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from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A ( __lowercase ):
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def lowerCAmelCase__ ( self: List[Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ ={"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
return Dataset.from_dict(_lowerCAmelCase )
def lowerCAmelCase__ ( self: Any ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ =self._create_example_records()
UpperCAmelCase_ =Dataset.from_list(_lowerCAmelCase )
self.assertListEqual(dset.column_names , ["col_1", "col_2"] )
for i, r in enumerate(_lowerCAmelCase ):
self.assertDictEqual(_lowerCAmelCase , example_records[i] )
def lowerCAmelCase__ ( self: int ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ =self._create_example_records()
UpperCAmelCase_ =Dataset.from_list(_lowerCAmelCase )
UpperCAmelCase_ =Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def lowerCAmelCase__ ( self: Tuple ) -> Dict: # checks what happens with missing columns
'''simple docstring'''
UpperCAmelCase_ =[{"col_1": 1}, {"col_2": "x"}]
UpperCAmelCase_ =Dataset.from_list(_lowerCAmelCase )
self.assertDictEqual(dset[0] , {"col_1": 1} )
self.assertDictEqual(dset[1] , {"col_1": None} ) # NB: first record is used for columns
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Dict: # checks if the type can be inferred from the second record
'''simple docstring'''
UpperCAmelCase_ =[{"col_1": []}, {"col_1": [1, 2]}]
UpperCAmelCase_ =Dataset.from_list(_lowerCAmelCase )
self.assertEqual(dset.info.features["col_1"] , Sequence(Value("int64" ) ) )
def lowerCAmelCase__ ( self: int ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ =Dataset.from_list([] )
self.assertEqual(len(_lowerCAmelCase ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 54
|
import random
from .binary_exp_mod import bin_exp_mod
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCamelCase : Optional[int] = n - 1
lowerCamelCase : int = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowerCamelCase : List[str] = 0
while count < prec:
lowerCamelCase : Optional[Any] = random.randint(2 ,n - 1 )
lowerCamelCase : Optional[int] = bin_exp_mod(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if b != 1:
lowerCamelCase : str = True
for _ in range(_SCREAMING_SNAKE_CASE ):
if b == n - 1:
lowerCamelCase : List[Any] = False
break
lowerCamelCase : Optional[int] = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Tuple = abs(int(input('Enter bound : ').strip()))
print('Here\'s the list of primes:')
print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 311
| 0
|
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(SCREAMING_SNAKE_CASE__ )
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
def count_of_possible_combinations_with_dp_array(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
__lowerCamelCase : List[Any] = sum(
count_of_possible_combinations_with_dp_array(target - item , SCREAMING_SNAKE_CASE__ )
for item in array )
__lowerCamelCase : Optional[int] = answer
return answer
__lowerCamelCase : Optional[int] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Dict = [0] * (target + 1)
__lowerCamelCase : List[str] = 1
for i in range(1 , target + 1 ):
for j in range(SCREAMING_SNAKE_CASE__ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase_ = 3
lowercase_ = 5
lowercase_ = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 230
|
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 230
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""facebook/data2vec-vision-base-ft""": (
"""https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
),
}
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = 'data2vec-vision'
def __init__( self : List[Any] , __snake_case : int=768 , __snake_case : Any=12 , __snake_case : Union[str, Any]=12 , __snake_case : str=3072 , __snake_case : int="gelu" , __snake_case : List[str]=0.0 , __snake_case : Tuple=0.0 , __snake_case : str=0.0_2 , __snake_case : Tuple=1e-1_2 , __snake_case : List[Any]=224 , __snake_case : List[Any]=16 , __snake_case : Dict=3 , __snake_case : Tuple=False , __snake_case : Any=False , __snake_case : str=False , __snake_case : Any=False , __snake_case : List[str]=0.1 , __snake_case : Optional[Any]=0.1 , __snake_case : int=True , __snake_case : Optional[int]=[3, 5, 7, 11] , __snake_case : Optional[int]=[1, 2, 3, 6] , __snake_case : int=True , __snake_case : str=0.4 , __snake_case : Optional[Any]=256 , __snake_case : Union[str, Any]=1 , __snake_case : Union[str, Any]=False , __snake_case : Dict=255 , **__snake_case : List[str] , ):
super().__init__(**__snake_case )
lowerCamelCase :Optional[Any] = hidden_size
lowerCamelCase :Any = num_hidden_layers
lowerCamelCase :str = num_attention_heads
lowerCamelCase :Any = intermediate_size
lowerCamelCase :List[str] = hidden_act
lowerCamelCase :Any = hidden_dropout_prob
lowerCamelCase :Dict = attention_probs_dropout_prob
lowerCamelCase :Dict = initializer_range
lowerCamelCase :Any = layer_norm_eps
lowerCamelCase :Tuple = image_size
lowerCamelCase :List[str] = patch_size
lowerCamelCase :Dict = num_channels
lowerCamelCase :Any = use_mask_token
lowerCamelCase :List[Any] = use_absolute_position_embeddings
lowerCamelCase :Tuple = use_relative_position_bias
lowerCamelCase :int = use_shared_relative_position_bias
lowerCamelCase :Any = layer_scale_init_value
lowerCamelCase :Any = drop_path_rate
lowerCamelCase :List[str] = use_mean_pooling
# decode head attributes (semantic segmentation)
lowerCamelCase :Optional[Any] = out_indices
lowerCamelCase :int = pool_scales
# auxiliary head attributes (semantic segmentation)
lowerCamelCase :Union[str, Any] = use_auxiliary_head
lowerCamelCase :List[Any] = auxiliary_loss_weight
lowerCamelCase :Union[str, Any] = auxiliary_channels
lowerCamelCase :str = auxiliary_num_convs
lowerCamelCase :List[Any] = auxiliary_concat_input
lowerCamelCase :List[str] = semantic_loss_ignore_index
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = version.parse('1.11' )
@property
def snake_case ( self : Any ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def snake_case ( self : List[Any] ):
return 1e-4
| 166
|
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
A__ = """\
@inproceedings{pillutla-etal:mauve:neurips2021,
title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
booktitle = {NeurIPS},
year = {2021}
}
"""
A__ = """\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.
MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.
For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).
This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""
A__ = """
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
predictions: list of generated text to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Optional Args:
num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
kmeans_max_iter: maximum number of k-means iterations. Default 500
featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
max_text_length: maximum number of tokens to consider. Default 1024
divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
mauve_scaling_factor: \"c\" from the paper. Default 5.
verbose: If True (default), print running time updates
seed: random seed to initialize k-means cluster assignments.
Returns:
mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
q_hist: same as above, but with q_text.
Examples:
>>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
>>> import datasets
>>> mauve = datasets.load_metric('mauve')
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
>>> print(out.mauve) # doctest: +SKIP
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
def snake_case ( self : int ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://github.com/krishnap25/mauve''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/krishnap25/mauve'''] , reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] , )
def snake_case ( self : List[str] , __snake_case : Any , __snake_case : Any , __snake_case : Tuple=None , __snake_case : Optional[Any]=None , __snake_case : List[Any]=None , __snake_case : int=None , __snake_case : Any="auto" , __snake_case : List[Any]=-1 , __snake_case : Tuple=0.9 , __snake_case : Dict=5 , __snake_case : Union[str, Any]=500 , __snake_case : Optional[Any]="gpt2-large" , __snake_case : Union[str, Any]=-1 , __snake_case : str=1024 , __snake_case : List[str]=25 , __snake_case : int=5 , __snake_case : int=True , __snake_case : List[Any]=25 , ):
lowerCamelCase :Optional[int] = compute_mauve(
p_text=__snake_case , q_text=__snake_case , p_features=__snake_case , q_features=__snake_case , p_tokens=__snake_case , q_tokens=__snake_case , num_buckets=__snake_case , pca_max_data=__snake_case , kmeans_explained_var=__snake_case , kmeans_num_redo=__snake_case , kmeans_max_iter=__snake_case , featurize_model_name=__snake_case , device_id=__snake_case , max_text_length=__snake_case , divergence_curve_discretization_size=__snake_case , mauve_scaling_factor=__snake_case , verbose=__snake_case , seed=__snake_case , )
return out
| 166
| 1
|
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( __lowerCAmelCase : Optional[int] = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
lowerCAmelCase_ = BeautifulSoup(requests.get(lowerCAmelCase__ ).text , "html.parser" )
lowerCAmelCase_ = soup.findAll("h1" )
lowerCAmelCase_ = soup.findAll("div" , {"class": "maincounter-number"} )
keys += soup.findAll("span" , {"class": "panel-title"} )
values += soup.findAll("div" , {"class": "number-table-main"} )
return {key.text.strip(): value.text.strip() for key, value in zip(lowerCAmelCase__ , lowerCAmelCase__ )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(f"""{key}\n{value}\n""")
| 714
|
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A = datasets.load_iris()
_A = np.array(data["data"])
_A = np.array(data["target"])
_A = data["target_names"]
_A, _A, _A, _A = train_test_split(X, y)
def lowerCamelCase__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
return np.linalg.norm(np.array(__lowerCAmelCase ) - np.array(__lowerCAmelCase ) )
def lowerCamelCase__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : Any=5 ):
"""simple docstring"""
lowerCAmelCase_ = zip(__lowerCAmelCase , __lowerCAmelCase )
# List of distances of all points from the point to be classified
lowerCAmelCase_ = []
for data_point in data:
lowerCAmelCase_ = euclidean_distance(data_point[0] , __lowerCAmelCase )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
lowerCAmelCase_ = [i[1] for i in sorted(__lowerCAmelCase )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
lowerCAmelCase_ = Counter(__lowerCAmelCase ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 279
| 0
|
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''adapter_layer''': '''encoder.layers.*.adapter_layer''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
'''pooling_layer.linear''': '''projector''',
'''pooling_layer.projection''': '''classifier''',
}
SCREAMING_SNAKE_CASE_ = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''projector''',
'''classifier''',
]
def lowercase__ ( lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = {}
with open(snake_case__ , 'r' ) as file:
for line_number, line in enumerate(snake_case__ ):
UpperCAmelCase = line.strip()
if line:
UpperCAmelCase = line.split()
UpperCAmelCase = line_number
UpperCAmelCase = words[0]
UpperCAmelCase = value
return result
def lowercase__ ( lowerCAmelCase : int , lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] , lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
for attribute in key.split('.' ):
UpperCAmelCase = getattr(snake_case__ , snake_case__ )
UpperCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(snake_case__ ):
UpperCAmelCase = PARAM_MAPPING[full_name.split('.' )[-1]]
UpperCAmelCase = 'param'
if weight_type is not None and weight_type != "param":
UpperCAmelCase = getattr(snake_case__ , snake_case__ ).shape
elif weight_type is not None and weight_type == "param":
UpperCAmelCase = hf_pointer
for attribute in hf_param_name.split('.' ):
UpperCAmelCase = getattr(snake_case__ , snake_case__ )
UpperCAmelCase = shape_pointer.shape
# let's reduce dimension
UpperCAmelCase = value[0]
else:
UpperCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}" )
if weight_type == "weight":
UpperCAmelCase = value
elif weight_type == "weight_g":
UpperCAmelCase = value
elif weight_type == "weight_v":
UpperCAmelCase = value
elif weight_type == "bias":
UpperCAmelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split('.' ):
UpperCAmelCase = getattr(snake_case__ , snake_case__ )
UpperCAmelCase = value
else:
UpperCAmelCase = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def lowercase__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : str , lowerCAmelCase : Dict , lowerCAmelCase : List[str] , lowerCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(snake_case__ ):
UpperCAmelCase = PARAM_MAPPING[full_name.split('.' )[-1]]
UpperCAmelCase = 'param'
if weight_type is not None and weight_type != "param":
UpperCAmelCase = '.'.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
UpperCAmelCase = '.'.join([key, hf_param_name] )
else:
UpperCAmelCase = key
UpperCAmelCase = value if 'lm_head' in full_key else value[0]
SCREAMING_SNAKE_CASE_ = {
'''W_a''': '''linear_1.weight''',
'''W_b''': '''linear_2.weight''',
'''b_a''': '''linear_1.bias''',
'''b_b''': '''linear_2.bias''',
'''ln_W''': '''norm.weight''',
'''ln_b''': '''norm.bias''',
}
def lowercase__ ( lowerCAmelCase : Dict , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : Tuple=None ) -> Any:
"""simple docstring"""
UpperCAmelCase = False
for key, mapped_key in MAPPING.items():
UpperCAmelCase = 'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
UpperCAmelCase = True
if "*" in mapped_key:
UpperCAmelCase = name.split(snake_case__ )[0].split('.' )[-2]
UpperCAmelCase = mapped_key.replace('*' , snake_case__ )
if "weight_g" in name:
UpperCAmelCase = 'weight_g'
elif "weight_v" in name:
UpperCAmelCase = 'weight_v'
elif "bias" in name:
UpperCAmelCase = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCAmelCase = 'weight'
else:
UpperCAmelCase = None
if hf_dict is not None:
rename_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
else:
set_recursively(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
return is_used
return is_used
def lowercase__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : int , lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = fairseq_model.state_dict()
UpperCAmelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , hf_model.config.feat_extract_norm == 'group' , )
UpperCAmelCase = True
else:
UpperCAmelCase = load_wavaveca_layer(snake_case__ , snake_case__ , snake_case__ )
if not is_used:
unused_weights.append(snake_case__ )
logger.warning(F"Unused weights: {unused_weights}" )
def lowercase__ ( lowerCAmelCase : Tuple , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = full_name.split('conv_layers.' )[-1]
UpperCAmelCase = name.split('.' )
UpperCAmelCase = int(items[0] )
UpperCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
UpperCAmelCase = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
UpperCAmelCase = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
UpperCAmelCase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
