : List[Any] = 0
for src, pred, refs in zip(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
sari_score += SARIsent(normalize(lowerCAmelCase_ ) , normalize(lowerCAmelCase_ ) , [normalize(lowerCAmelCase_ ) for sent in refs] )
snake_case_ : str = sari_score / len(lowerCAmelCase_ )
return 1_0_0 * sari_score
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: str="exp" , lowerCAmelCase_: List[Any]=None , lowerCAmelCase_: Dict=False , lowerCAmelCase_: Tuple=False , lowerCAmelCase_: Optional[int]=False , ):
snake_case_ : Dict = len(references[0] )
if any(len(lowerCAmelCase_ ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
snake_case_ : Union[str, Any] = [[refs[i] for refs in references] for i in range(lowerCAmelCase_ )]
snake_case_ : Dict = sacrebleu.corpus_bleu(
lowerCAmelCase_ , lowerCAmelCase_ , smooth_method=lowerCAmelCase_ , smooth_value=lowerCAmelCase_ , force=lowerCAmelCase_ , lowercase=lowerCAmelCase_ , use_effective_order=lowerCAmelCase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case__ ( datasets.Metric ):
def UpperCAmelCase__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Sequence(datasets.Value("string" , id="sequence" ) , id="references" ),
} ) , codebase_urls=[
"https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
"https://github.com/cocoxu/simplification/blob/master/SARI.py",
"https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
"https://github.com/mjpost/sacreBLEU",
] , reference_urls=[
"https://www.aclweb.org/anthology/Q16-1029.pdf",
"https://github.com/mjpost/sacreBLEU",
"https://en.wikipedia.org/wiki/BLEU",
"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
] , )
def UpperCAmelCase__ ( self : str , A__ : Union[str, Any] , A__ : Union[str, Any] , A__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Dict = {}
result.update({"sari": compute_sari(sources=A__ , predictions=A__ , references=A__ )} )
result.update({"sacrebleu": compute_sacrebleu(predictions=A__ , references=A__ )} )
result.update({"exact": compute_em(predictions=A__ , references=A__ )} )
return result
| 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
snake_case_ : Union[str, Any] = []
for position in positions:
snake_case_ ,snake_case_ : Union[str, Any] = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(lowerCAmelCase_ )
return permissible_positions
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[list[int]] ):
return not any(elem == 0 for row in board for elem in row )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[list[int]] , lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
if is_complete(lowerCAmelCase_ ):
return True
for position in get_valid_pos(lowerCAmelCase_ , len(lowerCAmelCase_ ) ):
snake_case_ ,snake_case_ : Dict = position
if board[y][x] == 0:
snake_case_ : List[str] = curr + 1
if open_knight_tour_helper(lowerCAmelCase_ , lowerCAmelCase_ , curr + 1 ):
return True
snake_case_ : Dict = 0
return False
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = [[0 for i in range(lowerCAmelCase_ )] for j in range(lowerCAmelCase_ )]
for i in range(lowerCAmelCase_ ):
for j in range(lowerCAmelCase_ ):
snake_case_ : Optional[Any] = 1
if open_knight_tour_helper(lowerCAmelCase_ , (i, j) , 1 ):
return board
snake_case_ : Dict = 0
snake_case_ : str = f"Open Kight Tour cannot be performed on a board of size {n}"
raise ValueError(lowerCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
UpperCAmelCase = "bert-base-cased"
UpperCAmelCase = "google/pegasus-xsum"
UpperCAmelCase = [" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCAmelCase = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCAmelCase = "patrickvonplaten/t5-tiny-random"
UpperCAmelCase = "sshleifer/bart-tiny-random"
UpperCAmelCase = "sshleifer/tiny-mbart"
UpperCAmelCase = "sshleifer/tiny-marian-en-de"
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Path , lowerCAmelCase_: list ):
snake_case_ : int = "\n".join(lowerCAmelCase_ )
Path(lowerCAmelCase_ ).open("w" ).writelines(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(lowerCAmelCase_ , f"{split}.source" ) , lowerCAmelCase_ )
_dump_articles(os.path.join(lowerCAmelCase_ , f"{split}.target" ) , lowerCAmelCase_ )
return tmp_dir
class snake_case__ ( _UpperCamelCase ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def UpperCAmelCase__ ( self : Dict , A__ : Dict ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = AutoTokenizer.from_pretrained(A__ )
snake_case_ : int = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
snake_case_ : Tuple = max(len(tokenizer.encode(A__ ) ) for a in ARTICLES )
snake_case_ : Tuple = max(len(tokenizer.encode(A__ ) ) for a in SUMMARIES )
snake_case_ : Optional[int] = 4
snake_case_ : str = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
snake_case_ ,snake_case_ : Any = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
snake_case_ : int = SeqaSeqDataset(
A__ , data_dir=A__ , type_path="train" , max_source_length=A__ , max_target_length=A__ , src_lang=A__ , tgt_lang=A__ , )
snake_case_ : Optional[int] = DataLoader(A__ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(A__ , A__ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
snake_case_ : Any = shift_tokens_right(batch["labels"] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def UpperCAmelCase__ ( self : Dict , A__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained(A__ )
snake_case_ : Optional[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
snake_case_ : int = max(len(tokenizer.encode(A__ ) ) for a in ARTICLES )
snake_case_ : Any = max(len(tokenizer.encode(A__ ) ) for a in SUMMARIES )
snake_case_ : Optional[int] = 4
snake_case_ : List[str] = LegacySeqaSeqDataset(
A__ , data_dir=A__ , type_path="train" , max_source_length=20 , max_target_length=A__ , )
snake_case_ : Dict = DataLoader(A__ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def UpperCAmelCase__ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
snake_case_ : Tuple = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
snake_case_ : Union[str, Any] = tmp_dir.joinpath("train.source" ).open().readlines()
snake_case_ : List[Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(A__ , A__ , 1_28 , A__ )
snake_case_ : Union[str, Any] = {x.name for x in tmp_dir.iterdir()}
snake_case_ : List[str] = {x.name for x in save_dir.iterdir()}
snake_case_ : Dict = save_dir.joinpath("train.source" ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(A__ ) < len(A__ )
assert len(A__ ) == 1
assert len(packed_examples[0] ) == sum(len(A__ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason="This test requires fairseq" )
def UpperCAmelCase__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
snake_case_ ,snake_case_ ,snake_case_ : List[str] = self._get_dataset(max_len=64 )
snake_case_ : Dict = 64
snake_case_ : Optional[Any] = ds.make_dynamic_sampler(A__ , required_batch_size_multiple=A__ )
snake_case_ : Optional[int] = [len(A__ ) for x in batch_sampler]
assert len(set(A__ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(A__ ) == len(A__ ) # no dropped or added examples
snake_case_ : Any = DataLoader(A__ , batch_sampler=A__ , collate_fn=ds.collate_fn , num_workers=2 )
snake_case_ : Any = []
snake_case_ : Tuple = []
for batch in data_loader:
snake_case_ : str = batch["input_ids"].shape
snake_case_ : Any = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
snake_case_ : Tuple = np.product(batch["input_ids"].shape )
num_src_per_batch.append(A__ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(A__ )
assert num_src_per_batch[0] == max(A__ )
if failures:
raise AssertionError(f"too many tokens in {len(A__ )} batches" )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
snake_case_ ,snake_case_ ,snake_case_ : str = self._get_dataset(max_len=5_12 )
snake_case_ : str = 2
snake_case_ : List[Any] = ds.make_sortish_sampler(A__ , shuffle=A__ )
snake_case_ : Optional[Any] = DataLoader(A__ , batch_size=A__ , collate_fn=ds.collate_fn , num_workers=2 )
snake_case_ : Any = DataLoader(A__ , batch_size=A__ , collate_fn=ds.collate_fn , num_workers=2 , sampler=A__ )
snake_case_ : Union[str, Any] = tokenizer.pad_token_id
def count_pad_tokens(A__ : Optional[int] , A__ : List[Any]="input_ids" ):
return [batch[k].eq(A__ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(A__ , k="labels" ) ) < sum(count_pad_tokens(A__ , k="labels" ) )
assert sum(count_pad_tokens(A__ ) ) < sum(count_pad_tokens(A__ ) )
assert len(A__ ) == len(A__ )
def UpperCAmelCase__ ( self : str , A__ : Dict=10_00 , A__ : Dict=1_28 ) -> Dict:
'''simple docstring'''
if os.getenv("USE_REAL_DATA" , A__ ):
snake_case_ : Union[str, Any] = "examples/seq2seq/wmt_en_ro"
snake_case_ : Tuple = max_len * 2 * 64
if not Path(A__ ).joinpath("train.len" ).exists():
save_len_file(A__ , A__ )
else:
snake_case_ : Tuple = "examples/seq2seq/test_data/wmt_en_ro"
snake_case_ : Tuple = max_len * 4
save_len_file(A__ , A__ )
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained(A__ )
snake_case_ : List[Any] = SeqaSeqDataset(
A__ , data_dir=A__ , type_path="train" , max_source_length=A__ , max_target_length=A__ , n_obs=A__ , )
return ds, max_tokens, tokenizer
def UpperCAmelCase__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ ,snake_case_ ,snake_case_ : List[Any] = self._get_dataset()
snake_case_ : Union[str, Any] = set(DistributedSortishSampler(A__ , 2_56 , num_replicas=2 , rank=0 , add_extra_examples=A__ ) )
snake_case_ : List[Any] = set(DistributedSortishSampler(A__ , 2_56 , num_replicas=2 , rank=1 , add_extra_examples=A__ ) )
assert idsa.intersection(A__ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def UpperCAmelCase__ ( self : Optional[int] , A__ : Optional[int] ) -> Any:
'''simple docstring'''
snake_case_ : Dict = AutoTokenizer.from_pretrained(A__ , use_fast=A__ )
if tok_name == MBART_TINY:
snake_case_ : int = SeqaSeqDataset(
A__ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , src_lang="EN" , tgt_lang="FR" , )
snake_case_ : str = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
snake_case_ : Dict = SeqaSeqDataset(
A__ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , )
snake_case_ : Tuple = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(A__ ) == 1 if tok_name == BART_TINY else len(A__ ) == 0
| 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , A__ : Any=24 , A__ : List[Any]=16 , A__ : List[Any]=40_96 , A__ : int="gelu" , A__ : List[str]=0.1 , A__ : List[str]=0.1 , A__ : str=5_12 , A__ : int=0.02 , A__ : Any=1E-12 , A__ : Optional[Any]=0 , A__ : List[str]=2 , A__ : Optional[int]=1 , A__ : str="absolute" , A__ : Any=True , **A__ : Optional[Any] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
snake_case_ : str = vocab_size
snake_case_ : int = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = intermediate_size
snake_case_ : str = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : str = max_position_embeddings
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Dict = use_cache
| 666 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
UpperCAmelCase = logging.get_logger(__name__)
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = "mask2former"
_SCREAMING_SNAKE_CASE : Optional[int] = ["swin"]
_SCREAMING_SNAKE_CASE : Any = {"hidden_size": "hidden_dim"}
def __init__( self : int , A__ : Optional[Dict] = None , A__ : int = 2_56 , A__ : int = 2_56 , A__ : int = 2_56 , A__ : int = 10_24 , A__ : str = "relu" , A__ : int = 6 , A__ : int = 10 , A__ : int = 8 , A__ : float = 0.0 , A__ : int = 20_48 , A__ : bool = False , A__ : bool = False , A__ : int = 4 , A__ : int = 2_55 , A__ : int = 1_00 , A__ : float = 0.1 , A__ : float = 2.0 , A__ : float = 5.0 , A__ : float = 5.0 , A__ : int = 1_25_44 , A__ : float = 3.0 , A__ : float = 0.75 , A__ : float = 0.02 , A__ : float = 1.0 , A__ : bool = True , A__ : List[int] = [4, 8, 16, 32] , A__ : bool = None , **A__ : Optional[int] , ) -> str:
'''simple docstring'''
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
snake_case_ : Dict = CONFIG_MAPPING["swin"](
image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=A__ , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(A__ , A__ ):
snake_case_ : Optional[int] = backbone_config.pop("model_type" )
snake_case_ : Dict = CONFIG_MAPPING[backbone_model_type]
snake_case_ : Tuple = config_class.from_dict(A__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
f"Supported model types: {','.join(self.backbones_supported )}" )
snake_case_ : Optional[int] = backbone_config
snake_case_ : Tuple = feature_size
snake_case_ : int = mask_feature_size
snake_case_ : Optional[Any] = hidden_dim
snake_case_ : Optional[Any] = encoder_feedforward_dim
snake_case_ : str = activation_function
snake_case_ : str = encoder_layers
snake_case_ : Optional[Any] = decoder_layers
snake_case_ : str = num_attention_heads
snake_case_ : Union[str, Any] = dropout
snake_case_ : Dict = dim_feedforward
snake_case_ : List[str] = pre_norm
snake_case_ : Union[str, Any] = enforce_input_projection
snake_case_ : List[Any] = common_stride
snake_case_ : str = ignore_value
snake_case_ : str = num_queries
snake_case_ : Optional[int] = no_object_weight
snake_case_ : Optional[int] = class_weight
snake_case_ : Tuple = mask_weight
snake_case_ : Tuple = dice_weight
snake_case_ : int = train_num_points
snake_case_ : Tuple = oversample_ratio
snake_case_ : Union[str, Any] = importance_sample_ratio
snake_case_ : Any = init_std
snake_case_ : Union[str, Any] = init_xavier_std
snake_case_ : Dict = use_auxiliary_loss
snake_case_ : Optional[Any] = feature_strides
snake_case_ : Union[str, Any] = output_auxiliary_logits
snake_case_ : Optional[int] = decoder_layers
super().__init__(**A__ )
@classmethod
def UpperCAmelCase__ ( cls : Dict , A__ : PretrainedConfig , **A__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return cls(
backbone_config=A__ , **A__ , )
def UpperCAmelCase__ ( self : List[str] ) -> Dict[str, any]:
'''simple docstring'''
snake_case_ : Optional[int] = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.backbone_config.to_dict()
snake_case_ : List[Any] = self.__class__.model_type
return output
| 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : str = [True] * (end + 1)
snake_case_ : Any = []
while start <= end:
if temp[start] is True:
in_prime.append(lowerCAmelCase_ )
for i in range(start * start , end + 1 , lowerCAmelCase_ ):
snake_case_ : Union[str, Any] = False
start += 1
prime += in_prime
snake_case_ : Dict = end + 1
snake_case_ : Dict = min(2 * end , lowerCAmelCase_ )
while low <= n:
snake_case_ : Any = [True] * (high - low + 1)
for each in in_prime:
snake_case_ : Optional[Any] = math.floor(low / each ) * each
if t < low:
t += each
for j in range(lowerCAmelCase_ , high + 1 , lowerCAmelCase_ ):
snake_case_ : List[Any] = False
for j in range(len(lowerCAmelCase_ ) ):
if temp[j] is True:
prime.append(j + low )
snake_case_ : int = high + 1
snake_case_ : Union[str, Any] = min(high + end , lowerCAmelCase_ )
return prime
print(sieve(1_0**6))
| 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: int ):
snake_case_ : Tuple = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Optional[int] = n - k
# Calculate C(n,k)
for i in range(lowerCAmelCase_ ):
result *= n - i
result //= i + 1
return result
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
return binomial_coefficient(2 * node_count , lowerCAmelCase_ ) // (node_count + 1)
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if n < 0:
raise ValueError("factorial() not defined for negative values" )
snake_case_ : List[str] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
return catalan_number(lowerCAmelCase_ ) * factorial(lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = int(input("Enter the number of nodes: ").strip() or 0)
if node_count <= 0:
raise ValueError("We need some nodes to work with.")
print(
F"Given {node_count} nodes, there are {binary_tree_count(node_count)} "
F"binary trees and {catalan_number(node_count)} binary search trees."
)
| 666 | 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 ConditionalDetrImageProcessor
class snake_case__ ( unittest.TestCase ):
def __init__( self : List[str] , A__ : List[Any] , A__ : int=7 , A__ : Union[str, Any]=3 , A__ : List[str]=30 , A__ : Optional[int]=4_00 , A__ : Optional[Any]=True , A__ : Optional[int]=None , A__ : Optional[Any]=True , A__ : Any=[0.5, 0.5, 0.5] , A__ : int=[0.5, 0.5, 0.5] , A__ : Any=True , A__ : int=1 / 2_55 , A__ : List[str]=True , ) -> Dict:
'''simple docstring'''
snake_case_ : int = size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
snake_case_ : Any = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : List[Any] = max_resolution
snake_case_ : Tuple = do_resize
snake_case_ : Dict = size
snake_case_ : Optional[Any] = do_normalize
snake_case_ : int = image_mean
snake_case_ : List[Any] = image_std
snake_case_ : Tuple = do_rescale
snake_case_ : Any = rescale_factor
snake_case_ : Optional[int] = do_pad
def UpperCAmelCase__ ( self : int ) -> List[str]:
'''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 UpperCAmelCase__ ( self : Optional[int] , A__ : Optional[int] , A__ : Any=False ) -> Optional[Any]:
'''simple docstring'''
if not batched:
snake_case_ : Any = image_inputs[0]
if isinstance(A__ , Image.Image ):
snake_case_ ,snake_case_ : Dict = image.size
else:
snake_case_ ,snake_case_ : int = image.shape[1], image.shape[2]
if w < h:
snake_case_ : Dict = int(self.size["shortest_edge"] * h / w )
snake_case_ : Optional[int] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : List[Any] = self.size["shortest_edge"]
else:
snake_case_ : str = []
for image in image_inputs:
snake_case_ ,snake_case_ : Tuple = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : List[Any] = max(A__ , key=lambda A__ : item[0] )[0]
snake_case_ : int = max(A__ , key=lambda A__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = ConditionalDetrImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = ConditionalDetrImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Any ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A__ , "image_mean" ) )
self.assertTrue(hasattr(A__ , "image_std" ) )
self.assertTrue(hasattr(A__ , "do_normalize" ) )
self.assertTrue(hasattr(A__ , "do_resize" ) )
self.assertTrue(hasattr(A__ , "size" ) )
def UpperCAmelCase__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = 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 , A__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , A__ )
def UpperCAmelCase__ ( self : str ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : Optional[Any] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ ,snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
snake_case_ : int = image_processing(A__ , 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 UpperCAmelCase__ ( self : int ) -> Any:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , numpify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , np.ndarray )
# Test not batched input
snake_case_ : int = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : List[str] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Optional[int] = image_processing(A__ , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : Dict = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self : Tuple ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , torchify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , torch.Tensor )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Any = image_processing(A__ , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : int = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : Optional[Any] = json.loads(f.read() )
snake_case_ : int = {"image_id": 3_97_69, "annotations": target}
# encode them
snake_case_ : Optional[int] = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
snake_case_ : Any = image_processing(images=A__ , annotations=A__ , return_tensors="pt" )
# verify pixel values
snake_case_ : List[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , A__ )
snake_case_ : List[str] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , A__ , atol=1E-4 ) )
# verify area
snake_case_ : Tuple = 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"] , A__ ) )
# verify boxes
snake_case_ : Any = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , A__ )
snake_case_ : str = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , A__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , A__ ) )
# verify is_crowd
snake_case_ : Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , A__ ) )
# verify class_labels
snake_case_ : Any = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , A__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , A__ ) )
# verify size
snake_case_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , A__ ) )
@slow
def UpperCAmelCase__ ( self : int ) -> str:
'''simple docstring'''
snake_case_ : Tuple = 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_ : Any = json.loads(f.read() )
snake_case_ : Optional[Any] = {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}
snake_case_ : int = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : Union[str, Any] = ConditionalDetrImageProcessor(format="coco_panoptic" )
snake_case_ : str = image_processing(images=A__ , annotations=A__ , masks_path=A__ , return_tensors="pt" )
# verify pixel values
snake_case_ : int = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , A__ )
snake_case_ : str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , A__ , atol=1E-4 ) )
# verify area
snake_case_ : Optional[int] = 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"] , A__ ) )
# verify boxes
snake_case_ : str = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , A__ )
snake_case_ : str = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , A__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , A__ ) )
# verify is_crowd
snake_case_ : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , A__ ) )
# verify class_labels
snake_case_ : Optional[int] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , A__ ) )
# verify masks
snake_case_ : Union[str, Any] = 82_28_73
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , A__ )
# verify orig_size
snake_case_ : Dict = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , A__ ) )
# verify size
snake_case_ : str = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , A__ ) )
| 666 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string" )} ) , supervised_keys=A__ , )
def UpperCAmelCase__ ( self : Optional[Any] , A__ : str , A__ : str ) -> Optional[int]:
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_dummy_examples()} )]
def UpperCAmelCase__ ( self : int , A__ : Optional[int] , A__ : Dict ) -> Optional[Any]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A__ )
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) , supervised_keys=A__ , )
def UpperCAmelCase__ ( self : Any , A__ : List[str] , A__ : str ) -> Optional[int]:
'''simple docstring'''
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_nested_examples()} )
]
def UpperCAmelCase__ ( self : List[Any] , A__ : List[str] , A__ : Optional[int] ) -> List[str]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A__ )
def SCREAMING_SNAKE_CASE_ ( ):
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"] )]
def SCREAMING_SNAKE_CASE_ ( ):
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"] )]
class snake_case__ ( _UpperCamelCase ):
@require_beam
def UpperCAmelCase__ ( self : str ) -> List[str]:
'''simple docstring'''
snake_case_ : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : Dict = DummyBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
snake_case_ : Optional[int] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
self.assertDictEqual(dset["train"][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
import apache_beam as beam
snake_case_ : Tuple = beam.io.parquetio.WriteToParquet
snake_case_ : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : List[Any] = DummyBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
snake_case_ : int = partial(A__ , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
snake_case_ : Optional[Any] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"] ) , sorted(["foo", "bar", "foobar"] ) )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : Tuple = DummyBeamDataset(cache_dir=A__ )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : List[str] = NestedBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
snake_case_ : int = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
self.assertDictEqual(dset["train"][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
| 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class snake_case__ :
_SCREAMING_SNAKE_CASE : str = field(
default=_UpperCamelCase , metadata={"help": "Model type selected in the list: " + ", ".join(_UpperCamelCase )} )
_SCREAMING_SNAKE_CASE : str = field(
default=_UpperCamelCase , metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
_SCREAMING_SNAKE_CASE : int = field(
default=1_2_8 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_SCREAMING_SNAKE_CASE : int = field(
default=1_2_8 , metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."} , )
_SCREAMING_SNAKE_CASE : int = field(
default=6_4 , metadata={
"help": (
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
)
} , )
_SCREAMING_SNAKE_CASE : int = field(
default=3_0 , metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
} , )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
_SCREAMING_SNAKE_CASE : float = field(
default=0.0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
_SCREAMING_SNAKE_CASE : int = field(
default=2_0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
_SCREAMING_SNAKE_CASE : int = field(
default=0 , metadata={
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
} , )
_SCREAMING_SNAKE_CASE : int = field(default=1 , metadata={"help": "multiple threads for converting example to features"} )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Tuple = "train"
_SCREAMING_SNAKE_CASE : Any = "dev"
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : SquadDataTrainingArguments
_SCREAMING_SNAKE_CASE : List[SquadFeatures]
_SCREAMING_SNAKE_CASE : Split
_SCREAMING_SNAKE_CASE : bool
def __init__( self : str , A__ : SquadDataTrainingArguments , A__ : PreTrainedTokenizer , A__ : Optional[int] = None , A__ : Union[str, Split] = Split.train , A__ : Optional[bool] = False , A__ : Optional[str] = None , A__ : Optional[str] = "pt" , ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = args
snake_case_ : int = is_language_sensitive
snake_case_ : int = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(A__ , A__ ):
try:
snake_case_ : List[str] = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
snake_case_ : Tuple = mode
# Load data features from cache or dataset file
snake_case_ : Dict = "v2" if args.version_2_with_negative else "v1"
snake_case_ : List[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case_ : List[Any] = cached_features_file + ".lock"
with FileLock(A__ ):
if os.path.exists(A__ ) and not args.overwrite_cache:
snake_case_ : int = time.time()
snake_case_ : List[Any] = torch.load(A__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case_ : Tuple = self.old_features["features"]
snake_case_ : List[str] = self.old_features.get("dataset" , A__ )
snake_case_ : Tuple = self.old_features.get("examples" , A__ )
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
" future run" )
else:
if mode == Split.dev:
snake_case_ : Tuple = self.processor.get_dev_examples(args.data_dir )
else:
snake_case_ : Tuple = self.processor.get_train_examples(args.data_dir )
snake_case_ ,snake_case_ : Optional[Any] = squad_convert_examples_to_features(
examples=self.examples , tokenizer=A__ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=A__ , )
snake_case_ : Any = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , A__ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )
def __len__( self : str ) -> Dict:
'''simple docstring'''
return len(self.features )
def __getitem__( self : Optional[int] , A__ : Optional[int] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
snake_case_ : Any = self.features[i]
snake_case_ : Optional[int] = torch.tensor(feature.input_ids , dtype=torch.long )
snake_case_ : Union[str, Any] = torch.tensor(feature.attention_mask , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.token_type_ids , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.cls_index , dtype=torch.long )
snake_case_ : str = torch.tensor(feature.p_mask , dtype=torch.float )
snake_case_ : str = torch.tensor(feature.is_impossible , dtype=torch.float )
snake_case_ : Optional[int] = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case_ : Any = torch.tensor(feature.start_position , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 666 | 1 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
UpperCAmelCase = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
UpperCAmelCase = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case__ ( datasets.Metric ):
def UpperCAmelCase__ ( self : List[Any] ) -> Any:
'''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" ),
} ) , reference_urls=[] , )
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : str , A__ : Dict , A__ : Dict=None , A__ : int=False , A__ : Union[str, Any]=False , A__ : int=False , ) -> int:
'''simple docstring'''
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
snake_case_ : Any = np.array([re.sub(A__ , "" , A__ ) for x in predictions] )
snake_case_ : Optional[Any] = np.array([re.sub(A__ , "" , A__ ) for x in references] )
else:
snake_case_ : Optional[int] = np.asarray(A__ )
snake_case_ : List[str] = np.asarray(A__ )
if ignore_case:
snake_case_ : Dict = np.char.lower(A__ )
snake_case_ : Optional[int] = np.char.lower(A__ )
if ignore_punctuation:
snake_case_ : Union[str, Any] = string.punctuation.maketrans("" , "" , string.punctuation )
snake_case_ : Dict = np.char.translate(A__ , table=A__ )
snake_case_ : List[str] = np.char.translate(A__ , table=A__ )
if ignore_numbers:
snake_case_ : Optional[Any] = string.digits.maketrans("" , "" , string.digits )
snake_case_ : List[Any] = np.char.translate(A__ , table=A__ )
snake_case_ : Union[str, Any] = np.char.translate(A__ , table=A__ )
snake_case_ : str = predictions == references
return {"exact_match": np.mean(A__ ) * 1_00}
| 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "git_vision_model"
def __init__( self : int , A__ : Union[str, Any]=7_68 , A__ : List[Any]=30_72 , A__ : Tuple=12 , A__ : Optional[Any]=12 , A__ : Optional[int]=3 , A__ : List[str]=2_24 , A__ : Dict=16 , A__ : int="quick_gelu" , A__ : Any=1E-5 , A__ : Tuple=0.0 , A__ : Optional[int]=0.02 , **A__ : List[str] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : str = intermediate_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = patch_size
snake_case_ : List[str] = image_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Any = attention_dropout
snake_case_ : Any = layer_norm_eps
snake_case_ : int = hidden_act
@classmethod
def UpperCAmelCase__ ( cls : List[Any] , A__ : Union[str, os.PathLike] , **A__ : Optional[int] ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(A__ )
snake_case_ ,snake_case_ : Tuple = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
snake_case_ : Any = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(A__ , **A__ )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = "git"
def __init__( self : Any , A__ : List[str]=None , A__ : List[str]=3_05_22 , A__ : Tuple=7_68 , A__ : Tuple=6 , A__ : str=12 , A__ : Any=30_72 , A__ : List[str]="gelu" , A__ : int=0.1 , A__ : Dict=0.1 , A__ : Any=10_24 , A__ : Optional[Any]=0.02 , A__ : Optional[Any]=1E-12 , A__ : Dict=0 , A__ : Any="absolute" , A__ : Tuple=True , A__ : Any=False , A__ : Tuple=1_01 , A__ : Tuple=1_02 , A__ : List[Any]=None , **A__ : List[str] , ) -> int:
'''simple docstring'''
super().__init__(bos_token_id=A__ , eos_token_id=A__ , pad_token_id=A__ , **A__ )
if vision_config is None:
snake_case_ : int = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
snake_case_ : str = GitVisionConfig(**A__ )
snake_case_ : int = vocab_size
snake_case_ : List[Any] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = hidden_act
snake_case_ : Dict = intermediate_size
snake_case_ : Any = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[str] = initializer_range
snake_case_ : List[str] = layer_norm_eps
snake_case_ : Any = position_embedding_type
snake_case_ : Union[str, Any] = use_cache
snake_case_ : str = tie_word_embeddings
snake_case_ : List[Any] = num_image_with_embedding
snake_case_ : Dict = bos_token_id
snake_case_ : int = eos_token_id
def UpperCAmelCase__ ( self : Any ) -> int:
'''simple docstring'''
snake_case_ : Tuple = copy.deepcopy(self.__dict__ )
snake_case_ : Optional[int] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
| 666 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "transfo-xl"
_SCREAMING_SNAKE_CASE : Tuple = ["mems"]
_SCREAMING_SNAKE_CASE : str = {
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[Any] , A__ : Union[str, Any]=26_77_35 , A__ : Any=[2_00_00, 4_00_00, 20_00_00] , A__ : Union[str, Any]=10_24 , A__ : List[str]=10_24 , A__ : List[str]=16 , A__ : List[Any]=64 , A__ : Any=40_96 , A__ : List[Any]=4 , A__ : Tuple=False , A__ : List[Any]=18 , A__ : List[Any]=16_00 , A__ : Union[str, Any]=10_00 , A__ : Union[str, Any]=True , A__ : Optional[Any]=True , A__ : List[Any]=0 , A__ : List[str]=-1 , A__ : Tuple=True , A__ : Optional[int]=0.1 , A__ : Tuple=0.0 , A__ : Tuple=True , A__ : Union[str, Any]="normal" , A__ : str=0.01 , A__ : str=0.01 , A__ : Tuple=0.02 , A__ : List[str]=1E-5 , A__ : Dict=0 , **A__ : Any , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[int] = []
self.cutoffs.extend(A__ )
if proj_share_all_but_first:
snake_case_ : List[str] = [False] + [True] * len(self.cutoffs )
else:
snake_case_ : int = [False] + [False] * len(self.cutoffs )
snake_case_ : Union[str, Any] = d_model
snake_case_ : Dict = d_embed
snake_case_ : List[str] = d_head
snake_case_ : Any = d_inner
snake_case_ : List[Any] = div_val
snake_case_ : Dict = pre_lnorm
snake_case_ : str = n_layer
snake_case_ : Union[str, Any] = n_head
snake_case_ : Dict = mem_len
snake_case_ : int = same_length
snake_case_ : Dict = attn_type
snake_case_ : Union[str, Any] = clamp_len
snake_case_ : Dict = sample_softmax
snake_case_ : Optional[int] = adaptive
snake_case_ : Any = dropout
snake_case_ : Dict = dropatt
snake_case_ : Tuple = untie_r
snake_case_ : str = init
snake_case_ : Optional[Any] = init_range
snake_case_ : Optional[Any] = proj_init_std
snake_case_ : List[str] = init_std
snake_case_ : int = layer_norm_epsilon
super().__init__(eos_token_id=A__ , **A__ )
@property
def UpperCAmelCase__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def UpperCAmelCase__ ( self : Optional[Any] , A__ : List[Any] ) -> List[Any]:
'''simple docstring'''
raise NotImplementedError(
f"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ : str = int(max(0 , i - limit ) )
snake_case_ : Optional[int] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCAmelCase_ )
snake_case_ : List[Any] = f"{_stra[0:_stra.index(lowerCAmelCase_ )]} {_stra[_stra.index(lowerCAmelCase_ ) + 1:]}"
return "".join(lowerCAmelCase_ )
# matching characters
snake_case_ : List[Any] = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : int = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = len(lowerCAmelCase_ )
# transposition
snake_case_ : List[str] = (
len([(ca, ca) for ca, ca in zip(lowerCAmelCase_ , lowerCAmelCase_ ) if ca != ca] ) // 2
)
if not match_count:
snake_case_ : str = 0.0
else:
snake_case_ : Optional[Any] = (
1
/ 3
* (
match_count / len(lowerCAmelCase_ )
+ match_count / len(lowerCAmelCase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
snake_case_ : Optional[Any] = 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"))
| 666 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
UpperCAmelCase = {
"vocab_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
),
},
"merges_file": {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
"allenai/longformer-large-4096": (
"https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
),
},
}
UpperCAmelCase = {
"allenai/longformer-base-4096": 4_0_9_6,
"allenai/longformer-large-4096": 4_0_9_6,
"allenai/longformer-large-4096-finetuned-triviaqa": 4_0_9_6,
"allenai/longformer-base-4096-extra.pos.embd.only": 4_0_9_6,
"allenai/longformer-large-4096-extra.pos.embd.only": 4_0_9_6,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def SCREAMING_SNAKE_CASE_ ( ):
snake_case_ : Tuple = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
snake_case_ : List[str] = bs[:]
snake_case_ : Any = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase_ )
cs.append(2**8 + n )
n += 1
snake_case_ : Optional[Any] = [chr(lowerCAmelCase_ ) for n in cs]
return dict(zip(lowerCAmelCase_ , lowerCAmelCase_ ) )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] ):
snake_case_ : Optional[int] = set()
snake_case_ : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case_ : List[Any] = char
return pairs
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE : int = ["input_ids", "attention_mask"]
def __init__( self : Dict , A__ : Dict , A__ : Tuple , A__ : Dict="replace" , A__ : int="<s>" , A__ : Optional[Any]="</s>" , A__ : Optional[int]="</s>" , A__ : Any="<s>" , A__ : Optional[Any]="<unk>" , A__ : Dict="<pad>" , A__ : int="<mask>" , A__ : Tuple=False , **A__ : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else bos_token