UpperCAmelCase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(snake_case__ )
@torch.no_grad()
def lowercase__ ( lowerCAmelCase : int , lowerCAmelCase : Dict , lowerCAmelCase : Optional[Any]=None , lowerCAmelCase : int=None , lowerCAmelCase : Dict=True , lowerCAmelCase : str=False ) -> int:
"""simple docstring"""
if config_path is not None:
UpperCAmelCase = WavaVecaConfig.from_pretrained(snake_case__ )
else:
UpperCAmelCase = WavaVecaConfig()
if is_seq_class:
UpperCAmelCase = read_txt_into_dict(snake_case__ )
UpperCAmelCase = idalabel
UpperCAmelCase = WavaVecaForSequenceClassification(snake_case__ )
UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=snake_case__ , return_attention_mask=snake_case__ , )
feature_extractor.save_pretrained(snake_case__ )
elif is_finetuned:
if dict_path:
UpperCAmelCase = Dictionary.load(snake_case__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase = target_dict.pad_index
UpperCAmelCase = target_dict.bos_index
UpperCAmelCase = target_dict.eos_index
UpperCAmelCase = len(target_dict.symbols )
UpperCAmelCase = os.path.join(snake_case__ , 'vocab.json' )
if not os.path.isdir(snake_case__ ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case__ ) )
return
os.makedirs(snake_case__ , exist_ok=snake_case__ )
UpperCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCAmelCase = 0
UpperCAmelCase = 1
with open(snake_case__ , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(snake_case__ , snake_case__ )
UpperCAmelCase = WavaVecaCTCTokenizer(
snake_case__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=snake_case__ , )
UpperCAmelCase = True if config.feat_extract_norm == 'layer' else False
UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=snake_case__ , return_attention_mask=snake_case__ , )
UpperCAmelCase = WavaVecaProcessor(feature_extractor=snake_case__ , tokenizer=snake_case__ )
processor.save_pretrained(snake_case__ )
UpperCAmelCase = WavaVecaForCTC(snake_case__ )
else:
UpperCAmelCase = WavaVecaForPreTraining(snake_case__ )
if is_finetuned or is_seq_class:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
UpperCAmelCase = argparse.Namespace(task='audio_pretraining' )
UpperCAmelCase = fairseq.tasks.setup_task(snake_case__ )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=snake_case__ )
UpperCAmelCase = model[0].eval()
recursively_load_weights(snake_case__ , snake_case__ , not is_finetuned )
hf_wavavec.save_pretrained(snake_case__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
parser.add_argument(
'''--is_seq_class''',
action='''store_true''',
help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
SCREAMING_SNAKE_CASE_ = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 373
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
"""google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
"""google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class A_ ( UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = '''big_bird'''
def __init__( self : str ,__A : Union[str, Any]=5_0358 ,__A : Any=768 ,__A : List[str]=12 ,__A : Union[str, Any]=12 ,__A : int=3072 ,__A : Tuple="gelu_new" ,__A : Any=0.1 ,__A : Optional[Any]=0.1 ,__A : Tuple=4096 ,__A : int=2 ,__A : Union[str, Any]=0.02 ,__A : Optional[int]=1e-12 ,__A : List[str]=True ,__A : List[Any]=0 ,__A : Optional[Any]=1 ,__A : Optional[int]=2 ,__A : Optional[int]=66 ,__A : Tuple="block_sparse" ,__A : Optional[int]=True ,__A : Optional[int]=False ,__A : Tuple=64 ,__A : str=3 ,__A : Optional[int]=None ,**__A : Dict ,) -> Union[str, Any]:
super().__init__(
pad_token_id=__A ,bos_token_id=__A ,eos_token_id=__A ,sep_token_id=__A ,**__A ,)
_lowercase = vocab_size
_lowercase = max_position_embeddings
_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 = initializer_range
_lowercase = type_vocab_size
_lowercase = layer_norm_eps
_lowercase = use_cache
_lowercase = rescale_embeddings
_lowercase = attention_type
_lowercase = use_bias
_lowercase = block_size
_lowercase = num_random_blocks
_lowercase = classifier_dropout
class A_ ( UpperCAmelCase ):
"""simple docstring"""
@property
def __UpperCAmelCase ( self : int ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowercase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowercase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 67
| 0
|
from __future__ import annotations
from collections import deque
class lowercase__:
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE) -> str:
"""simple docstring"""
UpperCamelCase__ : list[dict] =[]
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []})
for keyword in keywords:
self.add_keyword(__SCREAMING_SNAKE_CASE)
self.set_fail_transitions()
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) -> int | None:
"""simple docstring"""
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE) -> None:
"""simple docstring"""
UpperCamelCase__ : str =0
for character in keyword:
UpperCamelCase__ : Optional[Any] =self.find_next_state(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
})
self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
UpperCamelCase__ : Any =len(self.adlist) - 1
else:
UpperCamelCase__ : int =next_state
self.adlist[current_state]["output"].append(__SCREAMING_SNAKE_CASE)
def UpperCAmelCase ( self) -> None:
"""simple docstring"""
UpperCamelCase__ : deque =deque()
for node in self.adlist[0]["next_states"]:
q.append(__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : int =0
while q:
UpperCamelCase__ : Dict =q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(__SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Dict =self.adlist[r]["fail_state"]
while (
self.find_next_state(__SCREAMING_SNAKE_CASE , self.adlist[child]["value"]) is None
and state != 0
):
UpperCamelCase__ : Union[str, Any] =self.adlist[state]["fail_state"]
UpperCamelCase__ : Any =self.find_next_state(
__SCREAMING_SNAKE_CASE , self.adlist[child]["value"])
if self.adlist[child]["fail_state"] is None:
UpperCamelCase__ : str =0
UpperCamelCase__ : Optional[int] =(
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE) -> dict[str, list[int]]:
"""simple docstring"""
UpperCamelCase__ : dict ={} # returns a dict with keywords and list of its occurrences
UpperCamelCase__ : Optional[Any] =0
for i in range(len(__SCREAMING_SNAKE_CASE)):
while (
self.find_next_state(__SCREAMING_SNAKE_CASE , string[i]) is None
and current_state != 0
):
UpperCamelCase__ : Optional[int] =self.adlist[current_state]["fail_state"]
UpperCamelCase__ : List[Any] =self.find_next_state(__SCREAMING_SNAKE_CASE , string[i])
if next_state is None:
UpperCamelCase__ : Optional[Any] =0
else:
UpperCamelCase__ : Tuple =next_state
for key in self.adlist[current_state]["output"]:
if key not in result:
UpperCamelCase__ : Optional[Any] =[]
result[key].append(i - len(__SCREAMING_SNAKE_CASE) + 1)
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 582
|
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="my_dataset" )} ),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"train": SplitInfo()} ),
] , )
def _lowerCamelCase ( A_ : SplitDict ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Tuple =split_dict._to_yaml_list()
assert len(A_ ) == len(A_ )
UpperCamelCase__ : str =SplitDict._from_yaml_list(A_ )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
UpperCamelCase__ : int =None
# the split name of split_dict takes over the name of the split info object
UpperCamelCase__ : Dict =split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"split_info" , [SplitInfo(), SplitInfo(dataset_name=A_ ), SplitInfo(dataset_name="my_dataset" )] )
def _lowerCamelCase ( A_ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] =asdict(SplitDict({"train": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 582
| 1
|
def _SCREAMING_SNAKE_CASE ( __lowercase : list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
__A = sum(__lowercase ) / len(__lowercase ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(__lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 637
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[int] = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : Tuple = ["XLNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : List[str] = ["XLNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : Any = [
"XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLNetForMultipleChoice",
"XLNetForQuestionAnswering",
"XLNetForQuestionAnsweringSimple",
"XLNetForSequenceClassification",
"XLNetForTokenClassification",
"XLNetLMHeadModel",
"XLNetModel",
"XLNetPreTrainedModel",
"load_tf_weights_in_xlnet",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : Dict = [
"TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLNetForMultipleChoice",
"TFXLNetForQuestionAnsweringSimple",
"TFXLNetForSequenceClassification",
"TFXLNetForTokenClassification",
"TFXLNetLMHeadModel",
"TFXLNetMainLayer",
"TFXLNetModel",
"TFXLNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
__a : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 637
| 1
|
import requests
from bsa import BeautifulSoup
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : dict ) -> str:
'''simple docstring'''
A__ = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__ , params=SCREAMING_SNAKE_CASE__ ).content , 'html.parser' )
A__ = soup.find('div' , attrs={'class': 'gs_ri'} )
A__ = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
lowercase_ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 586
|
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
lowercase_ = random.Random()
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[int]=1.0 , SCREAMING_SNAKE_CASE__ : Tuple=None , SCREAMING_SNAKE_CASE__ : Tuple=None ) -> List[Any]:
'''simple docstring'''
if rng is None:
A__ = global_rng
A__ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple,lowercase_ : str,lowercase_ : Optional[Any]=7,lowercase_ : Union[str, Any]=4_0_0,lowercase_ : Optional[int]=2_0_0_0,lowercase_ : Dict=2_0_4_8,lowercase_ : int=1_2_8,lowercase_ : str=1,lowercase_ : List[Any]=5_1_2,lowercase_ : Union[str, Any]=3_0,lowercase_ : Any=4_4_1_0_0,)-> Union[str, Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = min_seq_length
A__ = max_seq_length
A__ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
A__ = spectrogram_length
A__ = feature_size
A__ = num_audio_channels
A__ = hop_length
A__ = chunk_length
A__ = sampling_rate
def snake_case__ ( self : Tuple )-> Dict:
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def snake_case__ ( self : Tuple,lowercase_ : List[Any]=False,lowercase_ : Optional[int]=False )-> str:
'''simple docstring'''
def _flatten(lowercase_ : Any ):
return list(itertools.chain(*lowercase_ ) )
if equal_length:
A__ = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
A__ = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length,self.max_seq_length,self.seq_length_diff )
]
if numpify:
A__ = [np.asarray(lowercase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class A ( _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = TvltFeatureExtractor
def snake_case__ ( self : Optional[Any] )-> Dict:
'''simple docstring'''
A__ = TvltFeatureExtractionTester(self )
def snake_case__ ( self : List[Any] )-> Any:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowercase_,'spectrogram_length' ) )
self.assertTrue(hasattr(lowercase_,'feature_size' ) )
self.assertTrue(hasattr(lowercase_,'num_audio_channels' ) )
self.assertTrue(hasattr(lowercase_,'hop_length' ) )
self.assertTrue(hasattr(lowercase_,'chunk_length' ) )
self.assertTrue(hasattr(lowercase_,'sampling_rate' ) )
def snake_case__ ( self : Dict )-> Any:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A__ = feat_extract_first.save_pretrained(lowercase_ )[0]
check_json_file_has_correct_format(lowercase_ )
A__ = self.feature_extraction_class.from_pretrained(lowercase_ )
A__ = feat_extract_first.to_dict()
A__ = feat_extract_second.to_dict()
A__ = dict_first.pop('mel_filters' )
A__ = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(lowercase_,lowercase_ ) )
self.assertEqual(lowercase_,lowercase_ )
def snake_case__ ( self : str )-> Dict:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A__ = os.path.join(lowercase_,'feat_extract.json' )
feat_extract_first.to_json_file(lowercase_ )
A__ = self.feature_extraction_class.from_json_file(lowercase_ )
A__ = feat_extract_first.to_dict()
A__ = feat_extract_second.to_dict()
A__ = dict_first.pop('mel_filters' )
A__ = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(lowercase_,lowercase_ ) )
self.assertEqual(lowercase_,lowercase_ )
def snake_case__ ( self : Optional[int] )-> Optional[int]:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
A__ = [floats_list((1, x) )[0] for x in range(8_0_0,1_4_0_0,2_0_0 )]
A__ = [np.asarray(lowercase_ ) for speech_input in speech_inputs]
# Test not batched input
A__ = feature_extractor(np_speech_inputs[0],return_tensors='np',sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
A__ = feature_extractor(lowercase_,return_tensors='np',sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
A__ = feature_extractor(
lowercase_,return_tensors='np',sampling_rate=4_4_1_0_0,mask_audio=lowercase_ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
A__ = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
A__ = np.asarray(lowercase_ )
A__ = feature_extractor(lowercase_,return_tensors='np',sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def snake_case__ ( self : Optional[Any],lowercase_ : Tuple )-> Tuple:
'''simple docstring'''
A__ = load_dataset('hf-internal-testing/librispeech_asr_dummy','clean',split='validation' )
# automatic decoding with librispeech
A__ = ds.sort('id' ).select(range(lowercase_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def snake_case__ ( self : List[Any] )-> Any:
'''simple docstring'''
A__ = self._load_datasamples(1 )
A__ = TvltFeatureExtractor()
A__ = feature_extractor(lowercase_,return_tensors='pt' ).audio_values
self.assertEquals(audio_values.shape,(1, 1, 1_9_2, 1_2_8) )
A__ = torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],lowercase_,atol=1E-4 ) )
| 586
| 1
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class SCREAMING_SNAKE_CASE__ (unittest.TestCase , _a ):
def A__ ( self : str ):
"""simple docstring"""
lowerCAmelCase__ = load_tool('''text-classification''' )
self.tool.setup()
lowerCAmelCase__ = load_tool('''text-classification''' , remote=__lowerCamelCase )
def A__ ( self : Union[str, Any] ):
"""simple docstring"""
lowerCAmelCase__ = self.tool('''That\'s quite cool''' , ['''positive''', '''negative'''] )
self.assertEqual(__lowerCamelCase , '''positive''' )
def A__ ( self : Tuple ):
"""simple docstring"""
lowerCAmelCase__ = self.remote_tool('''That\'s quite cool''' , ['''positive''', '''negative'''] )
self.assertEqual(__lowerCamelCase , '''positive''' )
def A__ ( self : Any ):
"""simple docstring"""
lowerCAmelCase__ = self.tool(text='''That\'s quite cool''' , labels=['''positive''', '''negative'''] )
self.assertEqual(__lowerCamelCase , '''positive''' )
def A__ ( self : Union[str, Any] ):
"""simple docstring"""
lowerCAmelCase__ = self.remote_tool(text='''That\'s quite cool''' , labels=['''positive''', '''negative'''] )
self.assertEqual(__lowerCamelCase , '''positive''' )
| 615
|
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
a__ : int = logging.get_logger(__name__)
a__ : int = {
"Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
# See all Marian models at https://huggingface.co/models?filter=marian
}
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : Union[str, Any] = "marian"
A : Dict = ["past_key_values"]
A : List[Any] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Any , lowerCAmelCase : List[Any]=5_81_01 , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : List[str]=10_24 , lowerCAmelCase : int=12 , lowerCAmelCase : Union[str, Any]=40_96 , lowerCAmelCase : int=16 , lowerCAmelCase : Dict=12 , lowerCAmelCase : Optional[int]=40_96 , lowerCAmelCase : str=16 , lowerCAmelCase : List[Any]=0.0 , lowerCAmelCase : Optional[Any]=0.0 , lowerCAmelCase : List[str]=True , lowerCAmelCase : Union[str, Any]=True , lowerCAmelCase : List[Any]="gelu" , lowerCAmelCase : int=10_24 , lowerCAmelCase : Optional[int]=0.1 , lowerCAmelCase : Optional[int]=0.0 , lowerCAmelCase : Tuple=0.0 , lowerCAmelCase : List[Any]=0.02 , lowerCAmelCase : Union[str, Any]=5_81_00 , lowerCAmelCase : str=False , lowerCAmelCase : Optional[Any]=5_81_00 , lowerCAmelCase : Union[str, Any]=0 , lowerCAmelCase : Optional[Any]=0 , lowerCAmelCase : int=True , **lowerCAmelCase : List[str] , ) -> Dict:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = decoder_vocab_size or vocab_size
lowercase__ = max_position_embeddings
lowercase__ = d_model
lowercase__ = encoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = encoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = use_cache
lowercase__ = encoder_layers
lowercase__ = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase__ = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , is_encoder_decoder=lowerCAmelCase , decoder_start_token_id=lowerCAmelCase , forced_eos_token_id=lowerCAmelCase , **lowerCAmelCase , )
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def UpperCAmelCase ( self : List[Any]) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
])
if self.use_past:
lowercase__ = {0: 'batch'}
lowercase__ = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
lowercase__ = {0: 'batch', 1: 'decoder_sequence'}
lowercase__ = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase , direction='inputs')
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
])
if self.use_past:
lowercase__, lowercase__ = self.num_layers
for i in range(lowerCAmelCase):
lowercase__ = {0: 'batch', 2: 'past_sequence + sequence'}
lowercase__ = {0: 'batch', 2: 'past_sequence + sequence'}
else:
lowercase__ = 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
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def UpperCAmelCase ( self : Any) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = super().outputs
else:
lowercase__ = super(lowerCAmelCase , self).outputs
if self.use_past:
lowercase__, lowercase__ = self.num_layers
for i in range(lowerCAmelCase):
lowercase__ = {0: 'batch', 2: 'past_sequence + sequence'}
lowercase__ = {0: 'batch', 2: 'past_sequence + sequence'}
return common_outputs
def UpperCAmelCase ( self : Optional[int] , lowerCAmelCase : PreTrainedTokenizer , lowerCAmelCase : int = -1 , lowerCAmelCase : int = -1 , lowerCAmelCase : bool = False , lowerCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
# Generate decoder inputs
lowercase__ = seq_length if not self.use_past else 1
lowercase__ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
lowercase__ = {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
lowercase__ = dict(**lowerCAmelCase , **lowerCAmelCase)
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
else:
import torch
lowercase__, lowercase__ = common_inputs['input_ids'].shape
lowercase__ = common_inputs['decoder_input_ids'].shape[1]
lowercase__, lowercase__ = self.num_attention_heads
lowercase__ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ = decoder_seq_length + 3
lowercase__ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase__ = torch.cat(
[common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase , lowerCAmelCase)] , dim=1)
lowercase__ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase__, lowercase__ = self.num_layers
lowercase__ = min(lowerCAmelCase , lowerCAmelCase)
lowercase__ = max(lowerCAmelCase , lowerCAmelCase) - min_num_layers
lowercase__ = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(lowerCAmelCase):
common_inputs["past_key_values"].append(
(
torch.zeros(lowerCAmelCase),
torch.zeros(lowerCAmelCase),
torch.zeros(lowerCAmelCase),
torch.zeros(lowerCAmelCase),
))
# TODO: test this.
lowercase__ = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(lowerCAmelCase , lowerCAmelCase):
common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)))
return common_inputs
def UpperCAmelCase ( self : str , lowerCAmelCase : PreTrainedTokenizer , lowerCAmelCase : int = -1 , lowerCAmelCase : int = -1 , lowerCAmelCase : bool = False , lowerCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
else:
import torch
lowercase__, lowercase__ = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
lowercase__ = seqlen + 2
lowercase__, lowercase__ = self.num_layers
lowercase__, lowercase__ = self.num_attention_heads
lowercase__ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ = common_inputs['attention_mask'].dtype
lowercase__ = torch.cat(
[common_inputs['attention_mask'], torch.ones(lowerCAmelCase , lowerCAmelCase , dtype=lowerCAmelCase)] , dim=1)
lowercase__ = [
(torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)) for _ in range(lowerCAmelCase)
]
return common_inputs
def UpperCAmelCase ( self : Dict , lowerCAmelCase : PreTrainedTokenizer , lowerCAmelCase : int = -1 , lowerCAmelCase : int = -1 , lowerCAmelCase : bool = False , lowerCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ = compute_effective_axis_dimension(
lowerCAmelCase , 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
lowercase__ = tokenizer.num_special_tokens_to_add(lowerCAmelCase)
lowercase__ = compute_effective_axis_dimension(
lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCAmelCase)
# Generate dummy inputs according to compute batch and sequence
lowercase__ = [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
lowercase__ = dict(tokenizer(lowerCAmelCase , return_tensors=lowerCAmelCase))
return common_inputs
def UpperCAmelCase ( self : str , lowerCAmelCase : PreTrainedTokenizer , lowerCAmelCase : int = -1 , lowerCAmelCase : int = -1 , lowerCAmelCase : bool = False , lowerCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowerCAmelCase , batch_size=lowerCAmelCase , seq_length=lowerCAmelCase , is_pair=lowerCAmelCase , framework=lowerCAmelCase)
else:
lowercase__ = self._generate_dummy_inputs_for_causal_lm(
lowerCAmelCase , batch_size=lowerCAmelCase , seq_length=lowerCAmelCase , is_pair=lowerCAmelCase , framework=lowerCAmelCase)
return common_inputs
def UpperCAmelCase ( self : List[Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : str , lowerCAmelCase : str) -> Optional[int]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = super()._flatten_past_key_values_(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
else:
lowercase__ = super(lowerCAmelCase , self)._flatten_past_key_values_(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase)
@property
def UpperCAmelCase ( self : Optional[int]) -> float:
"""simple docstring"""
return 1E-4
| 622
| 0
|
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
A = 2_5_6_0_4_7
A = 2_5_6_1_4_5
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = NllbTokenizer
lowerCAmelCase_ = NllbTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = {}
def _snake_case ( self : Tuple ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase = NllbTokenizer(snake_case__ , keep_accents=snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self : str ) -> Any:
_lowerCamelCase = NllbTokenizer(snake_case__ , keep_accents=snake_case__ )
_lowerCamelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(snake_case__ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_lowerCamelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
_lowerCamelCase = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
] , )
_lowerCamelCase = tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def _snake_case ( self : Any ) -> Dict:
_lowerCamelCase = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_lowerCamelCase = self.tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(snake_case__ )
_lowerCamelCase = tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
_lowerCamelCase = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(snake_case__ )
_lowerCamelCase = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=True
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
_lowerCamelCase = tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(snake_case__ )
_lowerCamelCase = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=False
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
_lowerCamelCase = tokenizer_p.save_pretrained(snake_case__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(snake_case__ )
_lowerCamelCase = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
@require_torch
def _snake_case ( self : Optional[Any] ) -> int:
if not self.test_seqaseq:
return
_lowerCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_lowerCamelCase = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
_lowerCamelCase = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
_lowerCamelCase = tokenizer.prepare_seqaseq_batch(
src_texts=snake_case__ , tgt_texts=snake_case__ , max_length=3 , max_target_length=1_0 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 1_0 )
# max_target_length will default to max_length if not specified
_lowerCamelCase = tokenizer.prepare_seqaseq_batch(
snake_case__ , tgt_texts=snake_case__ , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_lowerCamelCase = tokenizer.prepare_seqaseq_batch(
src_texts=snake_case__ , max_length=3 , max_target_length=1_0 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , snake_case__ )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def _snake_case ( self : Dict ) -> List[str]:
pass
def _snake_case ( self : List[str] ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = [AddedToken('<special>' , lstrip=snake_case__ )]
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
snake_case__ , additional_special_tokens=snake_case__ , **snake_case__ )
_lowerCamelCase = tokenizer_r.encode('Hey this is a <special> token' )
_lowerCamelCase = tokenizer_r.encode('<special>' , add_special_tokens=snake_case__ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(
snake_case__ , additional_special_tokens=snake_case__ , **snake_case__ , )
_lowerCamelCase = self.tokenizer_class.from_pretrained(
snake_case__ , additional_special_tokens=snake_case__ , **snake_case__ )
_lowerCamelCase = tokenizer_p.encode('Hey this is a <special> token' )
_lowerCamelCase = tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(snake_case__ , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = 'facebook/nllb-200-distilled-600M'
lowerCAmelCase_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowerCAmelCase_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowerCAmelCase_ = [
256_047,
16_297,
134_408,
8_165,
248_066,
14_734,
950,
1_135,
105_721,
3_573,
83,
27_352,
108,
49_486,
2,
]
@classmethod
def _snake_case ( cls : Optional[Any] ) -> Union[str, Any]:
_lowerCamelCase = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
_lowerCamelCase = 1
return cls
def _snake_case ( self : Tuple ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 2_5_6_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 2_5_6_0_0_2 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 2_5_6_0_5_7 )
def _snake_case ( self : List[Any] ) -> List[Any]:
_lowerCamelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
def _snake_case ( self : List[str] ) -> Any:
self.assertIn(snake_case__ , self.tokenizer.all_special_ids )
# fmt: off
_lowerCamelCase = [RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
# fmt: on
_lowerCamelCase = self.tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ )
_lowerCamelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
self.assertNotIn(self.tokenizer.eos_token , snake_case__ )
def _snake_case ( self : List[Any] ) -> Optional[int]:
_lowerCamelCase = ['this is gunna be a long sentence ' * 2_0]
assert isinstance(src_text[0] , snake_case__ )
_lowerCamelCase = 1_0
_lowerCamelCase = self.tokenizer(snake_case__ , max_length=snake_case__ , truncation=snake_case__ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , snake_case__ )
self.assertEqual(len(snake_case__ ) , snake_case__ )
def _snake_case ( self : Optional[int] ) -> str:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [2_5_6_2_0_3, 3] )
def _snake_case ( self : Dict ) -> List[str]:
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(snake_case__ )
_lowerCamelCase = NllbTokenizer.from_pretrained(snake_case__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , snake_case__ )
@require_torch
def _snake_case ( self : Optional[int] ) -> Optional[int]:
_lowerCamelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
_lowerCamelCase = shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual((2, 1_5) , batch.input_ids.shape )
self.assertEqual((2, 1_5) , batch.attention_mask.shape )
_lowerCamelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
self.assertEqual(snake_case__ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _snake_case ( self : Optional[Any] ) -> Optional[int]:
_lowerCamelCase = self.tokenizer(self.src_text , padding=snake_case__ , truncation=snake_case__ , max_length=3 , return_tensors='pt' )
_lowerCamelCase = self.tokenizer(
text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=1_0 , return_tensors='pt' )
_lowerCamelCase = targets['input_ids']
_lowerCamelCase = shift_tokens_right(
snake_case__ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def _snake_case ( self : List[Any] ) -> Optional[int]:
_lowerCamelCase = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(snake_case__ ) , {
# A, test, EOS, en_XX
'input_ids': [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 2_5_6_0_5_7,
} , )
@require_torch
def _snake_case ( self : int ) -> List[str]:
_lowerCamelCase = True
_lowerCamelCase = self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7] )
_lowerCamelCase = False
_lowerCamelCase = self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2] )
| 234
|
def lowerCamelCase ( UpperCamelCase : str ) -> list:
_lowerCamelCase = [0] * len(UpperCamelCase )
for i in range(1 , len(UpperCamelCase ) ):
# use last results for better performance - dynamic programming
_lowerCamelCase = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
_lowerCamelCase = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
_lowerCamelCase = j
return prefix_result
def lowerCamelCase ( UpperCamelCase : str ) -> int:
return max(prefix_function(UpperCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 234
| 1
|
'''simple docstring'''
from math import factorial
def lowerCamelCase ( UpperCAmelCase__ : int = 2_0 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
SCREAMING_SNAKE_CASE__ :Tuple = n // 2
return int(factorial(__A ) / (factorial(__A ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
UpperCamelCase_ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number.''')