snake_case_ : str = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else eos_token
snake_case_ : Optional[int] = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else sep_token
snake_case_ : Optional[Any] = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else cls_token
snake_case_ : List[Any] = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else unk_token
snake_case_ : Tuple = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case_ : Union[str, Any] = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else mask_token
super().__init__(
errors=A__ , bos_token=A__ , eos_token=A__ , unk_token=A__ , sep_token=A__ , cls_token=A__ , pad_token=A__ , mask_token=A__ , add_prefix_space=A__ , **A__ , )
with open(A__ , encoding="utf-8" ) as vocab_handle:
snake_case_ : List[str] = json.load(A__ )
snake_case_ : List[str] = {v: k for k, v in self.encoder.items()}
snake_case_ : Dict = errors # how to handle errors in decoding
snake_case_ : Any = bytes_to_unicode()
snake_case_ : List[str] = {v: k for k, v in self.byte_encoder.items()}
with open(A__ , encoding="utf-8" ) as merges_handle:
snake_case_ : List[str] = merges_handle.read().split("\n" )[1:-1]
snake_case_ : Union[str, Any] = [tuple(merge.split() ) for merge in bpe_merges]
snake_case_ : str = dict(zip(A__ , range(len(A__ ) ) ) )
snake_case_ : str = {}
snake_case_ : str = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
snake_case_ : List[str] = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def UpperCAmelCase__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase__ ( self : Any ) -> Any:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self : Any , A__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
snake_case_ : Any = tuple(A__ )
snake_case_ : Union[str, Any] = get_pairs(A__ )
if not pairs:
return token
while True:
snake_case_ : List[Any] = min(A__ , key=lambda A__ : self.bpe_ranks.get(A__ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case_ ,snake_case_ : int = bigram
snake_case_ : List[Any] = []
snake_case_ : Any = 0
while i < len(A__ ):
try:
snake_case_ : List[str] = word.index(A__ , A__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case_ : Any = j
if word[i] == first and i < len(A__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case_ : Tuple = tuple(A__ )
snake_case_ : Optional[Any] = new_word
if len(A__ ) == 1:
break
else:
snake_case_ : Union[str, Any] = get_pairs(A__ )
snake_case_ : Any = " ".join(A__ )
snake_case_ : str = word
return word
def UpperCAmelCase__ ( self : Dict , A__ : List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = []
for token in re.findall(self.pat , A__ ):
snake_case_ : str = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A__ ).split(" " ) )
return bpe_tokens
def UpperCAmelCase__ ( self : Dict , A__ : Any ) -> Any:
'''simple docstring'''
return self.encoder.get(A__ , self.encoder.get(self.unk_token ) )
def UpperCAmelCase__ ( self : int , A__ : List[Any] ) -> str:
'''simple docstring'''
return self.decoder.get(A__ )
def UpperCAmelCase__ ( self : Dict , A__ : Tuple ) -> str:
'''simple docstring'''
snake_case_ : int = "".join(A__ )
snake_case_ : List[str] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def UpperCAmelCase__ ( self : Optional[Any] , A__ : str , A__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(A__ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ : Any = os.path.join(
A__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Dict = os.path.join(
A__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(A__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=A__ , ensure_ascii=A__ ) + "\n" )
snake_case_ : List[str] = 0
with open(A__ , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda A__ : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
snake_case_ : List[Any] = token_index
writer.write(" ".join(A__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase__ ( self : str , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ : Optional[Any] = [self.cls_token_id]
snake_case_ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase__ ( self : Optional[Any] , A__ : List[int] , A__ : Optional[List[int]] = None , A__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A__ , token_ids_a=A__ , already_has_special_tokens=A__ )
if token_ids_a is None:
return [1] + ([0] * len(A__ )) + [1]
return [1] + ([0] * len(A__ )) + [1, 1] + ([0] * len(A__ )) + [1]
def UpperCAmelCase__ ( self : Optional[int] , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case_ : Union[str, Any] = [self.sep_token_id]
snake_case_ : List[Any] = [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 + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self : List[Any] , A__ : str , A__ : List[Any]=False , **A__ : Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(A__ ) > 0 and not text[0].isspace()):
snake_case_ : Tuple = " " + text
return (text, kwargs)
| 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
set_seed(7_7_0)
UpperCAmelCase = {
"c_attn": "att_proj",
"c_proj": "out_proj",
"c_fc": "in_proj",
"transformer.": "",
"h.": "layers.",
"ln_1": "layernorm_1",
"ln_2": "layernorm_2",
"ln_f": "layernorm_final",
"wpe": "position_embeds_layer",
"wte": "input_embeds_layer",
}
UpperCAmelCase = {
"text_small": {
"repo_id": "suno/bark",
"file_name": "text.pt",
},
"coarse_small": {
"repo_id": "suno/bark",
"file_name": "coarse.pt",
},
"fine_small": {
"repo_id": "suno/bark",
"file_name": "fine.pt",
},
"text": {
"repo_id": "suno/bark",
"file_name": "text_2.pt",
},
"coarse": {
"repo_id": "suno/bark",
"file_name": "coarse_2.pt",
},
"fine": {
"repo_id": "suno/bark",
"file_name": "fine_2.pt",
},
}
UpperCAmelCase = os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase = os.path.join(os.path.expanduser("~"), ".cache")
UpperCAmelCase = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: List[str]=False ):
snake_case_ : Union[str, Any] = model_type
if use_small:
key += "_small"
return os.path.join(lowerCAmelCase_ , REMOTE_MODEL_PATHS[key]["file_name"] )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: List[str] ):
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
hf_hub_download(repo_id=lowerCAmelCase_ , filename=lowerCAmelCase_ , local_dir=lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: Dict , lowerCAmelCase_: List[str]=False , lowerCAmelCase_: Dict="text" ):
if model_type == "text":
snake_case_ : int = BarkSemanticModel
snake_case_ : str = BarkSemanticConfig
snake_case_ : Optional[Any] = BarkSemanticGenerationConfig
elif model_type == "coarse":
snake_case_ : str = BarkCoarseModel
snake_case_ : Optional[int] = BarkCoarseConfig
snake_case_ : Any = BarkCoarseGenerationConfig
elif model_type == "fine":
snake_case_ : Optional[int] = BarkFineModel
snake_case_ : Tuple = BarkFineConfig
snake_case_ : List[str] = BarkFineGenerationConfig
else:
raise NotImplementedError()
snake_case_ : Optional[Any] = f"{model_type}_small" if use_small else model_type
snake_case_ : Any = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(lowerCAmelCase_ ):
logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`." )
_download(model_info["repo_id"] , model_info["file_name"] )
snake_case_ : Any = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
# this is a hack
snake_case_ : Union[str, Any] = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
snake_case_ : str = model_args["vocab_size"]
snake_case_ : Union[str, Any] = model_args["vocab_size"]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
snake_case_ : Union[str, Any] = model_args.pop("n_head" )
snake_case_ : int = model_args.pop("n_embd" )
snake_case_ : Any = model_args.pop("n_layer" )
snake_case_ : List[str] = ConfigClass(**checkpoint["model_args"] )
snake_case_ : Optional[Any] = ModelClass(config=lowerCAmelCase_ )
snake_case_ : Tuple = GenerationConfigClass()
snake_case_ : List[str] = model_generation_config
snake_case_ : Optional[int] = checkpoint["model"]
# fixup checkpoint
snake_case_ : Optional[int] = "_orig_mod."
for k, v in list(state_dict.items() ):
if k.startswith(lowerCAmelCase_ ):
# replace part of the key with corresponding layer name in HF implementation
snake_case_ : Tuple = k[len(lowerCAmelCase_ ) :]
for old_layer_name in new_layer_name_dict:
snake_case_ : int = new_k.replace(lowerCAmelCase_ , new_layer_name_dict[old_layer_name] )
snake_case_ : int = state_dict.pop(lowerCAmelCase_ )
snake_case_ : Optional[int] = set(state_dict.keys() ) - set(model.state_dict().keys() )
snake_case_ : str = {k for k in extra_keys if not k.endswith(".attn.bias" )}
snake_case_ : Any = set(model.state_dict().keys() ) - set(state_dict.keys() )
snake_case_ : List[Any] = {k for k in missing_keys if not k.endswith(".attn.bias" )}
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"extra keys found: {extra_keys}" )
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"missing keys: {missing_keys}" )
model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
snake_case_ : str = model.num_parameters(exclude_embeddings=lowerCAmelCase_ )
snake_case_ : Union[str, Any] = checkpoint["best_val_loss"].item()
logger.info(f"model loaded: {round(n_params/1e6 , 1 )}M params, {round(lowerCAmelCase_ , 3 )} loss" )
model.eval()
model.to(lowerCAmelCase_ )
del checkpoint, state_dict
return model
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] , lowerCAmelCase_: str=False , lowerCAmelCase_: int="text" ):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
snake_case_ : int = "cpu" # do conversion on cpu
snake_case_ : Optional[Any] = _get_ckpt_path(lowerCAmelCase_ , use_small=lowerCAmelCase_ )
snake_case_ : Tuple = _load_model(lowerCAmelCase_ , lowerCAmelCase_ , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
# load bark initial model
snake_case_ : int = _bark_load_model(lowerCAmelCase_ , "cpu" , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
if model_type == "text":
snake_case_ : Union[str, Any] = bark_model["model"]
if model.num_parameters(exclude_embeddings=lowerCAmelCase_ ) != bark_model.get_num_params():
raise ValueError("initial and new models don't have the same number of parameters" )
# check if same output as the bark model
snake_case_ : Optional[Any] = 5
snake_case_ : Optional[int] = 1_0
if model_type in ["text", "coarse"]:
snake_case_ : Optional[Any] = torch.randint(2_5_6 , (batch_size, sequence_length) , dtype=torch.int )
snake_case_ : str = bark_model(lowerCAmelCase_ )[0]
snake_case_ : Tuple = model(lowerCAmelCase_ )
# take last logits
snake_case_ : List[str] = output_new_model_total.logits[:, [-1], :]
else:
snake_case_ : Optional[int] = 3
snake_case_ : str = 8
snake_case_ : List[str] = torch.randint(2_5_6 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
snake_case_ : Any = model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Union[str, Any] = bark_model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("initial and new outputs don't have the same shape" )
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("initial and new outputs are not equal" )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple , lowerCAmelCase_: List[str] , lowerCAmelCase_: Any , lowerCAmelCase_: List[Any] , lowerCAmelCase_: int , lowerCAmelCase_: Optional[Any] , ):
snake_case_ : Optional[Any] = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[str] = BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = EncodecConfig.from_pretrained("facebook/encodec_24khz" )
snake_case_ : List[str] = BarkSemanticModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkCoarseModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Tuple = BarkFineModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Union[str, Any] = EncodecModel.from_pretrained("facebook/encodec_24khz" )
snake_case_ : Tuple = BarkConfig.from_sub_model_configs(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : List[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
snake_case_ : Optional[int] = BarkModel(lowerCAmelCase_ )
snake_case_ : int = semantic
snake_case_ : List[str] = coarseAcoustic
snake_case_ : str = fineAcoustic
snake_case_ : Optional[Any] = codec
snake_case_ : Any = bark_generation_config
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
bark.save_pretrained(lowerCAmelCase_ , repo_id=lowerCAmelCase_ , push_to_hub=lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("model_type", type=str, help="text, coarse or fine.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
UpperCAmelCase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 666 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: Any=1_0 ):
snake_case_ : Optional[int] = []
for _ in range(lowerCAmelCase_ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: Union[str, Any]=1_0 ):
snake_case_ : List[str] = []
for step in range(lowerCAmelCase_ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(lowerCAmelCase_ , "schedule.bin" )
torch.save(scheduler.state_dict() , lowerCAmelCase_ )
snake_case_ : Optional[int] = torch.load(lowerCAmelCase_ )
scheduler.load_state_dict(lowerCAmelCase_ )
return lrs
@require_torch
class snake_case__ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Optional[Any] , A__ : Tuple , A__ : Any , A__ : List[str] ) -> str:
'''simple docstring'''
self.assertEqual(len(A__ ) , len(A__ ) )
for a, b in zip(A__ , A__ ):
self.assertAlmostEqual(A__ , A__ , delta=A__ )
def UpperCAmelCase__ ( self : Tuple ) -> int:
'''simple docstring'''
snake_case_ : Any = torch.tensor([0.1, -0.2, -0.1] , requires_grad=A__ )
snake_case_ : Tuple = torch.tensor([0.4, 0.2, -0.5] )
snake_case_ : List[Any] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
snake_case_ : int = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(1_00 ):
snake_case_ : Union[str, Any] = criterion(A__ , A__ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def UpperCAmelCase__ ( self : Dict ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = torch.tensor([0.1, -0.2, -0.1] , requires_grad=A__ )
snake_case_ : Dict = torch.tensor([0.4, 0.2, -0.5] )
snake_case_ : Union[str, Any] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
snake_case_ : int = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=A__ , weight_decay=0.0 , relative_step=A__ , scale_parameter=A__ , warmup_init=A__ , )
for _ in range(10_00 ):
snake_case_ : Dict = criterion(A__ , A__ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class snake_case__ ( unittest.TestCase ):
_SCREAMING_SNAKE_CASE : str = nn.Linear(5_0 , 5_0 ) if is_torch_available() else None
_SCREAMING_SNAKE_CASE : Tuple = AdamW(m.parameters() , lr=1_0.0 ) if is_torch_available() else None
_SCREAMING_SNAKE_CASE : str = 1_0
def UpperCAmelCase__ ( self : str , A__ : Tuple , A__ : str , A__ : int , A__ : Dict=None ) -> Dict:
'''simple docstring'''
self.assertEqual(len(A__ ) , len(A__ ) )
for a, b in zip(A__ , A__ ):
self.assertAlmostEqual(A__ , A__ , delta=A__ , msg=A__ )
def UpperCAmelCase__ ( self : str ) -> List[str]:
'''simple docstring'''
snake_case_ : int = {"num_warmup_steps": 2, "num_training_steps": 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
snake_case_ : Optional[Any] = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"num_warmup_steps": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, "num_cycles": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, "power": 2.0, "lr_end": 1E-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{"num_warmup_steps": 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
snake_case_ ,snake_case_ : List[str] = data
snake_case_ : Dict = scheduler_func(self.optimizer , **A__ )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
snake_case_ : Union[str, Any] = unwrap_schedule(A__ , self.num_steps )
self.assertListAlmostEqual(
A__ , A__ , tol=1E-2 , msg=f"failed for {scheduler_func} in normal scheduler" , )
snake_case_ : List[str] = scheduler_func(self.optimizer , **A__ )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(A__ ) # wrap to test picklability of the schedule
snake_case_ : Tuple = unwrap_and_save_reload_schedule(A__ , self.num_steps )
self.assertListEqual(A__ , A__ , msg=f"failed for {scheduler_func} in save and reload" )
class snake_case__ :
def __init__( self : int , A__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = fn
def __call__( self : Dict , *A__ : List[str] , **A__ : str ) -> Optional[int]:
'''simple docstring'''
return self.fn(*A__ , **A__ )
@classmethod
def UpperCAmelCase__ ( self : Optional[Any] , A__ : Dict ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Dict = list(map(self , scheduler.lr_lambdas ) )
| 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["ReformerTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["ReformerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"ReformerAttention",
"ReformerForMaskedLM",
"ReformerForQuestionAnswering",
"ReformerForSequenceClassification",
"ReformerLayer",
"ReformerModel",
"ReformerModelWithLMHead",
"ReformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
UpperCAmelCase = logging.get_logger(__name__)
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : str = ["pixel_values"]
def __init__( self : List[Any] , A__ : bool = True , A__ : Optional[Dict[str, int]] = None , A__ : PILImageResampling = PILImageResampling.BILINEAR , A__ : bool = True , A__ : Dict[str, int] = None , A__ : bool = True , A__ : Union[int, float] = 1 / 2_55 , A__ : bool = True , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , **A__ : int , ) -> None:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : Optional[int] = size if size is not None else {"shortest_edge": 2_56}
snake_case_ : Dict = get_size_dict(A__ , default_to_square=A__ )
snake_case_ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
snake_case_ : Any = get_size_dict(A__ , param_name="crop_size" )
snake_case_ : int = do_resize
snake_case_ : Optional[Any] = size
snake_case_ : Optional[Any] = resample
snake_case_ : Optional[int] = do_center_crop
snake_case_ : List[Any] = crop_size
snake_case_ : List[Any] = do_rescale
snake_case_ : Optional[int] = rescale_factor
snake_case_ : Optional[Any] = do_normalize
snake_case_ : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case_ : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase__ ( self : List[str] , A__ : np.ndarray , A__ : Dict[str, int] , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : str , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Optional[Any] = get_size_dict(A__ , default_to_square=A__ )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
snake_case_ : Any = get_resize_output_image_size(A__ , size=size["shortest_edge"] , default_to_square=A__ )
return resize(A__ , size=A__ , resample=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : int , A__ : np.ndarray , A__ : Dict[str, int] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Union[str, Any] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Tuple = get_size_dict(A__ )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(A__ , size=(size["height"], size["width"]) , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : List[str] , A__ : np.ndarray , A__ : float , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Tuple ) -> np.ndarray:
'''simple docstring'''
return rescale(A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : np.ndarray , A__ : Union[float, List[float]] , A__ : Union[float, List[float]] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Dict , ) -> np.ndarray:
'''simple docstring'''
return normalize(A__ , mean=A__ , std=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : ImageInput , A__ : Optional[bool] = None , A__ : Dict[str, int] = None , A__ : PILImageResampling = None , A__ : bool = None , A__ : Dict[str, int] = None , A__ : Optional[bool] = None , A__ : Optional[float] = None , A__ : Optional[bool] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[str, TensorType]] = None , A__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **A__ : Union[str, Any] , ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
snake_case_ : Dict = size if size is not None else self.size
snake_case_ : Optional[Any] = get_size_dict(A__ , default_to_square=A__ )
snake_case_ : Tuple = resample if resample is not None else self.resample
snake_case_ : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ : str = crop_size if crop_size is not None else self.crop_size
snake_case_ : Tuple = get_size_dict(A__ , param_name="crop_size" )
snake_case_ : Dict = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : Any = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ : Any = image_mean if image_mean is not None else self.image_mean
snake_case_ : List[str] = image_std if image_std is not None else self.image_std
snake_case_ : Dict = make_list_of_images(A__ )
if not valid_images(A__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
snake_case_ : Tuple = [to_numpy_array(A__ ) for image in images]
if do_resize:
snake_case_ : Any = [self.resize(image=A__ , size=A__ , resample=A__ ) for image in images]
if do_center_crop:
snake_case_ : List[str] = [self.center_crop(image=A__ , size=A__ ) for image in images]
if do_rescale:
snake_case_ : Any = [self.rescale(image=A__ , scale=A__ ) for image in images]
if do_normalize:
snake_case_ : Union[str, Any] = [self.normalize(image=A__ , mean=A__ , std=A__ ) for image in images]
snake_case_ : Optional[Any] = [to_channel_dimension_format(A__ , A__ ) for image in images]