| 209
|
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
A_ : Optional[int] = get_logger(__name__)
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple , _SCREAMING_SNAKE_CASE : Optional[str] = None ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = (
os.path.join(_SCREAMING_SNAKE_CASE , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
SCREAMING_SNAKE_CASE : Dict = Extractor
def _lowerCAmelCase ( self : List[Any] , _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(_SCREAMING_SNAKE_CASE )
return os.path.join(self.extract_dir , hash_url_to_filename(_SCREAMING_SNAKE_CASE ) )
def _lowerCAmelCase ( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : bool ) -> bool:
"""simple docstring"""
return force_extract or (
not os.path.isfile(_SCREAMING_SNAKE_CASE ) and not (os.path.isdir(_SCREAMING_SNAKE_CASE ) and os.listdir(_SCREAMING_SNAKE_CASE ))
)
def _lowerCAmelCase ( self : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : bool = False ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.extractor.infer_extractor_format(_SCREAMING_SNAKE_CASE )
if not extractor_format:
return input_path
SCREAMING_SNAKE_CASE : List[str] = self._get_output_path(_SCREAMING_SNAKE_CASE )
if self._do_extract(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.extractor.extract(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return output_path
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
@classmethod
@abstractmethod
def _lowerCAmelCase ( cls : Tuple , _SCREAMING_SNAKE_CASE : Union[Path, str] , **_SCREAMING_SNAKE_CASE : Tuple ) -> bool:
"""simple docstring"""
...
@staticmethod
@abstractmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
...
class lowerCAmelCase__ ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[bytes] = []
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , 'rb' ) as f:
return f.read(_SCREAMING_SNAKE_CASE )
@classmethod
def _lowerCAmelCase ( cls : str , _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : bytes = b"" ) -> bool:
"""simple docstring"""
if not magic_number:
SCREAMING_SNAKE_CASE : Any = max(len(_SCREAMING_SNAKE_CASE ) for cls_magic_number in cls.magic_numbers )
try:
SCREAMING_SNAKE_CASE : int = cls.read_magic_number(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
except OSError:
return False
return any(magic_number.startswith(_SCREAMING_SNAKE_CASE ) for cls_magic_number in cls.magic_numbers )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
@classmethod
def _lowerCAmelCase ( cls : List[str] , _SCREAMING_SNAKE_CASE : Union[Path, str] , **_SCREAMING_SNAKE_CASE : List[Any] ) -> bool:
"""simple docstring"""
return tarfile.is_tarfile(_SCREAMING_SNAKE_CASE )
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Dict ) -> Dict:
"""simple docstring"""
def resolved(_SCREAMING_SNAKE_CASE : str ) -> str:
return os.path.realpath(os.path.abspath(_SCREAMING_SNAKE_CASE ) )
def badpath(_SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ).startswith(_SCREAMING_SNAKE_CASE )
def badlink(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str ) -> bool:
# Links are interpreted relative to the directory containing the link
SCREAMING_SNAKE_CASE : List[str] = resolved(os.path.join(_SCREAMING_SNAKE_CASE , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : str = resolved(_SCREAMING_SNAKE_CASE )
for finfo in members:
if badpath(finfo.name , _SCREAMING_SNAKE_CASE ):
logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Optional[int] = tarfile.open(_SCREAMING_SNAKE_CASE )
tar_file.extractall(_SCREAMING_SNAKE_CASE , members=TarExtractor.safemembers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
tar_file.close()
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[int] = [B'''\x1F\x8B''']
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
with gzip.open(_SCREAMING_SNAKE_CASE , 'rb' ) as gzip_file:
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as extracted_file:
shutil.copyfileobj(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[Any] = [
B'''PK\x03\x04''',
B'''PK\x05\x06''', # empty archive
B'''PK\x07\x08''', # spanned archive
]
@classmethod
def _lowerCAmelCase ( cls : Tuple , _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : bytes = b"" ) -> bool:
"""simple docstring"""
if super().is_extractable(_SCREAMING_SNAKE_CASE , magic_number=_SCREAMING_SNAKE_CASE ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(_SCREAMING_SNAKE_CASE , 'rb' ) as fp:
SCREAMING_SNAKE_CASE : List[str] = _EndRecData(_SCREAMING_SNAKE_CASE )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
SCREAMING_SNAKE_CASE : str = fp.read(_SCREAMING_SNAKE_CASE ) # CD is where we expect it to be
if len(_SCREAMING_SNAKE_CASE ) == sizeCentralDir:
SCREAMING_SNAKE_CASE : Optional[Any] = struct.unpack(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
with zipfile.ZipFile(_SCREAMING_SNAKE_CASE , 'r' ) as zip_file:
zip_file.extractall(_SCREAMING_SNAKE_CASE )
zip_file.close()
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Union[str, Any] = [B'''\xFD\x37\x7A\x58\x5A\x00''']
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
with lzma.open(_SCREAMING_SNAKE_CASE ) as compressed_file:
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as extracted_file:
shutil.copyfileobj(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Dict = [B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00'''] # RAR_ID # RAR5_ID
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.RARFILE_AVAILABLE:
raise ImportError('Please pip install rarfile' )
import rarfile
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : str = rarfile.RarFile(_SCREAMING_SNAKE_CASE )
rf.extractall(_SCREAMING_SNAKE_CASE )
rf.close()
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[int] = [B'''\x28\xb5\x2F\xFD''']
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.ZSTANDARD_AVAILABLE:
raise ImportError('Please pip install zstandard' )
import zstandard as zstd
SCREAMING_SNAKE_CASE : Any = zstd.ZstdDecompressor()
with open(_SCREAMING_SNAKE_CASE , 'rb' ) as ifh, open(_SCREAMING_SNAKE_CASE , 'wb' ) as ofh:
dctx.copy_stream(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Union[str, Any] = [B'''\x42\x5A\x68''']
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
with bza.open(_SCREAMING_SNAKE_CASE , 'rb' ) as compressed_file:
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as extracted_file:
shutil.copyfileobj(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str = [B'''\x37\x7A\xBC\xAF\x27\x1C''']
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.PY7ZR_AVAILABLE:
raise ImportError('Please pip install py7zr' )
import pyazr
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
with pyazr.SevenZipFile(_SCREAMING_SNAKE_CASE , 'r' ) as archive:
archive.extractall(_SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[str] = [B'''\x04\x22\x4D\x18''']
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.LZ4_AVAILABLE:
raise ImportError('Please pip install lz4' )
import lza.frame
with lza.frame.open(_SCREAMING_SNAKE_CASE , 'rb' ) as compressed_file:
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as extracted_file:
shutil.copyfileobj(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _lowerCAmelCase ( cls : List[str] ) -> Tuple:
"""simple docstring"""
return max(
len(_SCREAMING_SNAKE_CASE )
for extractor in cls.extractors.values()
if issubclass(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _lowerCAmelCase ( _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : int ) -> List[str]:
"""simple docstring"""
try:
return MagicNumberBaseExtractor.read_magic_number(_SCREAMING_SNAKE_CASE , magic_number_length=_SCREAMING_SNAKE_CASE )
except OSError:
return b""
@classmethod
def _lowerCAmelCase ( cls : Any , _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : bool = False ) -> bool:
"""simple docstring"""
warnings.warn(
'Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
'Use \'infer_extractor_format\' instead.' , category=_SCREAMING_SNAKE_CASE , )
SCREAMING_SNAKE_CASE : Optional[Any] = cls.infer_extractor_format(_SCREAMING_SNAKE_CASE )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _lowerCAmelCase ( cls : Union[str, Any] , _SCREAMING_SNAKE_CASE : Union[Path, str] ) -> str: # <Added version="2.4.0"/>
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = cls._get_magic_number_max_length()
SCREAMING_SNAKE_CASE : str = cls._read_magic_number(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(_SCREAMING_SNAKE_CASE , magic_number=_SCREAMING_SNAKE_CASE ):
return extractor_format
@classmethod
def _lowerCAmelCase ( cls : Dict , _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Union[Path, str] , _SCREAMING_SNAKE_CASE : Optional[str] = None , _SCREAMING_SNAKE_CASE : Optional[BaseExtractor] = "deprecated" , ) -> None:
"""simple docstring"""
os.makedirs(os.path.dirname(_SCREAMING_SNAKE_CASE ) , exist_ok=_SCREAMING_SNAKE_CASE )
# Prevent parallel extractions
SCREAMING_SNAKE_CASE : List[str] = str(Path(_SCREAMING_SNAKE_CASE ).with_suffix('.lock' ) )
with FileLock(_SCREAMING_SNAKE_CASE ):
shutil.rmtree(_SCREAMING_SNAKE_CASE , ignore_errors=_SCREAMING_SNAKE_CASE )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): # passed as positional arg
warnings.warn(
'Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
'Use \'extractor_format\' instead.' , category=_SCREAMING_SNAKE_CASE , )
SCREAMING_SNAKE_CASE : int = extractor if extractor != 'deprecated' else extractor_format
else:
SCREAMING_SNAKE_CASE : int = cls.extractors[extractor_format]
return extractor.extract(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
warnings.warn(
'Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '
'exception in 3.0.0.' , category=_SCREAMING_SNAKE_CASE , )
for extractor in cls.extractors.values():
if extractor.is_extractable(_SCREAMING_SNAKE_CASE ):
return extractor.extract(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 265
| 0
|
'''simple docstring'''
from math import sqrt
def UpperCAmelCase ( A : int ):
SCREAMING_SNAKE_CASE : Optional[int] = 0
for i in range(1 , int(sqrt(A ) + 1 ) ):
if n % i == 0 and i != sqrt(A ):
total += i + n // i
elif i == sqrt(A ):
total += i
return total - n
def UpperCAmelCase ( A : int = 10000 ):
SCREAMING_SNAKE_CASE : Union[str, Any] = sum(
i
for i in range(1 , A )
if sum_of_divisors(sum_of_divisors(A ) ) == i and sum_of_divisors(A ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 464
|
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase_ : int = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_std': True,
'scale': 0.1,
'eta': 0.0,
't_grad_cutoff': 2,
'device': 'cpu',
}
if __name__ == "__main__":
lowerCAmelCase_ : int = 'hopper-medium-v2'
lowerCAmelCase_ : str = gym.make(env_name)
lowerCAmelCase_ : Union[str, Any] = ValueGuidedRLPipeline.from_pretrained(
'bglick13/hopper-medium-v2-value-function-hor32',
env=env,
)
env.seed(0)
lowerCAmelCase_ : Any = env.reset()
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : str = 1000
lowerCAmelCase_ : int = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
lowerCAmelCase_ : int = pipeline(obs, planning_horizon=32)
# execute action in environment
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = env.step(denorm_actions)
lowerCAmelCase_ : Optional[int] = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
f' {total_score}'
)
# save observations for rendering
rollout.append(next_observation.copy())
lowerCAmelCase_ : Union[str, Any] = next_observation
except KeyboardInterrupt:
pass
print(f'Total reward: {total_reward}')
| 464
| 1
|
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = ""
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separator
return joined.strip(lowerCAmelCase__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 82
|
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Optional[Any]:
lowercase__: Any = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowercase__: Any = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
lowercase__: int = 4
lowercase__: Tuple = 4_8
lowercase__: str = '''pixelshuffle_aux'''
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowercase__: List[str] = [6, 6, 6, 6]
lowercase__: Union[str, Any] = 6_0
lowercase__: int = [6, 6, 6, 6]
lowercase__: List[Any] = '''pixelshuffledirect'''
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowercase__: Optional[Any] = 4
lowercase__: Union[str, Any] = '''nearest+conv'''
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
lowercase__: Tuple = 1
lowercase__: Union[str, Any] = 1
lowercase__: Optional[int] = 1_2_6
lowercase__: Optional[int] = 7
lowercase__: str = 2_5_5.0
lowercase__: int = ''''''
return config
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
if "patch_embed.proj" in name and "layers" not in name:
lowercase__: Optional[int] = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__: List[str] = name.replace('''patch_embed.norm''' , '''embeddings.patch_embeddings.layernorm''' )
if "layers" in name:
lowercase__: Union[str, Any] = name.replace('''layers''' , '''encoder.stages''' )
if "residual_group.blocks" in name:
lowercase__: int = name.replace('''residual_group.blocks''' , '''layers''' )
if "attn.proj" in name:
lowercase__: Optional[Any] = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
lowercase__: Any = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
lowercase__: Optional[int] = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowercase__: Optional[Any] = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowercase__: Any = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowercase__: List[Any] = name.replace('''mlp.fc2''' , '''output.dense''' )
if "q_bias" in name:
lowercase__: Dict = name.replace('''q_bias''' , '''query.bias''' )
if "k_bias" in name:
lowercase__: List[Any] = name.replace('''k_bias''' , '''key.bias''' )
if "v_bias" in name:
lowercase__: Any = name.replace('''v_bias''' , '''value.bias''' )
if "cpb_mlp" in name:
lowercase__: int = name.replace('''cpb_mlp''' , '''continuous_position_bias_mlp''' )
if "patch_embed.proj" in name:
lowercase__: Tuple = name.replace('''patch_embed.proj''' , '''patch_embed.projection''' )
if name == "norm.weight":
lowercase__: int = '''layernorm.weight'''
if name == "norm.bias":
lowercase__: Tuple = '''layernorm.bias'''
if "conv_first" in name:
lowercase__: List[str] = name.replace('''conv_first''' , '''first_convolution''' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
lowercase__: Tuple = name.replace('''conv_last''' , '''final_convolution''' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
lowercase__: List[Any] = name.replace('''conv_before_upsample.0''' , '''conv_before_upsample''' )
if "upsample.0" in name:
lowercase__: Optional[int] = name.replace('''upsample.0''' , '''upsample.convolution_0''' )
if "upsample.2" in name:
lowercase__: Tuple = name.replace('''upsample.2''' , '''upsample.convolution_1''' )
lowercase__: Union[str, Any] = '''upsample.''' + name
elif config.upsampler == "pixelshuffledirect":
lowercase__: Tuple = name.replace('''upsample.0.weight''' , '''upsample.conv.weight''' )
lowercase__: List[Any] = name.replace('''upsample.0.bias''' , '''upsample.conv.bias''' )
else:
pass
else:
lowercase__: Any = '''swin2sr.''' + name
return name
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
for key in orig_state_dict.copy().keys():
lowercase__: List[Any] = orig_state_dict.pop(__UpperCAmelCase )
if "qkv" in key:
lowercase__: Optional[Any] = key.split('''.''' )
lowercase__: str = int(key_split[1] )
lowercase__: Tuple = int(key_split[4] )
lowercase__: Union[str, Any] = config.embed_dim
if "weight" in key:
lowercase__: Tuple = val[:dim, :]
lowercase__: Dict = val[dim : dim * 2, :]
lowercase__: Dict = val[-dim:, :]
else:
lowercase__: Optional[Any] = val[:dim]
lowercase__: Any = val[dim : dim * 2]
lowercase__: str = val[-dim:]
pass
else:
lowercase__: int = val
return orig_state_dict
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
lowercase__: int = get_config(__UpperCAmelCase )
lowercase__: str = SwinaSRForImageSuperResolution(__UpperCAmelCase )
model.eval()
lowercase__: Optional[Any] = torch.hub.load_state_dict_from_url(__UpperCAmelCase , map_location='''cpu''' )
lowercase__: int = convert_state_dict(__UpperCAmelCase , __UpperCAmelCase )
lowercase__, lowercase__: int = model.load_state_dict(__UpperCAmelCase , strict=__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
raise ValueError('''Missing keys when converting: {}'''.format(__UpperCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F"""Unexpected key {key} in state_dict""" )
# verify values
lowercase__: List[Any] = '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
lowercase__: Dict = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw ).convert('''RGB''' )
lowercase__: Tuple = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
lowercase__: Union[str, Any] = 1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
lowercase__: List[str] = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowercase__: Union[str, Any] = transforms(__UpperCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
lowercase__: Optional[int] = pixel_values[:, 0, :, :].unsqueeze(1 )
lowercase__: int = model(__UpperCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
lowercase__: List[str] = torch.Size([1, 3, 5_1_2, 5_1_2] )
lowercase__: int = torch.tensor(
[[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
lowercase__: Tuple = torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
lowercase__: Any = torch.tensor(
[[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
lowercase__: Tuple = torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
lowercase__: List[Any] = torch.tensor(
[[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
lowercase__: str = torch.Size([1, 3, 5_1_2, 5_1_2] )
lowercase__: Any = torch.tensor(
[[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
lowercase__: int = torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
lowercase__: List[str] = torch.tensor(
[[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]] )
assert (
outputs.reconstruction.shape == expected_shape
), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , __UpperCAmelCase , atol=1e-3 )
print('''Looks ok!''' )
lowercase__: Tuple = {
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''': (
'''swin2SR-classical-sr-x2-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth''': (
'''swin2SR-classical-sr-x4-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth''': (
'''swin2SR-compressed-sr-x4-48'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth''': (
'''swin2SR-lightweight-x2-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth''': (
'''swin2SR-realworld-sr-x4-64-bsrgan-psnr'''
),
}
lowercase__: str = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCAmelCase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
model.push_to_hub(F"""caidas/{model_name}""" )
processor.push_to_hub(F"""caidas/{model_name}""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
type=str,
help="URL of the original Swin2SR checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")
__A = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 586
| 0
|
lowerCAmelCase__: Any = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 311
|
import math
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> bool:
return math.sqrt(SCREAMING_SNAKE_CASE ) * math.sqrt(SCREAMING_SNAKE_CASE ) == num
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> bool:
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : Tuple = n
while left <= right:
SCREAMING_SNAKE_CASE_ : List[str] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
SCREAMING_SNAKE_CASE_ : Dict = mid - 1
else:
SCREAMING_SNAKE_CASE_ : Optional[int] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311
| 1
|
import argparse
import os
# New Code #
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
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################
_lowercase: List[str] = 1_6
_lowercase: int = 3_2
def _lowerCamelCase ( snake_case , snake_case = 16 ):
_lowerCAmelCase = AutoTokenizer.from_pretrained('bert-base-cased' )
_lowerCAmelCase = load_dataset('glue' , 'mrpc' )
def tokenize_function(snake_case ):
# max_length=None => use the model max length (it's actually the default)
_lowerCAmelCase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=snake_case , max_length=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():
_lowerCAmelCase = datasets.map(
snake_case , batched=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
_lowerCAmelCase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(snake_case ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_lowerCAmelCase = 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":
_lowerCAmelCase = 16
elif accelerator.mixed_precision != "no":
_lowerCAmelCase = 8
else:
_lowerCAmelCase = None
return tokenizer.pad(
snake_case , padding='longest' , max_length=snake_case , pad_to_multiple_of=snake_case , return_tensors='pt' , )
# Instantiate dataloaders.
_lowerCAmelCase = DataLoader(
tokenized_datasets['train'] , shuffle=snake_case , collate_fn=snake_case , batch_size=snake_case )
_lowerCAmelCase = DataLoader(
tokenized_datasets['validation'] , shuffle=snake_case , collate_fn=snake_case , batch_size=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
_lowercase: str = mocked_dataloaders # noqa: F811
def _lowerCamelCase ( snake_case , snake_case ):
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , snake_case ) == "1":
_lowerCAmelCase = 2
# Initialize accelerator
_lowerCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCAmelCase = config['lr']
_lowerCAmelCase = int(config['num_epochs'] )
_lowerCAmelCase = int(config['seed'] )
_lowerCAmelCase = int(config['batch_size'] )
_lowerCAmelCase = evaluate.load('glue' , 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=snake_case )
def inner_training_loop(snake_case ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=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).
_lowerCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_lowerCAmelCase = AdamW(params=model.parameters() , lr=snake_case )
_lowerCAmelCase , _lowerCAmelCase = get_dataloaders(snake_case , snake_case )
# Instantiate scheduler
_lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=snake_case , num_warmup_steps=100 , num_training_steps=(len(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.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = accelerator.prepare(
snake_case , snake_case , snake_case , snake_case , snake_case )
# Now we train the model
for epoch in range(snake_case ):
model.train()
for step, batch in enumerate(snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_lowerCAmelCase = model(**snake_case )
_lowerCAmelCase = outputs.loss
accelerator.backward(snake_case )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
_lowerCAmelCase , _lowerCAmelCase = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=snake_case , references=snake_case , )
_lowerCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , snake_case )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def _lowerCamelCase ( ):
_lowerCAmelCase = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=snake_case , default=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.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(snake_case , snake_case )
if __name__ == "__main__":
main()
| 192
|
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowercase: str = '''sshleifer/bart-tiny-random'''
_lowercase: Union[str, Any] = '''patrickvonplaten/t5-tiny-random'''
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
return AutoConfig.from_pretrained(lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ):
_lowerCAmelCase , *_lowerCAmelCase = create_student_by_copying_alternating_layers(lowercase__ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
_lowerCAmelCase , *_lowerCAmelCase = create_student_by_copying_alternating_layers(lowercase__ , tempfile.mkdtemp() , e=1 , d=lowercase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
_lowerCAmelCase , *_lowerCAmelCase = create_student_by_copying_alternating_layers(lowercase__ , tempfile.mkdtemp() , e=1 , d=lowercase__ )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def SCREAMING_SNAKE_CASE__ ( self : str ):
_lowerCAmelCase , *_lowerCAmelCase = create_student_by_copying_alternating_layers(lowercase__ , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
with self.assertRaises(lowercase__ ):
create_student_by_copying_alternating_layers(lowercase__ , tempfile.mkdtemp() , e=lowercase__ , d=lowercase__ )
| 192
| 1
|
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCamelCase : Optional[int] = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE ( lowercase_ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
"""simple docstring"""
A__ = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(lowercase_ ).content ).xpath(lowercase_ ) )
_lowerCamelCase : Optional[int] = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 177
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Any=2 , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : Any=False , UpperCAmelCase__ : Union[str, Any]=10 , UpperCAmelCase__ : Any=3 , UpperCAmelCase__ : Optional[int]=32 * 4 , UpperCAmelCase__ : int=32 * 6 , UpperCAmelCase__ : List[str]=4 , UpperCAmelCase__ : Dict=32 , ) ->Optional[int]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = is_training
A__ = use_auxiliary_loss
A__ = num_queries
A__ = num_channels
A__ = min_size
A__ = max_size
A__ = num_labels
A__ = mask_feature_size
def SCREAMING_SNAKE_CASE ( self : Dict) ->int:
'''simple docstring'''
A__ = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
UpperCAmelCase__)
A__ = torch.ones([self.batch_size, self.min_size, self.max_size] , device=UpperCAmelCase__)
A__ = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=UpperCAmelCase__) > 0.5
).float()
A__ = (torch.rand((self.batch_size, self.num_labels) , device=UpperCAmelCase__) > 0.5).long()
A__ = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE ( self : str) ->List[Any]:
'''simple docstring'''
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->List[Any]:
'''simple docstring'''
A__ , A__ , A__ , A__ , A__ = self.prepare_config_and_inputs()
A__ = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[Any]) ->int:
'''simple docstring'''
A__ = output.encoder_hidden_states
A__ = output.pixel_decoder_hidden_states
A__ = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(UpperCAmelCase__) , len(config.backbone_config.depths))
self.parent.assertTrue(len(UpperCAmelCase__) , len(config.backbone_config.depths))
self.parent.assertTrue(len(UpperCAmelCase__) , config.decoder_config.decoder_layers)
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any]=False) ->Optional[int]:
'''simple docstring'''
with torch.no_grad():
A__ = MaskFormerModel(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(pixel_values=UpperCAmelCase__ , pixel_mask=UpperCAmelCase__)
A__ = model(UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__)
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(output.encoder_last_hidden_state is not None)
if output_hidden_states:
self.check_output_hidden_state(UpperCAmelCase__ , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : str , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : int) ->List[Any]:
'''simple docstring'''
A__ = MaskFormerForInstanceSegmentation(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
def comm_check_on_output(UpperCAmelCase__ : str):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.encoder_last_hidden_state is not None)
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1))
with torch.no_grad():
A__ = model(pixel_values=UpperCAmelCase__ , pixel_mask=UpperCAmelCase__)
A__ = model(UpperCAmelCase__)
comm_check_on_output(UpperCAmelCase__)
A__ = model(
pixel_values=UpperCAmelCase__ , pixel_mask=UpperCAmelCase__ , mask_labels=UpperCAmelCase__ , class_labels=UpperCAmelCase__)
comm_check_on_output(UpperCAmelCase__)
self.parent.assertTrue(result.loss is not None)
self.parent.assertEqual(result.loss.shape , torch.Size([1]))
@require_torch
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
UpperCAmelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Optional[int]:
'''simple docstring'''
A__ = MaskFormerModelTester(self)
A__ = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : Dict) ->List[str]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(UpperCAmelCase__ , **UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : str) ->Any:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*UpperCAmelCase__)
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''')
def SCREAMING_SNAKE_CASE ( self : Dict) ->Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''')
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''MaskFormer is not a generative model''')
def SCREAMING_SNAKE_CASE ( self : str) ->Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''')
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Union[str, Any]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Optional[int]:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def SCREAMING_SNAKE_CASE ( self : Tuple) ->Dict:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self : Dict) ->Dict:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCAmelCase__)
A__ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__)
@slow
def SCREAMING_SNAKE_CASE ( self : Any) ->Optional[Any]:
'''simple docstring'''
for model_name in ["facebook/maskformer-swin-small-coco"]:
A__ = MaskFormerModel.from_pretrained(UpperCAmelCase__)
self.assertIsNotNone(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Optional[int]:
'''simple docstring'''
A__ = (self.model_tester.min_size,) * 2
A__ = {
'''pixel_values''': torch.randn((2, 3, *size) , device=UpperCAmelCase__),
'''mask_labels''': torch.randn((2, 10, *size) , device=UpperCAmelCase__),
'''class_labels''': torch.zeros(2 , 10 , device=UpperCAmelCase__).long(),
}
A__ = MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(UpperCAmelCase__)
A__ = model(**UpperCAmelCase__)
self.assertTrue(outputs.loss is not None)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Union[str, Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(UpperCAmelCase__ , **UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : str) ->Union[str, Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCAmelCase__).to(UpperCAmelCase__)
A__ = model(**UpperCAmelCase__ , output_attentions=UpperCAmelCase__)
self.assertTrue(outputs.attentions is not None)
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Union[str, Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
A__ = self.all_model_classes[1]
A__ , A__ , A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs()
A__ = model_class(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.train()
A__ = model(UpperCAmelCase__ , mask_labels=UpperCAmelCase__ , class_labels=UpperCAmelCase__).loss
loss.backward()
def SCREAMING_SNAKE_CASE ( self : int) ->str:
'''simple docstring'''
A__ = self.all_model_classes[1]
A__ , A__ , A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs()
A__ = True
A__ = True
A__ = model_class(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.train()
A__ = model(UpperCAmelCase__ , mask_labels=UpperCAmelCase__ , class_labels=UpperCAmelCase__)
A__ = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
A__ = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
A__ = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
A__ = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=UpperCAmelCase__)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(pixel_decoder_hidden_states.grad)
self.assertIsNotNone(transformer_decoder_hidden_states.grad)
self.assertIsNotNone(attentions.grad)
_lowerCamelCase : Tuple = 1E-4
def SCREAMING_SNAKE_CASE ( ) -> int:
"""simple docstring"""
A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE ( self : Tuple) ->List[str]:
'''simple docstring'''
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''')
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Optional[int]:
'''simple docstring'''
A__ = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''').to(UpperCAmelCase__)
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(UpperCAmelCase__ , return_tensors='''pt''').to(UpperCAmelCase__)
A__ = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(UpperCAmelCase__ , (1, 3, 800, 1_088))
with torch.no_grad():
A__ = model(**UpperCAmelCase__)
A__ = torch.tensor(
[[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]).to(UpperCAmelCase__)
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , UpperCAmelCase__ , atol=UpperCAmelCase__))
A__ = torch.tensor(
[[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]).to(UpperCAmelCase__)
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , UpperCAmelCase__ , atol=UpperCAmelCase__))
A__ = torch.tensor(
[[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]).to(UpperCAmelCase__)
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=UpperCAmelCase__))
def SCREAMING_SNAKE_CASE ( self : Any) ->Optional[Any]:
'''simple docstring'''
A__ = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
.to(UpperCAmelCase__)
.eval()
)
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(UpperCAmelCase__ , return_tensors='''pt''').to(UpperCAmelCase__)
A__ = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(UpperCAmelCase__ , (1, 3, 800, 1_088))
with torch.no_grad():
A__ = model(**UpperCAmelCase__)
# masks_queries_logits
A__ = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
A__ = [
[-1.3737124, -1.7724937, -1.9364233],
[-1.5977281, -1.9867939, -2.1523695],
[-1.5795398, -1.9269832, -2.093942],
]
A__ = torch.tensor(UpperCAmelCase__).to(UpperCAmelCase__)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , UpperCAmelCase__ , atol=UpperCAmelCase__))
# class_queries_logits
A__ = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
A__ = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
]).to(UpperCAmelCase__)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , UpperCAmelCase__ , atol=UpperCAmelCase__))
def SCREAMING_SNAKE_CASE ( self : str) ->Optional[Any]:
'''simple docstring'''
A__ = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''')
.to(UpperCAmelCase__)
.eval()
)
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(UpperCAmelCase__ , return_tensors='''pt''').to(UpperCAmelCase__)
A__ = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(UpperCAmelCase__ , (1, 3, 800, 1_088))
with torch.no_grad():
A__ = model(**UpperCAmelCase__)
# masks_queries_logits
A__ = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
A__ = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
A__ = torch.tensor(UpperCAmelCase__).to(UpperCAmelCase__)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , UpperCAmelCase__ , atol=UpperCAmelCase__))
# class_queries_logits
A__ = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
A__ = torch.tensor(
[[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]).to(UpperCAmelCase__)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , UpperCAmelCase__ , atol=UpperCAmelCase__))
def SCREAMING_SNAKE_CASE ( self : Dict) ->Dict:
'''simple docstring'''
A__ = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
.to(UpperCAmelCase__)
.eval()
)
A__ = self.default_image_processor
A__ = image_processor(
[np.zeros((3, 800, 1_333)), np.zeros((3, 800, 1_333))] , segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)] , return_tensors='''pt''' , )
A__ = inputs['''pixel_values'''].to(UpperCAmelCase__)
A__ = [el.to(UpperCAmelCase__) for el in inputs['''mask_labels''']]
A__ = [el.to(UpperCAmelCase__) for el in inputs['''class_labels''']]
with torch.no_grad():
A__ = model(**UpperCAmelCase__)
self.assertTrue(outputs.loss is not None)
| 177
| 1
|
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''dpt'''
def __init__( self , lowerCamelCase=7_68 , lowerCamelCase=12 , lowerCamelCase=12 , lowerCamelCase=30_72 , lowerCamelCase="gelu" , lowerCamelCase=0.0 , lowerCamelCase=0.0 , lowerCamelCase=0.02 , lowerCamelCase=1e-12 , lowerCamelCase=3_84 , lowerCamelCase=16 , lowerCamelCase=3 , lowerCamelCase=False , lowerCamelCase=True , lowerCamelCase=[2, 5, 8, 11] , lowerCamelCase="project" , lowerCamelCase=[4, 2, 1, 0.5] , lowerCamelCase=[96, 1_92, 3_84, 7_68] , lowerCamelCase=2_56 , lowerCamelCase=-1 , lowerCamelCase=False , lowerCamelCase=True , lowerCamelCase=0.4 , lowerCamelCase=2_55 , lowerCamelCase=0.1 , lowerCamelCase=[1, 10_24, 24, 24] , lowerCamelCase=[0, 1] , lowerCamelCase=None , **lowerCamelCase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**lowerCamelCase )
UpperCamelCase : Optional[int] = hidden_size
UpperCamelCase : Union[str, Any] = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("Initializing the config with a `BiT` backbone." )
UpperCamelCase : Optional[Any] = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
}
UpperCamelCase : str = BitConfig(**lowerCamelCase )
elif isinstance(lowerCamelCase , lowerCamelCase ):
logger.info("Initializing the config with a `BiT` backbone." )
UpperCamelCase : List[Any] = BitConfig(**lowerCamelCase )
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCamelCase : str = backbone_config
else:
raise ValueError(
f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
UpperCamelCase : Union[str, Any] = backbone_featmap_shape
UpperCamelCase : str = neck_ignore_stages
if readout_type != "project":
raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode." )
else:
UpperCamelCase : Tuple = None
UpperCamelCase : Any = None
UpperCamelCase : Tuple = []
UpperCamelCase : str = num_hidden_layers
UpperCamelCase : Dict = num_attention_heads
UpperCamelCase : Optional[int] = intermediate_size
UpperCamelCase : Any = hidden_act
UpperCamelCase : Any = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : Any = initializer_range
UpperCamelCase : str = layer_norm_eps
UpperCamelCase : List[Any] = image_size
UpperCamelCase : int = patch_size
UpperCamelCase : Any = num_channels
UpperCamelCase : int = qkv_bias
UpperCamelCase : Optional[int] = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']" )
UpperCamelCase : Union[str, Any] = readout_type
UpperCamelCase : str = reassemble_factors
UpperCamelCase : Any = neck_hidden_sizes
UpperCamelCase : Dict = fusion_hidden_size
UpperCamelCase : Any = head_in_index
UpperCamelCase : Any = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
UpperCamelCase : Union[str, Any] = use_auxiliary_head
UpperCamelCase : str = auxiliary_loss_weight
UpperCamelCase : Dict = semantic_loss_ignore_index
UpperCamelCase : Optional[Any] = semantic_classifier_dropout
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase : List[str] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCamelCase : List[Any] = self.backbone_config.to_dict()
UpperCamelCase : int = self.__class__.model_type
return output
| 173
|
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def A__ ( A : Optional[int]):
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def A__ ( ):
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def A__ ( ):
'''simple docstring'''
UpperCamelCase : Tuple = "mock-s3-bucket"
UpperCamelCase : List[str] = F'''s3://{mock_bucket}'''
UpperCamelCase : Optional[Any] = extract_path_from_uri(A)
assert dataset_path.startswith("s3://") is False
UpperCamelCase : Any = "./local/path"
UpperCamelCase : str = extract_path_from_uri(A)
assert dataset_path == new_dataset_path
def A__ ( A : Optional[Any]):
'''simple docstring'''
UpperCamelCase : List[Any] = is_remote_filesystem(A)
assert is_remote is True
UpperCamelCase : Tuple = fsspec.filesystem("file")
UpperCamelCase : int = is_remote_filesystem(A)
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , A)
def A__ ( A : List[Any] , A : Any , A : str , A : Union[str, Any] , A : List[str] , A : List[Any] , A : Optional[Any]):
'''simple docstring'''
UpperCamelCase : List[str] = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
UpperCamelCase : Any = input_paths[compression_fs_class.protocol]
if input_path is None:
UpperCamelCase : Any = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(A)
UpperCamelCase : List[str] = fsspec.filesystem(compression_fs_class.protocol , fo=A)
assert isinstance(A , A)
UpperCamelCase : List[Any] = os.path.basename(A)
UpperCamelCase : Union[str, Any] = expected_filename[: expected_filename.rindex(".")]