snake_case_ : Any = {"pixel_values": images}
return BatchFeature(data=A__ , tensor_type=A__ )
def UpperCAmelCase__ ( self : List[str] , A__ : Dict , A__ : List[Tuple] = None ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(A__ ) != len(A__ ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(A__ ):
snake_case_ : Dict = target_sizes.numpy()
snake_case_ : int = []
for idx in range(len(A__ ) ):
snake_case_ : List[str] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=A__ )
snake_case_ : int = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A__ )
else:
snake_case_ : List[Any] = logits.argmax(dim=1 )
snake_case_ : List[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 666 | 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 SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: List[str] , lowerCAmelCase_: List[str] , lowerCAmelCase_: str , lowerCAmelCase_: int , lowerCAmelCase_: str , lowerCAmelCase_: Optional[int] , lowerCAmelCase_: List[Any] , lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: int , lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: Tuple , ):
snake_case_ : Tuple = {
"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_ : int = input_paths_and_base_extractors[compression_format]
if input_path is None:
snake_case_ : Tuple = 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(lowerCAmelCase_ )
assert base_extractor.is_extractable(lowerCAmelCase_ )
snake_case_ : int = tmp_path / ("extracted" if is_archive else "extracted.txt")
base_extractor.extract(lowerCAmelCase_ , lowerCAmelCase_ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case_ : List[Any] = file_path.read_text(encoding="utf-8" )
else:
snake_case_ : Union[str, Any] = output_path.read_text(encoding="utf-8" )
snake_case_ : Dict = 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 SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[int] , lowerCAmelCase_: int , lowerCAmelCase_: Dict , lowerCAmelCase_: int , lowerCAmelCase_: str , lowerCAmelCase_: List[str] , lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: List[str] , lowerCAmelCase_: Union[str, Any] , lowerCAmelCase_: Any , lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: str , ):
snake_case_ : int = {
"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_ : List[Any] = input_paths[compression_format]
if input_path is None:
snake_case_ : Optional[Any] = 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(lowerCAmelCase_ )
snake_case_ : str = Extractor.infer_extractor_format(lowerCAmelCase_ )
assert extractor_format is not None
snake_case_ : int = tmp_path / ("extracted" if is_archive else "extracted.txt")
Extractor.extract(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case_ : List[Any] = file_path.read_text(encoding="utf-8" )
else:
snake_case_ : int = output_path.read_text(encoding="utf-8" )
snake_case_ : str = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: Any ):
import tarfile
snake_case_ : Any = tmp_path / "data_dot_dot"
directory.mkdir()
snake_case_ : Any = directory / "tar_file_with_dot_dot.tar"
with tarfile.TarFile(lowerCAmelCase_ , "w" ) as f:
f.add(lowerCAmelCase_ , arcname=os.path.join(".." , text_file.name ) )
return path
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Union[str, Any] ):
import tarfile
snake_case_ : Tuple = tmp_path / "data_sym_link"
directory.mkdir()
snake_case_ : int = directory / "tar_file_with_sym_link.tar"
os.symlink(".." , directory / "subdir" , target_is_directory=lowerCAmelCase_ )
with tarfile.TarFile(lowerCAmelCase_ , "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 SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[int] , lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: Optional[int] , lowerCAmelCase_: str , lowerCAmelCase_: List[Any] ):
snake_case_ : Dict = {
"tar_file_with_dot_dot": tar_file_with_dot_dot,
"tar_file_with_sym_link": tar_file_with_sym_link,
}
snake_case_ : List[Any] = insecure_tar_files[insecure_tar_file]
snake_case_ : Optional[Any] = tmp_path / "extracted"
TarExtractor.extract(lowerCAmelCase_ , lowerCAmelCase_ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple ):
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
snake_case_ : Tuple = tmpdir / "not_a_zip_file"
# From: https://github.com/python/cpython/pull/5053
snake_case_ : List[str] = (
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(lowerCAmelCase_ )
assert zipfile.is_zipfile(str(lowerCAmelCase_ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(lowerCAmelCase_ ) # but we're right
| 666 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotConfig",
"BlenderbotOnnxConfig",
],
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ["BlenderbotTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | 1 |
class snake_case__ :
def __init__( self : Dict , A__ : str , A__ : Tuple , A__ : Union[str, Any] ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = None
snake_case_ : Tuple = None
snake_case_ : Optional[int] = graph
self._normalize_graph(A__ , A__ )
snake_case_ : Union[str, Any] = len(A__ )
snake_case_ : Dict = None
def UpperCAmelCase__ ( self : Any , A__ : List[str] , A__ : List[Any] ) -> int:
'''simple docstring'''
if sources is int:
snake_case_ : List[str] = [sources]
if sinks is int:
snake_case_ : Tuple = [sinks]
if len(A__ ) == 0 or len(A__ ) == 0:
return
snake_case_ : List[Any] = sources[0]
snake_case_ : List[str] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(A__ ) > 1 or len(A__ ) > 1:
snake_case_ : Optional[int] = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
snake_case_ : List[Any] = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
snake_case_ : Union[str, Any] = max_input_flow
snake_case_ : Optional[int] = 0
snake_case_ : int = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
snake_case_ : Any = max_input_flow
snake_case_ : int = size - 1
def UpperCAmelCase__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def UpperCAmelCase__ ( self : Optional[Any] , A__ : Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : int = algorithm(self )
class snake_case__ :
def __init__( self : List[Any] , A__ : Dict ) -> List[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = flow_network
snake_case_ : str = flow_network.verticesCount
snake_case_ : Dict = flow_network.sourceIndex
snake_case_ : List[Any] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
snake_case_ : Optional[int] = flow_network.graph
snake_case_ : Tuple = False
def UpperCAmelCase__ ( self : List[Any] ) -> int:
'''simple docstring'''
if not self.executed:
self._algorithm()
snake_case_ : Optional[int] = True
def UpperCAmelCase__ ( self : List[Any] ) -> int:
'''simple docstring'''
pass
class snake_case__ ( _UpperCamelCase ):
def __init__( self : List[Any] , A__ : Optional[int] ) -> int:
'''simple docstring'''
super().__init__(A__ )
# use this to save your result
snake_case_ : Tuple = -1
def UpperCAmelCase__ ( self : int ) -> Dict:
'''simple docstring'''
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class snake_case__ ( _UpperCamelCase ):
def __init__( self : List[str] , A__ : Optional[int] ) -> int:
'''simple docstring'''
super().__init__(A__ )
snake_case_ : Dict = [[0] * self.verticies_count for i in range(self.verticies_count )]
snake_case_ : Dict = [0] * self.verticies_count
snake_case_ : str = [0] * self.verticies_count
def UpperCAmelCase__ ( self : Optional[int] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
snake_case_ : str = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
snake_case_ : Optional[Any] = 0
while i < len(A__ ):
snake_case_ : List[Any] = vertices_list[i]
snake_case_ : Any = self.heights[vertex_index]
self.process_vertex(A__ )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(A__ ) )
snake_case_ : int = 0
else:
i += 1
snake_case_ : Dict = sum(self.preflow[self.source_index] )
def UpperCAmelCase__ ( self : str , A__ : Union[str, Any] ) -> str:
'''simple docstring'''
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(A__ , A__ )
self.relabel(A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : Any , A__ : Any ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def UpperCAmelCase__ ( self : List[Any] , A__ : int ) -> Any:
'''simple docstring'''
snake_case_ : Tuple = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
snake_case_ : int = self.heights[to_index]
if min_height is not None:
snake_case_ : Union[str, Any] = min_height + 1
if __name__ == "__main__":
UpperCAmelCase = [0]
UpperCAmelCase = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
UpperCAmelCase = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
UpperCAmelCase = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
UpperCAmelCase = flow_network.find_maximum_flow()
print(F"maximum flow is {maximum_flow}")
| 666 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "tapas"
def __init__( self : List[Any] , A__ : str=3_05_22 , A__ : Tuple=7_68 , A__ : List[Any]=12 , A__ : Optional[Any]=12 , A__ : Union[str, Any]=30_72 , A__ : Dict="gelu" , A__ : List[Any]=0.1 , A__ : str=0.1 , A__ : List[Any]=10_24 , A__ : Optional[int]=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , A__ : Union[str, Any]=0.02 , A__ : Tuple=1E-12 , A__ : Tuple=0 , A__ : Any=10.0 , A__ : List[str]=0 , A__ : List[str]=1.0 , A__ : Optional[Any]=None , A__ : Tuple=1.0 , A__ : Union[str, Any]=False , A__ : Any=None , A__ : Union[str, Any]=1.0 , A__ : int=1.0 , A__ : str=False , A__ : int=False , A__ : Optional[Any]="ratio" , A__ : str=None , A__ : int=None , A__ : Dict=64 , A__ : int=32 , A__ : Optional[Any]=False , A__ : List[str]=True , A__ : List[Any]=False , A__ : str=False , A__ : Any=True , A__ : Tuple=False , A__ : str=None , A__ : str=None , **A__ : List[str] , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=A__ , **A__ )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
snake_case_ : int = vocab_size
snake_case_ : int = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = hidden_act
snake_case_ : Optional[int] = intermediate_size
snake_case_ : str = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : Any = max_position_embeddings
snake_case_ : List[Any] = type_vocab_sizes
snake_case_ : str = initializer_range
snake_case_ : Optional[Any] = layer_norm_eps
# Fine-tuning task hyperparameters
snake_case_ : Optional[int] = positive_label_weight
snake_case_ : Dict = num_aggregation_labels
snake_case_ : List[str] = aggregation_loss_weight
snake_case_ : str = use_answer_as_supervision
snake_case_ : int = answer_loss_importance
snake_case_ : Any = use_normalized_answer_loss
snake_case_ : int = huber_loss_delta
snake_case_ : List[Any] = temperature
snake_case_ : str = aggregation_temperature
snake_case_ : List[str] = use_gumbel_for_cells
snake_case_ : List[str] = use_gumbel_for_aggregation
snake_case_ : Dict = average_approximation_function
snake_case_ : List[str] = cell_selection_preference
snake_case_ : Dict = answer_loss_cutoff
snake_case_ : List[str] = max_num_rows
snake_case_ : Union[str, Any] = max_num_columns
snake_case_ : str = average_logits_per_cell
snake_case_ : Union[str, Any] = select_one_column
snake_case_ : Dict = allow_empty_column_selection
snake_case_ : List[Any] = init_cell_selection_weights_to_zero
snake_case_ : str = reset_position_index_per_cell
snake_case_ : List[Any] = disable_per_token_loss
# Aggregation hyperparameters
snake_case_ : List[str] = aggregation_labels
snake_case_ : Union[str, Any] = no_aggregation_label_index
if isinstance(self.aggregation_labels , A__ ):
snake_case_ : Optional[int] = {int(A__ ): v for k, v in aggregation_labels.items()}
| 666 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
UpperCAmelCase = logging.get_logger(__name__)
class snake_case__ :
_SCREAMING_SNAKE_CASE : str
_SCREAMING_SNAKE_CASE : str = None
@staticmethod
def UpperCAmelCase__ ( ) -> Optional[int]:
'''simple docstring'''
raise NotImplementedError
def UpperCAmelCase__ ( self : List[str] , A__ : Union[str, Any] , A__ : int , A__ : str , **A__ : Any ) -> Tuple:
'''simple docstring'''
raise NotImplementedError
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : Optional[int] ) -> Any:
'''simple docstring'''
raise NotImplementedError
def UpperCAmelCase__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}." )
@classmethod
def UpperCAmelCase__ ( cls : Dict ) -> Any:
'''simple docstring'''
return f"`pip install {cls.pip_package or cls.name}`"
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : int = "optuna"
@staticmethod
def UpperCAmelCase__ ( ) -> List[str]:
'''simple docstring'''
return is_optuna_available()
def UpperCAmelCase__ ( self : Tuple , A__ : List[Any] , A__ : int , A__ : str , **A__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return run_hp_search_optuna(A__ , A__ , A__ , **A__ )
def UpperCAmelCase__ ( self : Optional[int] , A__ : Optional[Any] ) -> Any:
'''simple docstring'''
return default_hp_space_optuna(A__ )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = "ray"
_SCREAMING_SNAKE_CASE : Dict = "'ray[tune]'"
@staticmethod
def UpperCAmelCase__ ( ) -> Any:
'''simple docstring'''
return is_ray_available()
def UpperCAmelCase__ ( self : List[Any] , A__ : List[str] , A__ : int , A__ : str , **A__ : List[str] ) -> int:
'''simple docstring'''
return run_hp_search_ray(A__ , A__ , A__ , **A__ )
def UpperCAmelCase__ ( self : List[Any] , A__ : List[Any] ) -> str:
'''simple docstring'''
return default_hp_space_ray(A__ )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = "sigopt"
@staticmethod
def UpperCAmelCase__ ( ) -> Tuple:
'''simple docstring'''
return is_sigopt_available()
def UpperCAmelCase__ ( self : Any , A__ : Any , A__ : int , A__ : str , **A__ : List[Any] ) -> str:
'''simple docstring'''
return run_hp_search_sigopt(A__ , A__ , A__ , **A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : Optional[int] ) -> Any:
'''simple docstring'''
return default_hp_space_sigopt(A__ )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : int = "wandb"
@staticmethod
def UpperCAmelCase__ ( ) -> int:
'''simple docstring'''
return is_wandb_available()
def UpperCAmelCase__ ( self : Optional[Any] , A__ : Any , A__ : int , A__ : str , **A__ : str ) -> str:
'''simple docstring'''
return run_hp_search_wandb(A__ , A__ , A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : Optional[int] ) -> str:
'''simple docstring'''
return default_hp_space_wandb(A__ )
UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def SCREAMING_SNAKE_CASE_ ( ):
snake_case_ : Any = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCAmelCase_ ) > 0:
snake_case_ : Optional[Any] = available_backends[0].name
if len(lowerCAmelCase_ ) > 1:
logger.info(
f"{len(lowerCAmelCase_ )} hyperparameter search backends available. Using {name} as the default." )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f" - To install {backend.name} run {backend.pip_install()}"
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 666 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string" )} ) , supervised_keys=A__ , )
def UpperCAmelCase__ ( self : Optional[Any] , A__ : str , A__ : str ) -> Optional[int]:
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_dummy_examples()} )]
def UpperCAmelCase__ ( self : int , A__ : Optional[int] , A__ : Dict ) -> Optional[Any]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A__ )
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) , supervised_keys=A__ , )
def UpperCAmelCase__ ( self : Any , A__ : List[str] , A__ : str ) -> Optional[int]:
'''simple docstring'''
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_nested_examples()} )
]
def UpperCAmelCase__ ( self : List[Any] , A__ : List[str] , A__ : Optional[int] ) -> List[str]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A__ )
def SCREAMING_SNAKE_CASE_ ( ):
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"] )]
def SCREAMING_SNAKE_CASE_ ( ):
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"] )]
class snake_case__ ( _UpperCamelCase ):
@require_beam
def UpperCAmelCase__ ( self : str ) -> List[str]:
'''simple docstring'''
snake_case_ : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : Dict = DummyBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
snake_case_ : Optional[int] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
self.assertDictEqual(dset["train"][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
import apache_beam as beam
snake_case_ : Tuple = beam.io.parquetio.WriteToParquet
snake_case_ : Union[str, Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : List[Any] = DummyBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
snake_case_ : int = partial(A__ , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
snake_case_ : Optional[Any] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"] ) , sorted(["foo", "bar", "foobar"] ) )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def UpperCAmelCase__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : Tuple = DummyBeamDataset(cache_dir=A__ )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
snake_case_ : List[str] = NestedBeamDataset(cache_dir=A__ , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A__ , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
snake_case_ : int = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , A__ )
self.assertEqual(dset["train"].info.splits["train"].num_examples , A__ )
self.assertDictEqual(dset["train"][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A__ , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
| 666 | 1 |
UpperCAmelCase = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
UpperCAmelCase = {
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: float , lowerCAmelCase_: str , lowerCAmelCase_: str ):
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
snake_case_ : Union[str, Any] = (
f"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
f"Valid values are: {', '.join(lowerCAmelCase_ )}"
)
raise ValueError(lowerCAmelCase_ )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple ):
class snake_case__ :
def __init__( self : Any , A__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = metric_id
class snake_case__ :
_SCREAMING_SNAKE_CASE : List[str] = [MetricMock(_UpperCamelCase ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def UpperCAmelCase__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock() )
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple , lowerCAmelCase_: int , lowerCAmelCase_: List[Any] , lowerCAmelCase_: Any , lowerCAmelCase_: List[str] ):
if "tmp_path" in args:
snake_case_ : List[Any] = tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
with pytest.warns(lowerCAmelCase_ , match="https://huggingface.co/docs/evaluate" ):
func(*lowerCAmelCase_ )
| 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] ): # This function is recursive
snake_case_ : int = len(lowerCAmelCase_ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
snake_case_ : Any = array[0]
snake_case_ : List[str] = False
snake_case_ : Union[str, Any] = 1
snake_case_ : list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
snake_case_ : Tuple = True
snake_case_ : Dict = [element for element in array[i:] if element >= array[i]]
snake_case_ : Any = longest_subsequence(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > len(lowerCAmelCase_ ):
snake_case_ : Optional[int] = temp_array
else:
i += 1
snake_case_ : Any = [element for element in array[1:] if element >= pivot]
snake_case_ : Dict = [pivot, *longest_subsequence(lowerCAmelCase_ )]
if len(lowerCAmelCase_ ) > len(lowerCAmelCase_ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCAmelCase_ )
while lo < hi:
snake_case_ : List[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
snake_case_ : Tuple = mid + 1
else:
snake_case_ : Dict = mid
return lo
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Optional[Any] = len(lowerCAmelCase_ )
while lo < hi:
snake_case_ : Union[str, Any] = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
snake_case_ : Optional[Any] = mid + 1
else:
snake_case_ : Tuple = mid
return lo
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
sorted_collection.insert(bisect_left(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
sorted_collection.insert(bisect_right(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int ):
snake_case_ : Dict = 0
snake_case_ : Tuple = len(lowerCAmelCase_ ) - 1
while left <= right:
snake_case_ : int = left + (right - left) // 2
snake_case_ : Optional[Any] = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
snake_case_ : Optional[Any] = midpoint - 1
else:
snake_case_ : Optional[int] = midpoint + 1
return None
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int ):
snake_case_ : Optional[int] = bisect.bisect_left(lowerCAmelCase_ , lowerCAmelCase_ )
if index != len(lowerCAmelCase_ ) and sorted_collection[index] == item:
return index
return None
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if right < left:
return None
snake_case_ : List[Any] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , midpoint - 1 )
else:
return binary_search_by_recursion(lowerCAmelCase_ , lowerCAmelCase_ , midpoint + 1 , lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = input("Enter numbers separated by comma:\n").strip()
UpperCAmelCase = sorted(int(item) for item in user_input.split(","))
UpperCAmelCase = int(input("Enter a single number to be found in the list:\n"))
UpperCAmelCase = binary_search(collection, target)
if result is None:
print(F"{target} was not found in {collection}.")
else:
print(F"{target} was found at position {result} in {collection}.")
| 666 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
set_seed(7_7_0)
UpperCAmelCase = {
"c_attn": "att_proj",
"c_proj": "out_proj",
"c_fc": "in_proj",
"transformer.": "",
"h.": "layers.",
"ln_1": "layernorm_1",
"ln_2": "layernorm_2",
"ln_f": "layernorm_final",
"wpe": "position_embeds_layer",
"wte": "input_embeds_layer",
}
UpperCAmelCase = {
"text_small": {
"repo_id": "suno/bark",
"file_name": "text.pt",
},
"coarse_small": {
"repo_id": "suno/bark",
"file_name": "coarse.pt",
},
"fine_small": {
"repo_id": "suno/bark",
"file_name": "fine.pt",
},
"text": {
"repo_id": "suno/bark",
"file_name": "text_2.pt",
},
"coarse": {
"repo_id": "suno/bark",
"file_name": "coarse_2.pt",
},
"fine": {
"repo_id": "suno/bark",
"file_name": "fine_2.pt",
},
}
UpperCAmelCase = os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase = os.path.join(os.path.expanduser("~"), ".cache")
UpperCAmelCase = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: List[str]=False ):
snake_case_ : Union[str, Any] = model_type
if use_small:
key += "_small"
return os.path.join(lowerCAmelCase_ , REMOTE_MODEL_PATHS[key]["file_name"] )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: List[str] ):
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
hf_hub_download(repo_id=lowerCAmelCase_ , filename=lowerCAmelCase_ , local_dir=lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: Dict , lowerCAmelCase_: List[str]=False , lowerCAmelCase_: Dict="text" ):
if model_type == "text":
snake_case_ : int = BarkSemanticModel
snake_case_ : str = BarkSemanticConfig
snake_case_ : Optional[Any] = BarkSemanticGenerationConfig
elif model_type == "coarse":
snake_case_ : str = BarkCoarseModel
snake_case_ : Optional[int] = BarkCoarseConfig
snake_case_ : Any = BarkCoarseGenerationConfig
elif model_type == "fine":
snake_case_ : Optional[int] = BarkFineModel
snake_case_ : Tuple = BarkFineConfig
snake_case_ : List[str] = BarkFineGenerationConfig
else:
raise NotImplementedError()
snake_case_ : Optional[Any] = f"{model_type}_small" if use_small else model_type
snake_case_ : Any = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(lowerCAmelCase_ ):
logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`." )
_download(model_info["repo_id"] , model_info["file_name"] )
snake_case_ : Any = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
# this is a hack
snake_case_ : Union[str, Any] = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
snake_case_ : str = model_args["vocab_size"]
snake_case_ : Union[str, Any] = model_args["vocab_size"]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
snake_case_ : Union[str, Any] = model_args.pop("n_head" )
snake_case_ : int = model_args.pop("n_embd" )
snake_case_ : Any = model_args.pop("n_layer" )
snake_case_ : List[str] = ConfigClass(**checkpoint["model_args"] )
snake_case_ : Optional[Any] = ModelClass(config=lowerCAmelCase_ )
snake_case_ : Tuple = GenerationConfigClass()
snake_case_ : List[str] = model_generation_config
snake_case_ : Optional[int] = checkpoint["model"]
# fixup checkpoint
snake_case_ : Optional[int] = "_orig_mod."
for k, v in list(state_dict.items() ):
if k.startswith(lowerCAmelCase_ ):
# replace part of the key with corresponding layer name in HF implementation
snake_case_ : Tuple = k[len(lowerCAmelCase_ ) :]
for old_layer_name in new_layer_name_dict:
snake_case_ : int = new_k.replace(lowerCAmelCase_ , new_layer_name_dict[old_layer_name] )
snake_case_ : int = state_dict.pop(lowerCAmelCase_ )
snake_case_ : Optional[int] = set(state_dict.keys() ) - set(model.state_dict().keys() )
snake_case_ : str = {k for k in extra_keys if not k.endswith(".attn.bias" )}
snake_case_ : Any = set(model.state_dict().keys() ) - set(state_dict.keys() )
snake_case_ : List[Any] = {k for k in missing_keys if not k.endswith(".attn.bias" )}
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"extra keys found: {extra_keys}" )
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"missing keys: {missing_keys}" )
model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
snake_case_ : str = model.num_parameters(exclude_embeddings=lowerCAmelCase_ )
snake_case_ : Union[str, Any] = checkpoint["best_val_loss"].item()
logger.info(f"model loaded: {round(n_params/1e6 , 1 )}M params, {round(lowerCAmelCase_ , 3 )} loss" )
model.eval()
model.to(lowerCAmelCase_ )
del checkpoint, state_dict
return model
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] , lowerCAmelCase_: str=False , lowerCAmelCase_: int="text" ):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
snake_case_ : int = "cpu" # do conversion on cpu
snake_case_ : Optional[Any] = _get_ckpt_path(lowerCAmelCase_ , use_small=lowerCAmelCase_ )
snake_case_ : Tuple = _load_model(lowerCAmelCase_ , lowerCAmelCase_ , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
# load bark initial model
snake_case_ : int = _bark_load_model(lowerCAmelCase_ , "cpu" , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
if model_type == "text":
snake_case_ : Union[str, Any] = bark_model["model"]
if model.num_parameters(exclude_embeddings=lowerCAmelCase_ ) != bark_model.get_num_params():
raise ValueError("initial and new models don't have the same number of parameters" )
# check if same output as the bark model
snake_case_ : Optional[Any] = 5
snake_case_ : Optional[int] = 1_0
if model_type in ["text", "coarse"]:
snake_case_ : Optional[Any] = torch.randint(2_5_6 , (batch_size, sequence_length) , dtype=torch.int )
snake_case_ : str = bark_model(lowerCAmelCase_ )[0]
snake_case_ : Tuple = model(lowerCAmelCase_ )
# take last logits
snake_case_ : List[str] = output_new_model_total.logits[:, [-1], :]
else:
snake_case_ : Optional[int] = 3
snake_case_ : str = 8
snake_case_ : List[str] = torch.randint(2_5_6 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
snake_case_ : Any = model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Union[str, Any] = bark_model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("initial and new outputs don't have the same shape" )
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("initial and new outputs are not equal" )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple , lowerCAmelCase_: List[str] , lowerCAmelCase_: Any , lowerCAmelCase_: List[Any] , lowerCAmelCase_: int , lowerCAmelCase_: Optional[Any] , ):
snake_case_ : Optional[Any] = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[str] = BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = EncodecConfig.from_pretrained("facebook/encodec_24khz" )
snake_case_ : List[str] = BarkSemanticModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkCoarseModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Tuple = BarkFineModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Union[str, Any] = EncodecModel.from_pretrained("facebook/encodec_24khz" )
snake_case_ : Tuple = BarkConfig.from_sub_model_configs(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : List[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
snake_case_ : Optional[int] = BarkModel(lowerCAmelCase_ )
snake_case_ : int = semantic
snake_case_ : List[str] = coarseAcoustic
snake_case_ : str = fineAcoustic
snake_case_ : Optional[Any] = codec
snake_case_ : Any = bark_generation_config
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
bark.save_pretrained(lowerCAmelCase_ , repo_id=lowerCAmelCase_ , push_to_hub=lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("model_type", type=str, help="text, coarse or fine.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
UpperCAmelCase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 666 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Union[str, Any] , A__ : VQModel , A__ : UNetaDModel , A__ : DDIMScheduler ) -> List[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=A__ , unet=A__ , scheduler=A__ )
@torch.no_grad()
def __call__( self : str , A__ : int = 1 , A__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , A__ : float = 0.0 , A__ : int = 50 , A__ : Optional[str] = "pil" , A__ : bool = True , **A__ : Optional[Any] , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
snake_case_ : Optional[int] = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=A__ , )
snake_case_ : List[Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
snake_case_ : Any = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(A__ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
snake_case_ : Union[str, Any] = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case_ : List[Any] = {}
if accepts_eta:
snake_case_ : int = eta
for t in self.progress_bar(self.scheduler.timesteps ):
snake_case_ : Union[str, Any] = self.scheduler.scale_model_input(A__ , A__ )
# predict the noise residual
snake_case_ : Dict = self.unet(A__ , A__ ).sample
# compute the previous noisy sample x_t -> x_t-1
snake_case_ : Union[str, Any] = self.scheduler.step(A__ , A__ , A__ , **A__ ).prev_sample
# decode the image latents with the VAE
snake_case_ : int = self.vqvae.decode(A__ ).sample
snake_case_ : Dict = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ : Optional[int] = self.numpy_to_pil(A__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A__ )
| 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"processing_clipseg": ["CLIPSegProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
"CLIPSegModel",
"CLIPSegPreTrainedModel",
"CLIPSegTextModel",
"CLIPSegVisionModel",
"CLIPSegForImageSegmentation",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
snake_case_ : List[str] = precision
snake_case_ : Union[str, Any] = ceil(precision / 1_4 )
snake_case_ : List[str] = 4_2_6_8_8_0 * Decimal(1_0_0_0_5 ).sqrt()
snake_case_ : str = 1
snake_case_ : List[str] = 1_3_5_9_1_4_0_9
snake_case_ : str = Decimal(lowerCAmelCase_ )
for k in range(1 , lowerCAmelCase_ ):
snake_case_ : Tuple = factorial(6 * k ) // (factorial(3 * k ) * factorial(lowerCAmelCase_ ) ** 3)
linear_term += 5_4_5_1_4_0_1_3_4
exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
UpperCAmelCase = 5_0
print(F"The first {n} digits of pi is: {pi(n)}")
| 666 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class snake_case__ ( _UpperCamelCase ):
def __init__( self : int , A__ : Optional[NestedDataStructureLike[PathLike]] = None , A__ : Optional[NamedSplit] = None , A__ : Optional[Features] = None , A__ : str = None , A__ : bool = False , A__ : bool = False , A__ : Optional[int] = None , **A__ : Optional[Any] , ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = path_or_paths
snake_case_ : Any = split if split or isinstance(A__ , A__ ) else "train"
snake_case_ : List[str] = features
snake_case_ : Optional[int] = cache_dir
snake_case_ : Optional[int] = keep_in_memory
snake_case_ : Any = streaming
snake_case_ : Any = num_proc
snake_case_ : List[str] = kwargs
@abstractmethod
def UpperCAmelCase__ ( self : int ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
'''simple docstring'''
pass
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Optional[int] , A__ : Optional[Features] = None , A__ : str = None , A__ : bool = False , A__ : bool = False , A__ : Optional[int] = None , **A__ : Optional[Any] , ) -> Any:
'''simple docstring'''
snake_case_ : Any = features
snake_case_ : str = cache_dir
snake_case_ : Any = keep_in_memory
snake_case_ : int = streaming
snake_case_ : Union[str, Any] = num_proc
snake_case_ : Any = kwargs
@abstractmethod
def UpperCAmelCase__ ( self : Optional[int] ) -> Union[Dataset, IterableDataset]:
'''simple docstring'''
pass
| 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = fa + fa
snake_case_ ,snake_case_ : str = fa, f
index += 1
for _ in str(lowerCAmelCase_ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ : str = int(max(0 , i - limit ) )
snake_case_ : Optional[int] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCAmelCase_ )
snake_case_ : List[Any] = f"{_stra[0:_stra.index(lowerCAmelCase_ )]} {_stra[_stra.index(lowerCAmelCase_ ) + 1:]}"
return "".join(lowerCAmelCase_ )
# matching characters
snake_case_ : List[Any] = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : int = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = len(lowerCAmelCase_ )
# transposition
snake_case_ : List[str] = (
len([(ca, ca) for ca, ca in zip(lowerCAmelCase_ , lowerCAmelCase_ ) if ca != ca] ) // 2
)
if not match_count:
snake_case_ : str = 0.0
else:
snake_case_ : Optional[Any] = (
1
/ 3
* (
match_count / len(lowerCAmelCase_ )
+ match_count / len(lowerCAmelCase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
snake_case_ : Optional[Any] = 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"))
| 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(lowerCAmelCase_ )
or left < -len(lowerCAmelCase_ )
or right >= len(lowerCAmelCase_ )
or right < -len(lowerCAmelCase_ )
):
raise IndexError("list index out of range" )
if left == right:
return nums[left]
snake_case_ : List[Any] = (left + right) >> 1 # the middle
snake_case_ : Dict = find_max(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # find max in range[left, mid]
snake_case_ : int = find_max(lowerCAmelCase_ , mid + 1 , lowerCAmelCase_ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 666 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
UpperCAmelCase = logging.get_logger(__name__)
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = ["input_features", "attention_mask"]
def __init__( self : Optional[int] , A__ : List[Any]=80 , A__ : List[str]=1_60_00 , A__ : Any=0.0 , A__ : str=10 , A__ : Any=25 , A__ : str="hamming_window" , A__ : List[str]=3_2768.0 , A__ : Any=0.97 , A__ : Dict=1.0 , A__ : int=True , A__ : int=True , A__ : List[Any]=False , **A__ : int , ) -> Tuple:
'''simple docstring'''
super().__init__(feature_size=A__ , sampling_rate=A__ , padding_value=A__ , **A__ )
snake_case_ : List[Any] = feature_size
snake_case_ : Optional[Any] = sampling_rate
snake_case_ : List[Any] = padding_value
snake_case_ : Tuple = hop_length
snake_case_ : str = win_length
snake_case_ : Optional[int] = frame_signal_scale
snake_case_ : List[Any] = preemphasis_coeff
snake_case_ : Union[str, Any] = mel_floor
snake_case_ : Optional[int] = normalize_means
snake_case_ : Optional[Any] = normalize_vars
snake_case_ : str = win_function
snake_case_ : List[str] = return_attention_mask
snake_case_ : Optional[int] = win_length * sampling_rate // 10_00
snake_case_ : Optional[Any] = hop_length * sampling_rate // 10_00
snake_case_ : Optional[Any] = optimal_fft_length(self.sample_size )
snake_case_ : Union[str, Any] = (self.n_fft // 2) + 1
def UpperCAmelCase__ ( self : Any , A__ : np.array ) -> np.ndarray:
'''simple docstring'''
if self.win_function == "hamming_window":
snake_case_ : str = window_function(window_length=self.sample_size , name=self.win_function , periodic=A__ )
else:
snake_case_ : str = window_function(window_length=self.sample_size , name=self.win_function )
snake_case_ : str = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
snake_case_ : Union[str, Any] = spectrogram(
one_waveform * self.frame_signal_scale , window=A__ , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=A__ , preemphasis=self.preemphasis_coeff , mel_filters=A__ , mel_floor=self.mel_floor , log_mel="log" , )
return msfc_features.T
def UpperCAmelCase__ ( self : Any , A__ : Union[str, Any] , A__ : Optional[int] , A__ : Optional[int] ) -> Dict:
'''simple docstring'''
if self.normalize_means:
snake_case_ : Any = x[:input_length].mean(axis=0 )
snake_case_ : List[str] = np.subtract(A__ , A__ )
if self.normalize_vars:
snake_case_ : Tuple = x[:input_length].std(axis=0 )
snake_case_ : Optional[Any] = np.divide(A__ , A__ )
if input_length < x.shape[0]:
snake_case_ : List[str] = padding_value
# make sure array is in float32
snake_case_ : Optional[int] = x.astype(np.floataa )
return x
def UpperCAmelCase__ ( self : str , A__ : List[np.ndarray] , A__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
'''simple docstring'''
snake_case_ : Dict = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A__ , A__ , self.padding_value ) for x, n in zip(A__ , A__ )]
def __call__( self : Optional[Any] , A__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , A__ : Union[bool, str, PaddingStrategy] = False , A__ : Optional[int] = None , A__ : bool = False , A__ : Optional[int] = None , A__ : Optional[bool] = None , A__ : Optional[Union[str, TensorType]] = None , A__ : Optional[int] = None , **A__ : Union[str, Any] , ) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
f" {self.sampling_rate} and not {sampling_rate}." )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
snake_case_ : Optional[int] = isinstance(A__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}" )
snake_case_ : Optional[int] = is_batched_numpy or (
isinstance(A__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
snake_case_ : Union[str, Any] = [np.asarray(A__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A__ , np.ndarray ):
snake_case_ : Any = np.asarray(A__ , dtype=np.floataa )
elif isinstance(A__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
snake_case_ : Union[str, Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
snake_case_ : List[Any] = [raw_speech]
# extract fbank features
snake_case_ : Dict = [self._extract_mfsc_features(A__ ) for one_waveform in raw_speech]
# convert into correct format for padding
snake_case_ : Any = BatchFeature({"input_features": features} )
snake_case_ : Optional[int] = self.pad(
A__ , padding=A__ , max_length=A__ , truncation=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , **A__ , )
# make sure list is in array format
snake_case_ : int = padded_inputs.get("input_features" )
if isinstance(input_features[0] , A__ ):
snake_case_ : Optional[int] = [np.asarray(A__ , dtype=np.floataa ) for feature in input_features]
snake_case_ : Union[str, Any] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
snake_case_ : Tuple = [np.asarray(A__ , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
snake_case_ : List[str] = (
np.array(A__ , dtype=np.intaa )
if self._get_padding_strategies(A__ , max_length=A__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
snake_case_ : int = self.normalize(
padded_inputs["input_features"] , attention_mask=A__ )
if return_tensors is not None:
snake_case_ : Optional[Any] = padded_inputs.convert_to_tensors(A__ )
return padded_inputs
| 666 | import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase = {
"vocab_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
),
},
"merges_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
),
},
"tokenizer_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
"roberta-base-openai-detector": (
"https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
),
"roberta-large-openai-detector": (
"https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase = {
"roberta-base": 5_1_2,
"roberta-large": 5_1_2,
"roberta-large-mnli": 5_1_2,
"distilroberta-base": 5_1_2,
"roberta-base-openai-detector": 5_1_2,
"roberta-large-openai-detector": 5_1_2,
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE : int = ["input_ids", "attention_mask"]
_SCREAMING_SNAKE_CASE : List[str] = RobertaTokenizer
def __init__( self : Optional[int] , A__ : List[Any]=None , A__ : Optional[int]=None , A__ : List[str]=None , A__ : Dict="replace" , A__ : List[str]="<s>" , A__ : Optional[Any]="</s>" , A__ : List[str]="</s>" , A__ : List[Any]="<s>" , A__ : int="<unk>" , A__ : int="<pad>" , A__ : List[Any]="<mask>" , A__ : Any=False , A__ : Optional[int]=True , **A__ : Union[str, Any] , ) -> int:
'''simple docstring'''
super().__init__(
A__ , A__ , tokenizer_file=A__ , errors=A__ , bos_token=A__ , eos_token=A__ , sep_token=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , add_prefix_space=A__ , trim_offsets=A__ , **A__ , )
snake_case_ : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , A__ ) != add_prefix_space:
snake_case_ : List[Any] = getattr(A__ , pre_tok_state.pop("type" ) )
snake_case_ : Any = add_prefix_space
snake_case_ : List[Any] = pre_tok_class(**A__ )
snake_case_ : Optional[int] = add_prefix_space
snake_case_ : List[str] = "post_processor"
snake_case_ : Tuple = getattr(self.backend_tokenizer , A__ , A__ )
if tokenizer_component_instance:
snake_case_ : List[str] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
snake_case_ : str = tuple(state["sep"] )
if "cls" in state:
snake_case_ : Tuple = tuple(state["cls"] )
snake_case_ : Tuple = False
if state.get("add_prefix_space" , A__ ) != add_prefix_space:
snake_case_ : Optional[Any] = add_prefix_space
snake_case_ : str = True
if state.get("trim_offsets" , A__ ) != trim_offsets:
snake_case_ : Optional[int] = trim_offsets
snake_case_ : List[Any] = True
if changes_to_apply:
snake_case_ : int = getattr(A__ , state.pop("type" ) )
snake_case_ : List[Any] = component_class(**A__ )
setattr(self.backend_tokenizer , A__ , A__ )
@property
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Tuple , A__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else value
snake_case_ : Any = value
def UpperCAmelCase__ ( self : int , *A__ : Optional[Any] , **A__ : int ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Optional[Any] = kwargs.get("is_split_into_words" , A__ )
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(*A__ , **A__ )
def UpperCAmelCase__ ( self : Union[str, Any] , *A__ : Any , **A__ : List[Any] ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Optional[int] = kwargs.get("is_split_into_words" , A__ )
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(*A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : str , A__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
snake_case_ : Optional[Any] = self._tokenizer.model.save(A__ , name=A__ )
return tuple(A__ )
def UpperCAmelCase__ ( self : int , A__ : List[str] , A__ : Union[str, Any]=None ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Dict , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case_ : str = [self.sep_token_id]
snake_case_ : List[Any] = [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 + sep + token_ids_a + sep ) * [0]
| 666 | 1 |
from __future__ import annotations
import unittest
from transformers import 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 numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class snake_case__ :
def __init__( self : Union[str, Any] , A__ : Dict , A__ : Optional[int]=13 , A__ : Optional[int]=7 , A__ : Optional[Any]=True , A__ : Any=True , A__ : int=True , A__ : List[Any]=True , A__ : List[Any]=99 , A__ : Dict=32 , A__ : Tuple=2 , A__ : Optional[int]=4 , A__ : str=37 , A__ : Tuple="gelu" , A__ : Optional[int]=0.1 , A__ : int=0.1 , A__ : int=5_12 , A__ : int=16 , A__ : Union[str, Any]=2 , A__ : Optional[Any]=0.02 , A__ : List[str]=3 , A__ : str=4 , A__ : Union[str, Any]=None , A__ : int=0 , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Union[str, Any] = seq_length
snake_case_ : List[str] = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : List[Any] = use_token_type_ids
snake_case_ : Optional[int] = use_labels
snake_case_ : Tuple = vocab_size
snake_case_ : Optional[int] = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : Any = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Dict = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Optional[int] = max_position_embeddings
snake_case_ : int = type_vocab_size
snake_case_ : Dict = type_sequence_label_size
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : str = scope
snake_case_ : Dict = projection_dim
def UpperCAmelCase__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Optional[Any] = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
snake_case_ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Tuple = None
if self.use_token_type_ids:
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : str = None
snake_case_ : Dict = None
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : List[str] = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : Any = BertConfig(
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=A__ , initializer_range=self.initializer_range , )
snake_case_ : List[Any] = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self : List[Any] , A__ : Union[str, Any] , A__ : Dict , A__ : List[Any] , A__ : Dict , A__ : Dict , A__ : Optional[Any] , A__ : int ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = TFDPRContextEncoder(config=A__ )
snake_case_ : Dict = model(A__ , attention_mask=A__ , token_type_ids=A__ )
snake_case_ : Union[str, Any] = model(A__ , token_type_ids=A__ )
snake_case_ : Optional[Any] = model(A__ )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def UpperCAmelCase__ ( self : Any , A__ : Union[str, Any] , A__ : Union[str, Any] , A__ : Dict , A__ : int , A__ : str , A__ : Optional[Any] , A__ : List[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = TFDPRQuestionEncoder(config=A__ )
snake_case_ : Union[str, Any] = model(A__ , attention_mask=A__ , token_type_ids=A__ )
snake_case_ : List[Any] = model(A__ , token_type_ids=A__ )
snake_case_ : Optional[Any] = model(A__ )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def UpperCAmelCase__ ( self : Any , A__ : Union[str, Any] , A__ : str , A__ : Optional[Any] , A__ : List[str] , A__ : List[Any] , A__ : Optional[Any] , A__ : List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = TFDPRReader(config=A__ )
snake_case_ : Dict = model(A__ , attention_mask=A__ )
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) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def UpperCAmelCase__ ( self : int ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,
) : Optional[Any] = config_and_inputs
snake_case_ : Optional[Any] = {"input_ids": input_ids}
return config, inputs_dict
@require_tf
class snake_case__ ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[Any] = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
_SCREAMING_SNAKE_CASE : Optional[Any] = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
_SCREAMING_SNAKE_CASE : str = False
_SCREAMING_SNAKE_CASE : Tuple = False
_SCREAMING_SNAKE_CASE : Optional[Any] = False
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
_SCREAMING_SNAKE_CASE : Optional[Any] = False
def UpperCAmelCase__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Any = TFDPRModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCAmelCase__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : Any ) -> Any:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*A__ )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*A__ )
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*A__ )
@slow
def UpperCAmelCase__ ( self : str ) -> Dict:
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Union[str, Any] = TFDPRContextEncoder.from_pretrained(A__ )
self.assertIsNotNone(A__ )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Union[str, Any] = TFDPRContextEncoder.from_pretrained(A__ )
self.assertIsNotNone(A__ )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Union[str, Any] = TFDPRQuestionEncoder.from_pretrained(A__ )
self.assertIsNotNone(A__ )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : List[str] = TFDPRReader.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@require_tf
class snake_case__ ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base" )
snake_case_ : List[Any] = tf.constant(
[[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] ) # [CLS] hello, is my dog cute? [SEP]
snake_case_ : Tuple = model(A__ )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
snake_case_ : List[str] = tf.constant(
[
[
0.0323_6253,
0.1275_3335,
0.1681_8509,
0.0027_9786,
0.389_6933,
0.2426_4945,
0.217_8971,
-0.0233_5227,
-0.0848_1959,
-0.1432_4117,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 666 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class snake_case__ ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Dict = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base" )
snake_case_ : Any = {
"input_ids": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute"
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
snake_case_ : List[str] = model(A__ )["last_hidden_state"]
snake_case_ : str = tf.TensorShape((1, 6, 7_68) )
self.assertEqual(output.shape , A__ )
# compare the actual values for a slice.
snake_case_ : List[str] = tf.convert_to_tensor(
[
[
[0.068_1762, 0.1089_4451, 0.0677_2504],
[-0.0642_3668, 0.0236_6615, 0.0432_9344],
[-0.0605_7295, 0.0997_4135, -0.0007_0584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 666 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] , lowerCAmelCase_: List[str] , lowerCAmelCase_: str , lowerCAmelCase_: Optional[int] ):
# Initialise PyTorch model
snake_case_ : List[str] = FunnelConfig.from_json_file(lowerCAmelCase_ )
print(f"Building PyTorch model from configuration: {config}" )
snake_case_ : Tuple = FunnelBaseModel(lowerCAmelCase_ ) if base_model else FunnelModel(lowerCAmelCase_ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = 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."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
UpperCAmelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 666 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCAmelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = ["pixel_values"]
def __init__( self : Union[str, Any] , A__ : bool = True , A__ : Dict[str, int] = None , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : bool = True , A__ : Dict[str, int] = None , A__ : bool = True , A__ : Union[int, float] = 1 / 2_55 , A__ : bool = True , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : bool = True , **A__ : Optional[int] , ) -> None:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : str = size if size is not None else {"shortest_edge": 2_24}
snake_case_ : Union[str, Any] = get_size_dict(A__ , default_to_square=A__ )
snake_case_ : List[Any] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
snake_case_ : Dict = get_size_dict(A__ , default_to_square=A__ , param_name="crop_size" )
snake_case_ : str = do_resize
snake_case_ : str = size
snake_case_ : Optional[Any] = resample
snake_case_ : Any = do_center_crop
snake_case_ : Any = crop_size
snake_case_ : str = do_rescale
snake_case_ : Optional[Any] = rescale_factor
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
snake_case_ : List[str] = image_std if image_std is not None else OPENAI_CLIP_STD
snake_case_ : int = do_convert_rgb
def UpperCAmelCase__ ( self : Optional[int] , A__ : np.ndarray , A__ : Dict[str, int] , A__ : PILImageResampling = PILImageResampling.BICUBIC , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[str] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : str = get_size_dict(A__ , default_to_square=A__ )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
snake_case_ : str = get_resize_output_image_size(A__ , size=size["shortest_edge"] , default_to_square=A__ )
return resize(A__ , size=A__ , resample=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Tuple , A__ : np.ndarray , A__ : Dict[str, int] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[Any] , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Optional[int] = get_size_dict(A__ )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
return center_crop(A__ , size=(size["height"], size["width"]) , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Optional[Any] , A__ : np.ndarray , A__ : Union[int, float] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : List[str] , ) -> str:
'''simple docstring'''
return rescale(A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : Any , A__ : np.ndarray , A__ : Union[float, List[float]] , A__ : Union[float, List[float]] , A__ : Optional[Union[str, ChannelDimension]] = None , **A__ : Any , ) -> np.ndarray:
'''simple docstring'''
return normalize(A__ , mean=A__ , std=A__ , data_format=A__ , **A__ )
def UpperCAmelCase__ ( self : List[Any] , A__ : ImageInput , A__ : bool = None , A__ : Dict[str, int] = None , A__ : PILImageResampling = None , A__ : bool = None , A__ : int = None , A__ : bool = None , A__ : float = None , A__ : bool = None , A__ : Optional[Union[float, List[float]]] = None , A__ : Optional[Union[float, List[float]]] = None , A__ : bool = None , A__ : Optional[Union[str, TensorType]] = None , A__ : Optional[ChannelDimension] = ChannelDimension.FIRST , **A__ : Optional[Any] , ) -> PIL.Image.Image:
'''simple docstring'''
snake_case_ : List[Any] = do_resize if do_resize is not None else self.do_resize
snake_case_ : Union[str, Any] = size if size is not None else self.size
snake_case_ : Any = get_size_dict(A__ , param_name="size" , default_to_square=A__ )
snake_case_ : Optional[int] = resample if resample is not None else self.resample
snake_case_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ : List[str] = crop_size if crop_size is not None else self.crop_size
snake_case_ : Tuple = get_size_dict(A__ , param_name="crop_size" , default_to_square=A__ )
snake_case_ : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ : Any = image_mean if image_mean is not None else self.image_mean
snake_case_ : List[str] = image_std if image_std is not None else self.image_std
snake_case_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
snake_case_ : List[Any] = make_list_of_images(A__ )
if not valid_images(A__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
snake_case_ : Dict = [convert_to_rgb(A__ ) for image in images]
# All transformations expect numpy arrays.
snake_case_ : Dict = [to_numpy_array(A__ ) for image in images]
if do_resize:
snake_case_ : Dict = [self.resize(image=A__ , size=A__ , resample=A__ ) for image in images]
if do_center_crop:
snake_case_ : Tuple = [self.center_crop(image=A__ , size=A__ ) for image in images]
if do_rescale:
snake_case_ : str = [self.rescale(image=A__ , scale=A__ ) for image in images]
if do_normalize:
snake_case_ : int = [self.normalize(image=A__ , mean=A__ , std=A__ ) for image in images]
snake_case_ : List[Any] = [to_channel_dimension_format(A__ , A__ ) for image in images]
snake_case_ : Tuple = {"pixel_values": images}
return BatchFeature(data=A__ , tensor_type=A__ )
| 666 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
"constant": get_constant_schedule,
"constant_w_warmup": get_constant_schedule_with_warmup,
}
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Dict , A__ : Optional[int]=None , A__ : str=None , *A__ : Tuple , **A__ : int ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*A__ , **A__ )
if config is None:
assert isinstance(self.model , A__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f" {self.model.__class__}"
)
snake_case_ : Tuple = self.model.config
else:
snake_case_ : Optional[int] = config
snake_case_ : Optional[Any] = data_args
snake_case_ : Any = self.config.tgt_vocab_size if isinstance(self.config , A__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
" padding.." )
if self.args.label_smoothing == 0:
snake_case_ : Tuple = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
snake_case_ : Tuple = label_smoothed_nll_loss
def UpperCAmelCase__ ( self : Optional[int] , A__ : int ) -> Tuple:
'''simple docstring'''
if self.optimizer is None:
snake_case_ : Dict = ["bias", "LayerNorm.weight"]
snake_case_ : Any = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
snake_case_ : List[Any] = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
snake_case_ : Dict = Adafactor
snake_case_ : List[Any] = {"scale_parameter": False, "relative_step": False}
else:
snake_case_ : Union[str, Any] = AdamW
snake_case_ : List[str] = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
snake_case_ : List[str] = self.args.learning_rate
if self.sharded_ddp:
snake_case_ : List[str] = OSS(
params=A__ , optim=A__ , **A__ , )
else:
snake_case_ : Any = optimizer_cls(A__ , **A__ )
if self.lr_scheduler is None:
snake_case_ : List[str] = self._get_lr_scheduler(A__ )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def UpperCAmelCase__ ( self : Dict , A__ : Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : Tuple = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
snake_case_ : Optional[Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
snake_case_ : Optional[int] = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
snake_case_ : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A__ )
return scheduler
def UpperCAmelCase__ ( self : List[str] ) -> Optional[torch.utils.data.Sampler]:
'''simple docstring'''
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def UpperCAmelCase__ ( self : Optional[int] , A__ : Optional[Any] , A__ : Optional[Any] , A__ : Optional[Any] ) -> Dict:
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
snake_case_ : Dict = model(**A__ , use_cache=A__ )[0]
snake_case_ : int = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
snake_case_ ,snake_case_ : List[str] = model(**A__ , labels=A__ , use_cache=A__ )[:2]
else:
# compute label smoothed loss
snake_case_ : List[str] = model(**A__ , use_cache=A__ )[0]
snake_case_ : List[str] = torch.nn.functional.log_softmax(A__ , dim=-1 )
snake_case_ ,snake_case_ : str = self.loss_fn(A__ , A__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def UpperCAmelCase__ ( self : Any , A__ : List[str] , A__ : List[str] ) -> str:
'''simple docstring'''
snake_case_ : Dict = inputs.pop("labels" )
snake_case_ ,snake_case_ : Tuple = self._compute_loss(A__ , A__ , A__ )
return loss
def UpperCAmelCase__ ( self : int , A__ : nn.Module , A__ : Dict[str, Union[torch.Tensor, Any]] , A__ : bool , A__ : Optional[List[str]] = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
'''simple docstring'''
snake_case_ : Any = self._prepare_inputs(A__ )
snake_case_ : Optional[Any] = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
snake_case_ : Optional[Any] = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **A__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
snake_case_ : Any = self._pad_tensors_to_max_len(A__ , gen_kwargs["max_length"] )
snake_case_ : Dict = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
snake_case_ ,snake_case_ : Optional[int] = self._compute_loss(A__ , A__ , A__ )
snake_case_ : Optional[Any] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
snake_case_ : Optional[Any] = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
snake_case_ : str = self._pad_tensors_to_max_len(A__ , gen_kwargs["max_length"] )
return (loss, logits, labels)
def UpperCAmelCase__ ( self : Tuple , A__ : Any , A__ : Optional[Any] ) -> int:
'''simple docstring'''
snake_case_ : int = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
f" padded to `max_length`={max_length}" )
snake_case_ : List[str] = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
snake_case_ : str = tensor
return padded_tensor
| 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
snake_case_ : Union[str, Any] = []
for position in positions:
snake_case_ ,snake_case_ : Union[str, Any] = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(lowerCAmelCase_ )
return permissible_positions
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[list[int]] ):
return not any(elem == 0 for row in board for elem in row )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[list[int]] , lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
if is_complete(lowerCAmelCase_ ):
return True
for position in get_valid_pos(lowerCAmelCase_ , len(lowerCAmelCase_ ) ):
snake_case_ ,snake_case_ : Dict = position
if board[y][x] == 0:
snake_case_ : List[str] = curr + 1
if open_knight_tour_helper(lowerCAmelCase_ , lowerCAmelCase_ , curr + 1 ):
return True
snake_case_ : Dict = 0
return False
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = [[0 for i in range(lowerCAmelCase_ )] for j in range(lowerCAmelCase_ )]
for i in range(lowerCAmelCase_ ):
for j in range(lowerCAmelCase_ ):
snake_case_ : Optional[Any] = 1
if open_knight_tour_helper(lowerCAmelCase_ , (i, j) , 1 ):
return board
snake_case_ : Dict = 0
snake_case_ : str = f"Open Kight Tour cannot be performed on a board of size {n}"
raise ValueError(lowerCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 | 1 |
UpperCAmelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
UpperCAmelCase = [{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , A__ : Any=24 , A__ : List[Any]=16 , A__ : List[Any]=40_96 , A__ : int="gelu" , A__ : List[str]=0.1 , A__ : List[str]=0.1 , A__ : str=5_12 , A__ : int=0.02 , A__ : Any=1E-12 , A__ : Optional[Any]=0 , A__ : List[str]=2 , A__ : Optional[int]=1 , A__ : str="absolute" , A__ : Any=True , **A__ : Optional[Any] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
snake_case_ : str = vocab_size
snake_case_ : int = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = intermediate_size
snake_case_ : str = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : str = max_position_embeddings
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Dict = use_cache
| 666 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
UpperCAmelCase = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str ):
with open(lowerCAmelCase_ , "rb" ) as f:
snake_case_ : Optional[Any] = Image.open(lowerCAmelCase_ )
return im.convert("RGB" )
@dataclass
class snake_case__ :
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={
"help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
} , )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(default=_UpperCamelCase , metadata={"help": "A folder containing the training data."} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(default=_UpperCamelCase , metadata={"help": "A folder containing the validation data."} )
_SCREAMING_SNAKE_CASE : Optional[float] = field(
default=0.1_5 , metadata={"help": "Percent to split off of train for validation."} )
_SCREAMING_SNAKE_CASE : Optional[int] = field(
default=_UpperCamelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
_SCREAMING_SNAKE_CASE : Optional[int] = field(
default=_UpperCamelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
"You must specify either a dataset name from the hub or a train and/or validation directory." )
@dataclass
class snake_case__ :
_SCREAMING_SNAKE_CASE : str = field(
default="google/vit-base-patch16-224-in21k" , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} , )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_UpperCamelCase )} , )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_UpperCamelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
_SCREAMING_SNAKE_CASE : str = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
_SCREAMING_SNAKE_CASE : str = field(default=_UpperCamelCase , metadata={"help": "Name or path of preprocessor config."} )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Union[str, Any] ):
snake_case_ : Any = torch.stack([example["pixel_values"] for example in examples] )
snake_case_ : List[Any] = torch.tensor([example["labels"] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def SCREAMING_SNAKE_CASE_ ( ):
# 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_ : str = 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_ : Dict = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ ,snake_case_ ,snake_case_ : str = 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_image_classification" , lowerCAmelCase_ , lowerCAmelCase_ )
# 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_ : Dict = training_args.get_process_log_level()
logger.setLevel(lowerCAmelCase_ )
transformers.utils.logging.set_verbosity(lowerCAmelCase_ )
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_ : Union[str, Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ : Dict = 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 )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
snake_case_ : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task="image-classification" , use_auth_token=True if model_args.use_auth_token else None , )
else:
snake_case_ : List[str] = {}
if data_args.train_dir is not None:
snake_case_ : Union[str, Any] = os.path.join(data_args.train_dir , "**" )
if data_args.validation_dir is not None:
snake_case_ : int = os.path.join(data_args.validation_dir , "**" )
snake_case_ : Union[str, Any] = load_dataset(
"imagefolder" , data_files=lowerCAmelCase_ , cache_dir=model_args.cache_dir , task="image-classification" , )
# If we don't have a validation split, split off a percentage of train as validation.
snake_case_ : Tuple = None if "validation" in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowerCAmelCase_ ) and data_args.train_val_split > 0.0:
snake_case_ : Union[str, Any] = dataset["train"].train_test_split(data_args.train_val_split )
snake_case_ : List[str] = split["train"]
snake_case_ : Tuple = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
snake_case_ : Tuple = dataset["train"].features["labels"].names
snake_case_ ,snake_case_ : Optional[int] = {}, {}
for i, label in enumerate(lowerCAmelCase_ ):
snake_case_ : str = str(lowerCAmelCase_ )
snake_case_ : Dict = label
# Load the accuracy metric from the datasets package
snake_case_ : Any = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowerCAmelCase_: List[Any] ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
snake_case_ : str = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowerCAmelCase_ ) , labelaid=lowerCAmelCase_ , idalabel=lowerCAmelCase_ , finetuning_task="image-classification" , 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_ : Any = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowerCAmelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or 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 , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
snake_case_ : Optional[Any] = image_processor.size["shortest_edge"]
else:
snake_case_ : int = (image_processor.size["height"], image_processor.size["width"])
snake_case_ : str = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
snake_case_ : Optional[int] = Compose(
[
RandomResizedCrop(lowerCAmelCase_ ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
snake_case_ : str = Compose(
[
Resize(lowerCAmelCase_ ),
CenterCrop(lowerCAmelCase_ ),
ToTensor(),
normalize,
] )
def train_transforms(lowerCAmelCase_: Optional[int] ):
snake_case_ : Optional[int] = [
_train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
]
return example_batch
def val_transforms(lowerCAmelCase_: Optional[Any] ):
snake_case_ : List[Any] = [_val_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError("--do_train requires a train dataset" )
if data_args.max_train_samples is not None:
snake_case_ : List[str] = (
dataset["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(lowerCAmelCase_ )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError("--do_eval requires a validation dataset" )
if data_args.max_eval_samples is not None:
snake_case_ : List[Any] = (
dataset["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(lowerCAmelCase_ )
# Initalize our trainer
snake_case_ : Dict = Trainer(
model=lowerCAmelCase_ , args=lowerCAmelCase_ , train_dataset=dataset["train"] if training_args.do_train else None , eval_dataset=dataset["validation"] if training_args.do_eval else None , compute_metrics=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ , data_collator=lowerCAmelCase_ , )
# Training
if training_args.do_train:
snake_case_ : Any = None
if training_args.resume_from_checkpoint is not None:
snake_case_ : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ : List[Any] = last_checkpoint
snake_case_ : Optional[int] = trainer.train(resume_from_checkpoint=lowerCAmelCase_ )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
snake_case_ : Any = trainer.evaluate()
trainer.log_metrics("eval" , lowerCAmelCase_ )
trainer.save_metrics("eval" , lowerCAmelCase_ )
# Write model card and (optionally) push to hub
snake_case_ : Optional[int] = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "image-classification",
"dataset": data_args.dataset_name,
"tags": ["image-classification", "vision"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCAmelCase_ )
else:
trainer.create_model_card(**lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : str = [True] * (end + 1)
snake_case_ : Any = []
while start <= end:
if temp[start] is True:
in_prime.append(lowerCAmelCase_ )
for i in range(start * start , end + 1 , lowerCAmelCase_ ):
snake_case_ : Union[str, Any] = False
start += 1
prime += in_prime
snake_case_ : Dict = end + 1
snake_case_ : Dict = min(2 * end , lowerCAmelCase_ )
while low <= n:
snake_case_ : Any = [True] * (high - low + 1)
for each in in_prime:
snake_case_ : Optional[Any] = math.floor(low / each ) * each
if t < low:
t += each
for j in range(lowerCAmelCase_ , high + 1 , lowerCAmelCase_ ):
snake_case_ : List[Any] = False
for j in range(len(lowerCAmelCase_ ) ):
if temp[j] is True:
prime.append(j + low )
snake_case_ : int = high + 1
snake_case_ : Union[str, Any] = min(high + end , lowerCAmelCase_ )
return prime
print(sieve(1_0**6))
| 666 | 1 |
# Copyright 2023 The HuggingFace 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
UpperCAmelCase = {"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MraForMaskedLM",
"MraForMultipleChoice",
"MraForQuestionAnswering",
"MraForSequenceClassification",
"MraForTokenClassification",
"MraLayer",
"MraModel",
"MraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 666 | 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 ConditionalDetrImageProcessor
class snake_case__ ( unittest.TestCase ):
def __init__( self : List[str] , A__ : List[Any] , A__ : int=7 , A__ : Union[str, Any]=3 , A__ : List[str]=30 , A__ : Optional[int]=4_00 , A__ : Optional[Any]=True , A__ : Optional[int]=None , A__ : Optional[Any]=True , A__ : Any=[0.5, 0.5, 0.5] , A__ : int=[0.5, 0.5, 0.5] , A__ : Any=True , A__ : int=1 / 2_55 , A__ : List[str]=True , ) -> Dict:
'''simple docstring'''
snake_case_ : int = size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
snake_case_ : Any = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : List[Any] = max_resolution
snake_case_ : Tuple = do_resize
snake_case_ : Dict = size
snake_case_ : Optional[Any] = do_normalize
snake_case_ : int = image_mean
snake_case_ : List[Any] = image_std
snake_case_ : Tuple = do_rescale
snake_case_ : Any = rescale_factor
snake_case_ : Optional[int] = do_pad
def UpperCAmelCase__ ( self : int ) -> List[str]:
'''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 UpperCAmelCase__ ( self : Optional[int] , A__ : Optional[int] , A__ : Any=False ) -> Optional[Any]:
'''simple docstring'''
if not batched:
snake_case_ : Any = image_inputs[0]
if isinstance(A__ , Image.Image ):
snake_case_ ,snake_case_ : Dict = image.size
else:
snake_case_ ,snake_case_ : int = image.shape[1], image.shape[2]
if w < h:
snake_case_ : Dict = int(self.size["shortest_edge"] * h / w )
snake_case_ : Optional[int] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : List[Any] = self.size["shortest_edge"]
else:
snake_case_ : str = []
for image in image_inputs:
snake_case_ ,snake_case_ : Tuple = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : List[Any] = max(A__ , key=lambda A__ : item[0] )[0]
snake_case_ : int = max(A__ , key=lambda A__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = ConditionalDetrImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = ConditionalDetrImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Any ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A__ , "image_mean" ) )
self.assertTrue(hasattr(A__ , "image_std" ) )
self.assertTrue(hasattr(A__ , "do_normalize" ) )
self.assertTrue(hasattr(A__ , "do_resize" ) )
self.assertTrue(hasattr(A__ , "size" ) )
def UpperCAmelCase__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = 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 , A__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , A__ )
def UpperCAmelCase__ ( self : str ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , Image.Image )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : Optional[Any] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ ,snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
snake_case_ : int = image_processing(A__ , 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 UpperCAmelCase__ ( self : int ) -> Any:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , numpify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , np.ndarray )
# Test not batched input
snake_case_ : int = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : List[str] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Optional[int] = image_processing(A__ , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : Dict = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase__ ( self : Tuple ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A__ , torchify=A__ )
for image in image_inputs:
self.assertIsInstance(A__ , torch.Tensor )
# Test not batched input
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : List[Any] = self.image_processor_tester.get_expected_values(A__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case_ : Any = image_processing(A__ , return_tensors="pt" ).pixel_values
snake_case_ ,snake_case_ : int = self.image_processor_tester.get_expected_values(A__ , batched=A__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : Optional[Any] = json.loads(f.read() )
snake_case_ : int = {"image_id": 3_97_69, "annotations": target}
# encode them
snake_case_ : Optional[int] = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
snake_case_ : Any = image_processing(images=A__ , annotations=A__ , return_tensors="pt" )
# verify pixel values
snake_case_ : List[Any] = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , A__ )
snake_case_ : List[str] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , A__ , atol=1E-4 ) )
# verify area
snake_case_ : Tuple = 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"] , A__ ) )
# verify boxes
snake_case_ : Any = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , A__ )
snake_case_ : str = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , A__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[Any] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , A__ ) )
# verify is_crowd
snake_case_ : Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , A__ ) )
# verify class_labels
snake_case_ : Any = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , A__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , A__ ) )
# verify size
snake_case_ : List[str] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , A__ ) )
@slow
def UpperCAmelCase__ ( self : int ) -> str:
'''simple docstring'''
snake_case_ : Tuple = 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_ : Any = json.loads(f.read() )
snake_case_ : Optional[Any] = {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}
snake_case_ : int = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : Union[str, Any] = ConditionalDetrImageProcessor(format="coco_panoptic" )
snake_case_ : str = image_processing(images=A__ , annotations=A__ , masks_path=A__ , return_tensors="pt" )
# verify pixel values
snake_case_ : int = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , A__ )
snake_case_ : str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , A__ , atol=1E-4 ) )
# verify area
snake_case_ : Optional[int] = 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"] , A__ ) )
# verify boxes
snake_case_ : str = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , A__ )
snake_case_ : str = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , A__ , atol=1E-3 ) )
# verify image_id
snake_case_ : List[str] = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , A__ ) )
# verify is_crowd
snake_case_ : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , A__ ) )
# verify class_labels
snake_case_ : Optional[int] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , A__ ) )
# verify masks
snake_case_ : Union[str, Any] = 82_28_73
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , A__ )
# verify orig_size
snake_case_ : Dict = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , A__ ) )
# verify size
snake_case_ : str = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , A__ ) )
| 666 | 1 |
from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , A__ : Any=24 , A__ : List[Any]=16 , A__ : List[Any]=40_96 , A__ : int="gelu" , A__ : List[str]=0.1 , A__ : List[str]=0.1 , A__ : str=5_12 , A__ : int=0.02 , A__ : Any=1E-12 , A__ : Optional[Any]=0 , A__ : List[str]=2 , A__ : Optional[int]=1 , A__ : str="absolute" , A__ : Any=True , **A__ : Optional[Any] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
snake_case_ : str = vocab_size
snake_case_ : int = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = intermediate_size
snake_case_ : str = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : str = max_position_embeddings
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Dict = use_cache
| 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class snake_case__ :
_SCREAMING_SNAKE_CASE : str = field(
default=_UpperCamelCase , metadata={"help": "Model type selected in the list: " + ", ".join(_UpperCamelCase )} )
_SCREAMING_SNAKE_CASE : str = field(
default=_UpperCamelCase , metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
_SCREAMING_SNAKE_CASE : int = field(
default=1_2_8 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_SCREAMING_SNAKE_CASE : int = field(
default=1_2_8 , metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."} , )
_SCREAMING_SNAKE_CASE : int = field(
default=6_4 , metadata={
"help": (
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
)
} , )
_SCREAMING_SNAKE_CASE : int = field(
default=3_0 , metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
} , )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
_SCREAMING_SNAKE_CASE : bool = field(
default=_UpperCamelCase , metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
_SCREAMING_SNAKE_CASE : float = field(
default=0.0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
_SCREAMING_SNAKE_CASE : int = field(
default=2_0 , metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
_SCREAMING_SNAKE_CASE : int = field(
default=0 , metadata={
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
} , )
_SCREAMING_SNAKE_CASE : int = field(default=1 , metadata={"help": "multiple threads for converting example to features"} )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Tuple = "train"
_SCREAMING_SNAKE_CASE : Any = "dev"
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : SquadDataTrainingArguments
_SCREAMING_SNAKE_CASE : List[SquadFeatures]
_SCREAMING_SNAKE_CASE : Split
_SCREAMING_SNAKE_CASE : bool
def __init__( self : str , A__ : SquadDataTrainingArguments , A__ : PreTrainedTokenizer , A__ : Optional[int] = None , A__ : Union[str, Split] = Split.train , A__ : Optional[bool] = False , A__ : Optional[str] = None , A__ : Optional[str] = "pt" , ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = args
snake_case_ : int = is_language_sensitive
snake_case_ : int = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(A__ , A__ ):
try:
snake_case_ : List[str] = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
snake_case_ : Tuple = mode
# Load data features from cache or dataset file
snake_case_ : Dict = "v2" if args.version_2_with_negative else "v1"
snake_case_ : List[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case_ : List[Any] = cached_features_file + ".lock"
with FileLock(A__ ):
if os.path.exists(A__ ) and not args.overwrite_cache:
snake_case_ : int = time.time()
snake_case_ : List[Any] = torch.load(A__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case_ : Tuple = self.old_features["features"]
snake_case_ : List[str] = self.old_features.get("dataset" , A__ )
snake_case_ : Tuple = self.old_features.get("examples" , A__ )
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
" future run" )
else:
if mode == Split.dev:
snake_case_ : Tuple = self.processor.get_dev_examples(args.data_dir )
else:
snake_case_ : Tuple = self.processor.get_train_examples(args.data_dir )
snake_case_ ,snake_case_ : Optional[Any] = squad_convert_examples_to_features(
examples=self.examples , tokenizer=A__ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=A__ , )
snake_case_ : Any = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , A__ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )
def __len__( self : str ) -> Dict:
'''simple docstring'''
return len(self.features )
def __getitem__( self : Optional[int] , A__ : Optional[int] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
snake_case_ : Any = self.features[i]
snake_case_ : Optional[int] = torch.tensor(feature.input_ids , dtype=torch.long )
snake_case_ : Union[str, Any] = torch.tensor(feature.attention_mask , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.token_type_ids , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.cls_index , dtype=torch.long )
snake_case_ : str = torch.tensor(feature.p_mask , dtype=torch.float )
snake_case_ : str = torch.tensor(feature.is_impossible , dtype=torch.float )
snake_case_ : Optional[int] = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case_ : Any = torch.tensor(feature.start_position , dtype=torch.long )
snake_case_ : List[Any] = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 666 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "git_vision_model"
def __init__( self : int , A__ : Union[str, Any]=7_68 , A__ : List[Any]=30_72 , A__ : Tuple=12 , A__ : Optional[Any]=12 , A__ : Optional[int]=3 , A__ : List[str]=2_24 , A__ : Dict=16 , A__ : int="quick_gelu" , A__ : Any=1E-5 , A__ : Tuple=0.0 , A__ : Optional[int]=0.02 , **A__ : List[str] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : str = intermediate_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = patch_size
snake_case_ : List[str] = image_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Any = attention_dropout
snake_case_ : Any = layer_norm_eps
snake_case_ : int = hidden_act
@classmethod
def UpperCAmelCase__ ( cls : List[Any] , A__ : Union[str, os.PathLike] , **A__ : Optional[int] ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(A__ )
snake_case_ ,snake_case_ : Tuple = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
snake_case_ : Any = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(A__ , **A__ )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = "git"
def __init__( self : Any , A__ : List[str]=None , A__ : List[str]=3_05_22 , A__ : Tuple=7_68 , A__ : Tuple=6 , A__ : str=12 , A__ : Any=30_72 , A__ : List[str]="gelu" , A__ : int=0.1 , A__ : Dict=0.1 , A__ : Any=10_24 , A__ : Optional[Any]=0.02 , A__ : Optional[Any]=1E-12 , A__ : Dict=0 , A__ : Any="absolute" , A__ : Tuple=True , A__ : Any=False , A__ : Tuple=1_01 , A__ : Tuple=1_02 , A__ : List[Any]=None , **A__ : List[str] , ) -> int:
'''simple docstring'''
super().__init__(bos_token_id=A__ , eos_token_id=A__ , pad_token_id=A__ , **A__ )
if vision_config is None:
snake_case_ : int = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
snake_case_ : str = GitVisionConfig(**A__ )
snake_case_ : int = vocab_size
snake_case_ : List[Any] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = hidden_act
snake_case_ : Dict = intermediate_size
snake_case_ : Any = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[str] = initializer_range
snake_case_ : List[str] = layer_norm_eps
snake_case_ : Any = position_embedding_type
snake_case_ : Union[str, Any] = use_cache
snake_case_ : str = tie_word_embeddings
snake_case_ : List[Any] = num_image_with_embedding
snake_case_ : Dict = bos_token_id
snake_case_ : int = eos_token_id
def UpperCAmelCase__ ( self : Any ) -> int:
'''simple docstring'''
snake_case_ : Tuple = copy.deepcopy(self.__dict__ )
snake_case_ : Optional[int] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
| 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "git_vision_model"
def __init__( self : int , A__ : Union[str, Any]=7_68 , A__ : List[Any]=30_72 , A__ : Tuple=12 , A__ : Optional[Any]=12 , A__ : Optional[int]=3 , A__ : List[str]=2_24 , A__ : Dict=16 , A__ : int="quick_gelu" , A__ : Any=1E-5 , A__ : Tuple=0.0 , A__ : Optional[int]=0.02 , **A__ : List[str] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : str = intermediate_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = patch_size
snake_case_ : List[str] = image_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Any = attention_dropout
snake_case_ : Any = layer_norm_eps
snake_case_ : int = hidden_act
@classmethod
def UpperCAmelCase__ ( cls : List[Any] , A__ : Union[str, os.PathLike] , **A__ : Optional[int] ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(A__ )
snake_case_ ,snake_case_ : Tuple = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
snake_case_ : Any = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(A__ , **A__ )
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = "git"
def __init__( self : Any , A__ : List[str]=None , A__ : List[str]=3_05_22 , A__ : Tuple=7_68 , A__ : Tuple=6 , A__ : str=12 , A__ : Any=30_72 , A__ : List[str]="gelu" , A__ : int=0.1 , A__ : Dict=0.1 , A__ : Any=10_24 , A__ : Optional[Any]=0.02 , A__ : Optional[Any]=1E-12 , A__ : Dict=0 , A__ : Any="absolute" , A__ : Tuple=True , A__ : Any=False , A__ : Tuple=1_01 , A__ : Tuple=1_02 , A__ : List[Any]=None , **A__ : List[str] , ) -> int:
'''simple docstring'''
super().__init__(bos_token_id=A__ , eos_token_id=A__ , pad_token_id=A__ , **A__ )
if vision_config is None:
snake_case_ : int = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
snake_case_ : str = GitVisionConfig(**A__ )
snake_case_ : int = vocab_size
snake_case_ : List[Any] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = hidden_act
snake_case_ : Dict = intermediate_size
snake_case_ : Any = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[str] = initializer_range
snake_case_ : List[str] = layer_norm_eps
snake_case_ : Any = position_embedding_type
snake_case_ : Union[str, Any] = use_cache
snake_case_ : str = tie_word_embeddings
snake_case_ : List[Any] = num_image_with_embedding
snake_case_ : Dict = bos_token_id
snake_case_ : int = eos_token_id
def UpperCAmelCase__ ( self : Any ) -> int:
'''simple docstring'''
snake_case_ : Tuple = copy.deepcopy(self.__dict__ )
snake_case_ : Optional[int] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
| 666 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = DDIMPipeline
_SCREAMING_SNAKE_CASE : int = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
_SCREAMING_SNAKE_CASE : List[str] = PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"latents",
"callback",
"callback_steps",
}
_SCREAMING_SNAKE_CASE : List[Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
_SCREAMING_SNAKE_CASE : List[str] = False
def UpperCAmelCase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Optional[Any] = 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") , )
snake_case_ : Dict = DDIMScheduler()
snake_case_ : Optional[Any] = {"unet": unet, "scheduler": scheduler}
return components
def UpperCAmelCase__ ( self : Optional[int] , A__ : Any , A__ : List[Any]=0 ) -> List[str]:
'''simple docstring'''
if str(A__ ).startswith("mps" ):
snake_case_ : Optional[int] = torch.manual_seed(A__ )
else:
snake_case_ : Tuple = torch.Generator(device=A__ ).manual_seed(A__ )
snake_case_ : Dict = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
snake_case_ : Union[str, Any] = "cpu"
snake_case_ : Tuple = self.get_dummy_components()
snake_case_ : List[Any] = self.pipeline_class(**A__ )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
snake_case_ : Any = self.get_dummy_inputs(A__ )
snake_case_ : List[Any] = pipe(**A__ ).images
snake_case_ : Dict = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
snake_case_ : List[str] = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
snake_case_ : Tuple = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(A__ , 1E-3 )
def UpperCAmelCase__ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def UpperCAmelCase__ ( self : str ) -> List[Any]:
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3E-3 )
def UpperCAmelCase__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def UpperCAmelCase__ ( self : str ) -> int:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = "google/ddpm-cifar10-32"
snake_case_ : str = UNetaDModel.from_pretrained(A__ )
snake_case_ : str = DDIMScheduler()
snake_case_ : Optional[Any] = DDIMPipeline(unet=A__ , scheduler=A__ )
ddim.to(A__ )
ddim.set_progress_bar_config(disable=A__ )
snake_case_ : Optional[Any] = torch.manual_seed(0 )
snake_case_ : str = ddim(generator=A__ , eta=0.0 , output_type="numpy" ).images
snake_case_ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ : List[Any] = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[Any] = "google/ddpm-ema-bedroom-256"
snake_case_ : Optional[Any] = UNetaDModel.from_pretrained(A__ )
snake_case_ : Optional[Any] = DDIMScheduler.from_pretrained(A__ )
snake_case_ : Dict = DDIMPipeline(unet=A__ , scheduler=A__ )
ddpm.to(A__ )
ddpm.set_progress_bar_config(disable=A__ )
snake_case_ : Any = torch.manual_seed(0 )
snake_case_ : List[str] = ddpm(generator=A__ , output_type="numpy" ).images
snake_case_ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
snake_case_ : Union[str, Any] = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ : str = int(max(0 , i - limit ) )
snake_case_ : Optional[int] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCAmelCase_ )
snake_case_ : List[Any] = f"{_stra[0:_stra.index(lowerCAmelCase_ )]} {_stra[_stra.index(lowerCAmelCase_ ) + 1:]}"
return "".join(lowerCAmelCase_ )
# matching characters
snake_case_ : List[Any] = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : int = get_matched_characters(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = len(lowerCAmelCase_ )
# transposition
snake_case_ : List[str] = (
len([(ca, ca) for ca, ca in zip(lowerCAmelCase_ , lowerCAmelCase_ ) if ca != ca] ) // 2
)
if not match_count:
snake_case_ : str = 0.0
else:
snake_case_ : Optional[Any] = (
1
/ 3
* (
match_count / len(lowerCAmelCase_ )
+ match_count / len(lowerCAmelCase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
snake_case_ : Optional[Any] = 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"))
| 666 | 1 |
from __future__ import annotations
class snake_case__ :
def __init__( self : Union[str, Any] , A__ : int = 0 ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = key
def UpperCAmelCase__ ( self : Optional[int] , A__ : str , A__ : int ) -> list[str]:
'''simple docstring'''
assert isinstance(A__ , A__ ) and isinstance(A__ , A__ )
snake_case_ : Dict = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(A__ ) ^ key ) for ch in content]
def UpperCAmelCase__ ( self : Union[str, Any] , A__ : str , A__ : int ) -> list[str]:
'''simple docstring'''
assert isinstance(A__ , A__ ) and isinstance(A__ , A__ )
snake_case_ : str = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(A__ ) ^ key ) for ch in content]
def UpperCAmelCase__ ( self : str , A__ : str , A__ : int = 0 ) -> str:
'''simple docstring'''
assert isinstance(A__ , A__ ) and isinstance(A__ , A__ )
snake_case_ : Optional[int] = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
snake_case_ : List[Any] = ""
for ch in content:
ans += chr(ord(A__ ) ^ key )
return ans
def UpperCAmelCase__ ( self : Dict , A__ : str , A__ : int = 0 ) -> str:
'''simple docstring'''
assert isinstance(A__ , A__ ) and isinstance(A__ , A__ )
snake_case_ : Union[str, Any] = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
snake_case_ : Dict = ""
for ch in content:
ans += chr(ord(A__ ) ^ key )
return ans
def UpperCAmelCase__ ( self : Any , A__ : str , A__ : int = 0 ) -> bool:
'''simple docstring'''
assert isinstance(A__ , A__ ) and isinstance(A__ , A__ )
try:
with open(A__ ) as fin, open("encrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(A__ , A__ ) )
except OSError:
return False
return True
def UpperCAmelCase__ ( self : Optional[int] , A__ : str , A__ : int ) -> bool:
'''simple docstring'''
assert isinstance(A__ , A__ ) and isinstance(A__ , A__ )
try:
with open(A__ ) as fin, open("decrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(A__ , A__ ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
set_seed(7_7_0)
UpperCAmelCase = {
"c_attn": "att_proj",
"c_proj": "out_proj",
"c_fc": "in_proj",
"transformer.": "",
"h.": "layers.",
"ln_1": "layernorm_1",
"ln_2": "layernorm_2",
"ln_f": "layernorm_final",
"wpe": "position_embeds_layer",
"wte": "input_embeds_layer",
}
UpperCAmelCase = {
"text_small": {
"repo_id": "suno/bark",
"file_name": "text.pt",
},
"coarse_small": {
"repo_id": "suno/bark",
"file_name": "coarse.pt",
},
"fine_small": {
"repo_id": "suno/bark",
"file_name": "fine.pt",
},
"text": {
"repo_id": "suno/bark",
"file_name": "text_2.pt",
},
"coarse": {
"repo_id": "suno/bark",
"file_name": "coarse_2.pt",
},
"fine": {
"repo_id": "suno/bark",
"file_name": "fine_2.pt",
},
}
UpperCAmelCase = os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase = os.path.join(os.path.expanduser("~"), ".cache")
UpperCAmelCase = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: List[str]=False ):
snake_case_ : Union[str, Any] = model_type
if use_small:
key += "_small"
return os.path.join(lowerCAmelCase_ , REMOTE_MODEL_PATHS[key]["file_name"] )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: List[str] ):
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
hf_hub_download(repo_id=lowerCAmelCase_ , filename=lowerCAmelCase_ , local_dir=lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any , lowerCAmelCase_: Dict , lowerCAmelCase_: List[str]=False , lowerCAmelCase_: Dict="text" ):
if model_type == "text":
snake_case_ : int = BarkSemanticModel
snake_case_ : str = BarkSemanticConfig
snake_case_ : Optional[Any] = BarkSemanticGenerationConfig
elif model_type == "coarse":
snake_case_ : str = BarkCoarseModel
snake_case_ : Optional[int] = BarkCoarseConfig
snake_case_ : Any = BarkCoarseGenerationConfig
elif model_type == "fine":
snake_case_ : Optional[int] = BarkFineModel
snake_case_ : Tuple = BarkFineConfig
snake_case_ : List[str] = BarkFineGenerationConfig
else:
raise NotImplementedError()
snake_case_ : Optional[Any] = f"{model_type}_small" if use_small else model_type
snake_case_ : Any = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(lowerCAmelCase_ ):
logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`." )
_download(model_info["repo_id"] , model_info["file_name"] )
snake_case_ : Any = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
# this is a hack
snake_case_ : Union[str, Any] = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
snake_case_ : str = model_args["vocab_size"]
snake_case_ : Union[str, Any] = model_args["vocab_size"]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
snake_case_ : Union[str, Any] = model_args.pop("n_head" )
snake_case_ : int = model_args.pop("n_embd" )
snake_case_ : Any = model_args.pop("n_layer" )
snake_case_ : List[str] = ConfigClass(**checkpoint["model_args"] )
snake_case_ : Optional[Any] = ModelClass(config=lowerCAmelCase_ )
snake_case_ : Tuple = GenerationConfigClass()
snake_case_ : List[str] = model_generation_config
snake_case_ : Optional[int] = checkpoint["model"]
# fixup checkpoint
snake_case_ : Optional[int] = "_orig_mod."
for k, v in list(state_dict.items() ):
if k.startswith(lowerCAmelCase_ ):
# replace part of the key with corresponding layer name in HF implementation
snake_case_ : Tuple = k[len(lowerCAmelCase_ ) :]
for old_layer_name in new_layer_name_dict:
snake_case_ : int = new_k.replace(lowerCAmelCase_ , new_layer_name_dict[old_layer_name] )
snake_case_ : int = state_dict.pop(lowerCAmelCase_ )
snake_case_ : Optional[int] = set(state_dict.keys() ) - set(model.state_dict().keys() )
snake_case_ : str = {k for k in extra_keys if not k.endswith(".attn.bias" )}
snake_case_ : Any = set(model.state_dict().keys() ) - set(state_dict.keys() )
snake_case_ : List[Any] = {k for k in missing_keys if not k.endswith(".attn.bias" )}
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"extra keys found: {extra_keys}" )
if len(lowerCAmelCase_ ) != 0:
raise ValueError(f"missing keys: {missing_keys}" )
model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
snake_case_ : str = model.num_parameters(exclude_embeddings=lowerCAmelCase_ )
snake_case_ : Union[str, Any] = checkpoint["best_val_loss"].item()
logger.info(f"model loaded: {round(n_params/1e6 , 1 )}M params, {round(lowerCAmelCase_ , 3 )} loss" )
model.eval()
model.to(lowerCAmelCase_ )
del checkpoint, state_dict
return model
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] , lowerCAmelCase_: str=False , lowerCAmelCase_: int="text" ):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
snake_case_ : int = "cpu" # do conversion on cpu
snake_case_ : Optional[Any] = _get_ckpt_path(lowerCAmelCase_ , use_small=lowerCAmelCase_ )
snake_case_ : Tuple = _load_model(lowerCAmelCase_ , lowerCAmelCase_ , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
# load bark initial model
snake_case_ : int = _bark_load_model(lowerCAmelCase_ , "cpu" , model_type=lowerCAmelCase_ , use_small=lowerCAmelCase_ )
if model_type == "text":
snake_case_ : Union[str, Any] = bark_model["model"]
if model.num_parameters(exclude_embeddings=lowerCAmelCase_ ) != bark_model.get_num_params():
raise ValueError("initial and new models don't have the same number of parameters" )
# check if same output as the bark model
snake_case_ : Optional[Any] = 5
snake_case_ : Optional[int] = 1_0
if model_type in ["text", "coarse"]:
snake_case_ : Optional[Any] = torch.randint(2_5_6 , (batch_size, sequence_length) , dtype=torch.int )
snake_case_ : str = bark_model(lowerCAmelCase_ )[0]
snake_case_ : Tuple = model(lowerCAmelCase_ )
# take last logits
snake_case_ : List[str] = output_new_model_total.logits[:, [-1], :]
else:
snake_case_ : Optional[int] = 3
snake_case_ : str = 8
snake_case_ : List[str] = torch.randint(2_5_6 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
snake_case_ : Any = model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Union[str, Any] = bark_model(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[int] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("initial and new outputs don't have the same shape" )
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("initial and new outputs are not equal" )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Tuple , lowerCAmelCase_: List[str] , lowerCAmelCase_: Any , lowerCAmelCase_: List[Any] , lowerCAmelCase_: int , lowerCAmelCase_: Optional[Any] , ):
snake_case_ : Optional[Any] = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[str] = BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_ , "config.json" ) )
snake_case_ : List[Any] = EncodecConfig.from_pretrained("facebook/encodec_24khz" )
snake_case_ : List[str] = BarkSemanticModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Optional[Any] = BarkCoarseModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Tuple = BarkFineModel.from_pretrained(lowerCAmelCase_ )
snake_case_ : Union[str, Any] = EncodecModel.from_pretrained("facebook/encodec_24khz" )
snake_case_ : Tuple = BarkConfig.from_sub_model_configs(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : List[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
snake_case_ : Optional[int] = BarkModel(lowerCAmelCase_ )
snake_case_ : int = semantic
snake_case_ : List[str] = coarseAcoustic
snake_case_ : str = fineAcoustic
snake_case_ : Optional[Any] = codec
snake_case_ : Any = bark_generation_config
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
bark.save_pretrained(lowerCAmelCase_ , repo_id=lowerCAmelCase_ , push_to_hub=lowerCAmelCase_ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("model_type", type=str, help="text, coarse or fine.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
UpperCAmelCase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
snake_case_ : str = f"The input value of [n={number}] has to be > 0"
raise ValueError(lowerCAmelCase_ )
else:
snake_case_ : List[Any] = sylvester(number - 1 )
snake_case_ : List[str] = num - 1
snake_case_ : Tuple = num
return lower * upper + 1
if __name__ == "__main__":
print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")
| 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 666 | 1 |
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