assert fs.glob("*") == [expected_filename]
with fs.open(A , "r" , encoding="utf-8") as f, open(A , encoding="utf-8") as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"])
def A__ ( A : Optional[int] , A : str , A : Optional[Any]):
'''simple docstring'''
UpperCamelCase : Any = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
UpperCamelCase : str = compressed_file_paths[protocol]
UpperCamelCase : Optional[int] = "dataset.jsonl"
UpperCamelCase : Tuple = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
UpperCamelCase , *UpperCamelCase : Dict = fsspec.get_fs_token_paths(A)
assert fs.isfile(A)
assert not fs.isfile("non_existing_" + member_file_path)
@pytest.mark.integration
def A__ ( A : Dict , A : List[str] , A : Dict , A : List[Any]):
'''simple docstring'''
UpperCamelCase : Optional[int] = hf_api.dataset_info(A , token=A)
UpperCamelCase : List[str] = HfFileSystem(repo_info=A , token=A)
assert sorted(hffs.glob("*")) == [".gitattributes", "data"]
assert hffs.isdir("data")
assert hffs.isfile(".gitattributes") and hffs.isfile("data/text_data.txt")
with open(A) as f:
assert hffs.open("data/text_data.txt" , "r").read() == f.read()
def A__ ( ):
'''simple docstring'''
UpperCamelCase : str = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(A , A , clobber=A)
with pytest.warns(A) as warning_info:
importlib.reload(datasets.filesystems)
assert len(A) == 1
assert (
str(warning_info[0].message)
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 173
| 1
|
'''simple docstring'''
from functools import reduce
lowerCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def SCREAMING_SNAKE_CASE( UpperCamelCase = N ) -> int:
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda UpperCamelCase ,UpperCamelCase : str(int(UpperCamelCase ) * int(UpperCamelCase ) ) ,n[i : i + 1_3] ) )
for i in range(len(UpperCamelCase ) - 1_2 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 471
|
'''simple docstring'''
import math
class lowercase :
def __init__( self , _snake_case=0) -> Union[str, Any]: # a graph with Node 0,1,...,N-1
UpperCAmelCase_ : Tuple = n
UpperCAmelCase_ : Optional[Any] = [
[math.inf for j in range(0 , _snake_case)] for i in range(0 , _snake_case)
] # adjacency matrix for weight
UpperCAmelCase_ : Tuple = [
[math.inf for j in range(0 , _snake_case)] for i in range(0 , _snake_case)
] # dp[i][j] stores minimum distance from i to j
def _snake_case ( self , _snake_case , _snake_case , _snake_case) -> List[Any]:
UpperCAmelCase_ : Union[str, Any] = w
def _snake_case ( self) -> str:
for k in range(0 , self.n):
for i in range(0 , self.n):
for j in range(0 , self.n):
UpperCAmelCase_ : Optional[int] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j])
def _snake_case ( self , _snake_case , _snake_case) -> str:
return self.dp[u][v]
if __name__ == "__main__":
lowerCAmelCase__ = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 471
| 1
|
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'BlipImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self : Optional[int] , a_ : Union[str, Any] , a_ : List[Any] )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = False
super().__init__(a_ , a_ )
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processor
def __call__( self : List[Any] , a_ : ImageInput = None , a_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , a_ : bool = True , a_ : Union[bool, str, PaddingStrategy] = False , a_ : Union[bool, str, TruncationStrategy] = None , a_ : Optional[int] = None , a_ : int = 0 , a_ : Optional[int] = None , a_ : Optional[bool] = None , a_ : bool = False , a_ : bool = False , a_ : bool = False , a_ : bool = False , a_ : bool = False , a_ : bool = True , a_ : Optional[Union[str, TensorType]] = None , **a_ : int , )-> BatchEncoding:
"""simple docstring"""
if images is None and text is None:
raise ValueError('You have to specify either images or text.' )
# Get only text
if images is None:
SCREAMING_SNAKE_CASE__ : int = self.tokenizer
SCREAMING_SNAKE_CASE__ : Dict = 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_ , )
return text_encoding
# add pixel_values
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processor(a_ , return_tensors=a_ )
if text is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 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_ , )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
if text_encoding is not None:
encoding_image_processor.update(a_ )
return encoding_image_processor
def __lowercase( self : Any , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : List[Any] , *a_ : Any , **a_ : Dict )-> int:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 85
|
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase , json={'''text''': message_body} , headers=UpperCamelCase )
if response.status_code != 200:
_a = (
'''Request to slack returned an error '''
f'{response.status_code}, the response is:\n{response.text}'
)
raise ValueError(UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 22
| 0
|
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowerCamelCase__ = random.Random()
def UpperCamelCase ( snake_case__ : List[Any] ,snake_case__ : int=1.0 ,snake_case__ : Any=None ,snake_case__ : Optional[Any]=None ):
'''simple docstring'''
if rng is None:
__snake_case :Optional[int] = global_rng
__snake_case :int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class snake_case__ ( unittest.TestCase):
'''simple docstring'''
def __init__( self , a__ , a__=7 , a__=4_00 , a__=20_00 , a__=1 , a__=0.0 , a__=1_60_00 , a__=True , a__=True , ) -> Dict:
'''simple docstring'''
__snake_case :Any = parent
__snake_case :Optional[int] = batch_size
__snake_case :int = min_seq_length
__snake_case :Tuple = max_seq_length
__snake_case :List[str] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__snake_case :List[str] = feature_size
__snake_case :Optional[Any] = padding_value
__snake_case :List[str] = sampling_rate
__snake_case :Optional[int] = return_attention_mask
__snake_case :Dict = do_normalize
def __lowercase ( self ) -> Dict:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def __lowercase ( self , a__=False , a__=False ) -> List[Any]:
'''simple docstring'''
def _flatten(a__ ):
return list(itertools.chain(*a__ ) )
if equal_length:
__snake_case :Dict = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__snake_case :Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__snake_case :Union[str, Any] = [np.asarray(a__ ) for x in speech_inputs]
return speech_inputs
class snake_case__ ( __lowercase , unittest.TestCase):
'''simple docstring'''
lowerCamelCase : Tuple = WavaVecaFeatureExtractor
def __lowercase ( self ) -> Optional[int]:
'''simple docstring'''
__snake_case :str = WavaVecaFeatureExtractionTester(self )
def __lowercase ( self , a__ ) -> str:
'''simple docstring'''
self.assertTrue(np.all(np.mean(a__ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a__ , axis=0 ) - 1 ) < 1e-3 ) )
def __lowercase ( self ) -> int:
'''simple docstring'''
__snake_case :Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__snake_case :int = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
__snake_case :Tuple = [np.asarray(a__ ) for speech_input in speech_inputs]
# Test not batched input
__snake_case :int = feat_extract(speech_inputs[0] , return_tensors="""np""" ).input_values
__snake_case :Dict = feat_extract(np_speech_inputs[0] , return_tensors="""np""" ).input_values
self.assertTrue(np.allclose(a__ , a__ , atol=1e-3 ) )
# Test batched
__snake_case :Union[str, Any] = feat_extract(a__ , return_tensors="""np""" ).input_values
__snake_case :Optional[Any] = feat_extract(a__ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(a__ , a__ ):
self.assertTrue(np.allclose(a__ , a__ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
__snake_case :List[str] = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
__snake_case :Tuple = np.asarray(a__ )
__snake_case :List[Any] = feat_extract(a__ , return_tensors="""np""" ).input_values
__snake_case :Union[str, Any] = feat_extract(a__ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(a__ , a__ ):
self.assertTrue(np.allclose(a__ , a__ , atol=1e-3 ) )
def __lowercase ( self ) -> int:
'''simple docstring'''
__snake_case :Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case :Tuple = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
__snake_case :Dict = ["longest", "max_length", "do_not_pad"]
__snake_case :List[str] = [None, 16_00, None]
for max_length, padding in zip(a__ , a__ ):
__snake_case :str = feat_extract(a__ , padding=a__ , max_length=a__ , return_tensors="""np""" )
__snake_case :Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00] )
self.assertTrue(input_values[0][8_00:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:10_00] )
self.assertTrue(input_values[0][10_00:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:12_00] )
def __lowercase ( self ) -> Any:
'''simple docstring'''
__snake_case :Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case :Any = range(8_00 , 14_00 , 2_00 )
__snake_case :int = [floats_list((1, x) )[0] for x in lengths]
__snake_case :Any = ["longest", "max_length", "do_not_pad"]
__snake_case :Union[str, Any] = [None, 16_00, None]
for max_length, padding in zip(a__ , a__ ):
__snake_case :Union[str, Any] = feat_extract(a__ , max_length=a__ , padding=a__ )
__snake_case :Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00] )
self._check_zero_mean_unit_variance(input_values[1][:10_00] )
self._check_zero_mean_unit_variance(input_values[2][:12_00] )
def __lowercase ( self ) -> Optional[int]:
'''simple docstring'''
__snake_case :int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case :List[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
__snake_case :Dict = feat_extract(
a__ , truncation=a__ , max_length=10_00 , padding="""max_length""" , return_tensors="""np""" )
__snake_case :Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __lowercase ( self ) -> str:
'''simple docstring'''
__snake_case :str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case :Tuple = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
__snake_case :List[str] = feat_extract(
a__ , truncation=a__ , max_length=10_00 , padding="""longest""" , return_tensors="""np""" )
__snake_case :List[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00] )
self._check_zero_mean_unit_variance(input_values[1, :10_00] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 10_00) )
__snake_case :Optional[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
__snake_case :Any = feat_extract(
a__ , truncation=a__ , max_length=20_00 , padding="""longest""" , return_tensors="""np""" )
__snake_case :Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00] )
self._check_zero_mean_unit_variance(input_values[1, :10_00] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 12_00) )
@require_torch
def __lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
import torch
__snake_case :Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case :Optional[Any] = np.random.rand(1_00 ).astype(np.floataa )
__snake_case :Optional[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__snake_case :Optional[Any] = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__snake_case :Dict = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def __lowercase ( self ) -> Any:
'''simple docstring'''
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
__snake_case :List[Any] = WavaVecaConfig.from_pretrained(a__ )
__snake_case :Tuple = WavaVecaFeatureExtractor.from_pretrained(a__ )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == """layer""" )
| 706
|
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCamelCase ( snake_case__ : str ,snake_case__ : Dict ,snake_case__ : List[str] ):
'''simple docstring'''
__snake_case :Tuple = 1.5
__snake_case :Any = int(factor * num_class_images )
__snake_case :List[str] = ClipClient(
url="""https://knn.laion.ai/knn-service""" ,indice_name="""laion_400m""" ,num_images=snake_case__ ,aesthetic_weight=0.1 )
os.makedirs(f'''{class_data_dir}/images''' ,exist_ok=snake_case__ )
if len(list(Path(f'''{class_data_dir}/images''' ).iterdir() ) ) >= num_class_images:
return
while True:
__snake_case :Optional[Any] = client.query(text=snake_case__ )
if len(snake_case__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
__snake_case :Tuple = int(factor * num_images )
__snake_case :Any = ClipClient(
url="""https://knn.laion.ai/knn-service""" ,indice_name="""laion_400m""" ,num_images=snake_case__ ,aesthetic_weight=0.1 ,)
__snake_case :Dict = 0
__snake_case :Tuple = 0
__snake_case :Dict = tqdm(desc="""downloading real regularization images""" ,total=snake_case__ )
with open(f'''{class_data_dir}/caption.txt''' ,"""w""" ) as fa, open(f'''{class_data_dir}/urls.txt''' ,"""w""" ) as fa, open(
f'''{class_data_dir}/images.txt''' ,"""w""" ) as fa:
while total < num_class_images:
__snake_case :List[Any] = class_images[count]
count += 1
try:
__snake_case :str = requests.get(images["""url"""] )
if img.status_code == 200:
__snake_case :Any = Image.open(BytesIO(img.content ) )
with open(f'''{class_data_dir}/images/{total}.jpg''' ,"""wb""" ) as f:
f.write(img.content )
fa.write(images["""caption"""] + """\n""" )
fa.write(images["""url"""] + """\n""" )
fa.write(f'''{class_data_dir}/images/{total}.jpg''' + """\n""" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def UpperCamelCase ( ):
'''simple docstring'''
__snake_case :List[str] = argparse.ArgumentParser("""""" ,add_help=snake_case__ )
parser.add_argument("""--class_prompt""" ,help="""text prompt to retrieve images""" ,required=snake_case__ ,type=snake_case__ )
parser.add_argument("""--class_data_dir""" ,help="""path to save images""" ,required=snake_case__ ,type=snake_case__ )
parser.add_argument("""--num_class_images""" ,help="""number of images to download""" ,default=200 ,type=snake_case__ )
return parser.parse_args()
if __name__ == "__main__":
lowerCamelCase__ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 291
| 0
|
"""simple docstring"""
import unittest
import numpy as np
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
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=18 , SCREAMING_SNAKE_CASE__=30 , SCREAMING_SNAKE_CASE__=400 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , SCREAMING_SNAKE_CASE__=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , SCREAMING_SNAKE_CASE__=True , ) -> List[Any]:
A__ = size if size is not None else {"height": 224, "width": 224}
A__ = crop_size if crop_size is not None else {"height": 18, "width": 18}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = image_size
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_center_crop
A__ = crop_size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_convert_rgb
def snake_case__ ( self ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def snake_case__ ( self , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False ) -> Tuple:
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
A__ = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
A__ = []
for i in range(self.batch_size ):
A__ , A__ = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
A__ = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
A__ = [torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class UpperCamelCase__ ( _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
A__ : List[Any] = ChineseCLIPImageProcessor if is_vision_available() else None
def snake_case__ ( self ) -> str:
A__ = ChineseCLIPImageProcessingTester(self , do_center_crop=SCREAMING_SNAKE_CASE__ )
@property
def snake_case__ ( self ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self ) -> Tuple:
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_resize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "size" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_normalize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_mean" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_std" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_convert_rgb" ) )
def snake_case__ ( self ) -> List[str]:
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 224, "width": 224} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
A__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def snake_case__ ( self ) -> str:
pass
def snake_case__ ( self ) -> str:
# Initialize image_processing
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
A__ = 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(SCREAMING_SNAKE_CASE__ , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def snake_case__ ( self ) -> List[str]:
# Initialize image_processing
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , numpify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , np.ndarray )
# Test not batched input
A__ = 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(SCREAMING_SNAKE_CASE__ , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def snake_case__ ( self ) -> int:
# Initialize image_processing
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ , torchify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , torch.Tensor )
# Test not batched input
A__ = 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(SCREAMING_SNAKE_CASE__ , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
@require_torch
@require_vision
class UpperCamelCase__ ( _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
A__ : str = ChineseCLIPImageProcessor if is_vision_available() else None
def snake_case__ ( self ) -> Dict:
A__ = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=SCREAMING_SNAKE_CASE__ )
A__ = 3
@property
def snake_case__ ( self ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self ) -> Dict:
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_resize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "size" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "center_crop" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_normalize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_mean" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "image_std" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_convert_rgb" ) )
def snake_case__ ( self ) -> Dict:
pass
def snake_case__ ( self ) -> Optional[Any]:
# Initialize image_processing
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 104
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
"Pix2StructVisionConfig",
],
"processing_pix2struct": ["Pix2StructProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ["Pix2StructImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Pix2StructPreTrainedModel",
"Pix2StructForConditionalGeneration",
"Pix2StructVisionModel",
"Pix2StructTextModel",
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 518
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
a : Any = {
"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 UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = "tapas"
def __init__( self , snake_case=3_0_5_2_2 , snake_case=7_6_8 , snake_case=1_2 , snake_case=1_2 , snake_case=3_0_7_2 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=1_0_2_4 , snake_case=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0] , snake_case=0.02 , snake_case=1e-12 , snake_case=0 , snake_case=10.0 , snake_case=0 , snake_case=1.0 , snake_case=None , snake_case=1.0 , snake_case=False , snake_case=None , snake_case=1.0 , snake_case=1.0 , snake_case=False , snake_case=False , snake_case="ratio" , snake_case=None , snake_case=None , snake_case=6_4 , snake_case=3_2 , snake_case=False , snake_case=True , snake_case=False , snake_case=False , snake_case=True , snake_case=False , snake_case=None , snake_case=None , **snake_case , ):
'''simple docstring'''
super().__init__(pad_token_id=snake_case , **snake_case )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
UpperCAmelCase : List[str] = vocab_size
UpperCAmelCase : Dict = hidden_size
UpperCAmelCase : List[Any] = num_hidden_layers
UpperCAmelCase : str = num_attention_heads
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : Any = max_position_embeddings
UpperCAmelCase : int = type_vocab_sizes
UpperCAmelCase : int = initializer_range
UpperCAmelCase : Dict = layer_norm_eps
# Fine-tuning task hyperparameters
UpperCAmelCase : List[str] = positive_label_weight
UpperCAmelCase : Any = num_aggregation_labels
UpperCAmelCase : Any = aggregation_loss_weight
UpperCAmelCase : Any = use_answer_as_supervision
UpperCAmelCase : Optional[int] = answer_loss_importance
UpperCAmelCase : int = use_normalized_answer_loss
UpperCAmelCase : Any = huber_loss_delta
UpperCAmelCase : str = temperature
UpperCAmelCase : Optional[int] = aggregation_temperature
UpperCAmelCase : int = use_gumbel_for_cells
UpperCAmelCase : Optional[Any] = use_gumbel_for_aggregation
UpperCAmelCase : List[str] = average_approximation_function
UpperCAmelCase : Optional[Any] = cell_selection_preference
UpperCAmelCase : Any = answer_loss_cutoff
UpperCAmelCase : Union[str, Any] = max_num_rows
UpperCAmelCase : Optional[int] = max_num_columns
UpperCAmelCase : Dict = average_logits_per_cell
UpperCAmelCase : List[str] = select_one_column
UpperCAmelCase : Tuple = allow_empty_column_selection
UpperCAmelCase : Tuple = init_cell_selection_weights_to_zero
UpperCAmelCase : List[str] = reset_position_index_per_cell
UpperCAmelCase : Any = disable_per_token_loss
# Aggregation hyperparameters
UpperCAmelCase : Optional[int] = aggregation_labels
UpperCAmelCase : Any = no_aggregation_label_index
if isinstance(self.aggregation_labels , snake_case ):
UpperCAmelCase : Union[str, Any] = {int(snake_case ): v for k, v in aggregation_labels.items()}
| 702
|
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
UpperCAmelCase : Dict = SamImageProcessor()
UpperCAmelCase : Tuple = SamProcessor(snake_case )
processor.save_pretrained(self.tmpdirname )
def A_ ( self , **snake_case ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case ).image_processor
def A_ ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Tuple = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
UpperCAmelCase : Union[str, Any] = [Image.fromarray(np.moveaxis(snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : int = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase : Tuple = self.get_image_processor(do_normalize=snake_case , padding_value=1.0 )
UpperCAmelCase : str = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=snake_case , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Dict = self.get_image_processor()
UpperCAmelCase : Tuple = SamProcessor(image_processor=snake_case )
UpperCAmelCase : str = self.prepare_image_inputs()
UpperCAmelCase : Dict = image_processor(snake_case , return_tensors="np" )
UpperCAmelCase : str = processor(images=snake_case , return_tensors="np" )
input_feat_extract.pop("original_sizes" ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop("reshaped_input_sizes" ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
@require_torch
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Dict = self.get_image_processor()
UpperCAmelCase : Dict = SamProcessor(image_processor=snake_case )
UpperCAmelCase : Tuple = [torch.ones((1, 3, 5, 5) )]
UpperCAmelCase : int = [[1_7_6_4, 2_6_4_6]]
UpperCAmelCase : Optional[int] = [[6_8_3, 1_0_2_4]]
UpperCAmelCase : str = processor.post_process_masks(snake_case , snake_case , snake_case )
self.assertEqual(masks[0].shape , (1, 3, 1_7_6_4, 2_6_4_6) )
UpperCAmelCase : Union[str, Any] = processor.post_process_masks(
snake_case , torch.tensor(snake_case ) , torch.tensor(snake_case ) )
self.assertEqual(masks[0].shape , (1, 3, 1_7_6_4, 2_6_4_6) )
# should also work with np
UpperCAmelCase : Optional[int] = [np.ones((1, 3, 5, 5) )]
UpperCAmelCase : Union[str, Any] = processor.post_process_masks(snake_case , np.array(snake_case ) , np.array(snake_case ) )
self.assertEqual(masks[0].shape , (1, 3, 1_7_6_4, 2_6_4_6) )
UpperCAmelCase : List[str] = [[1, 0], [0, 1]]
with self.assertRaises(snake_case ):
UpperCAmelCase : str = processor.post_process_masks(snake_case , np.array(snake_case ) , np.array(snake_case ) )
@require_vision
@require_tf
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Dict = tempfile.mkdtemp()
UpperCAmelCase : Optional[int] = SamImageProcessor()
UpperCAmelCase : str = SamProcessor(snake_case )
processor.save_pretrained(self.tmpdirname )
def A_ ( self , **snake_case ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case ).image_processor
def A_ ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Tuple = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
UpperCAmelCase : Any = [Image.fromarray(np.moveaxis(snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase : Optional[Any] = self.get_image_processor(do_normalize=snake_case , padding_value=1.0 )
UpperCAmelCase : str = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=snake_case , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : List[Any] = self.get_image_processor()
UpperCAmelCase : Union[str, Any] = SamProcessor(image_processor=snake_case )
UpperCAmelCase : List[str] = self.prepare_image_inputs()
UpperCAmelCase : Union[str, Any] = image_processor(snake_case , return_tensors="np" )
UpperCAmelCase : Any = processor(images=snake_case , return_tensors="np" )
input_feat_extract.pop("original_sizes" ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop("reshaped_input_sizes" ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
@require_tf
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = self.get_image_processor()
UpperCAmelCase : List[Any] = SamProcessor(image_processor=snake_case )
UpperCAmelCase : Union[str, Any] = [tf.ones((1, 3, 5, 5) )]
UpperCAmelCase : Optional[int] = [[1_7_6_4, 2_6_4_6]]
UpperCAmelCase : int = [[6_8_3, 1_0_2_4]]
UpperCAmelCase : str = processor.post_process_masks(snake_case , snake_case , snake_case , return_tensors="tf" )
self.assertEqual(masks[0].shape , (1, 3, 1_7_6_4, 2_6_4_6) )
UpperCAmelCase : int = processor.post_process_masks(
snake_case , tf.convert_to_tensor(snake_case ) , tf.convert_to_tensor(snake_case ) , return_tensors="tf" , )
self.assertEqual(masks[0].shape , (1, 3, 1_7_6_4, 2_6_4_6) )
# should also work with np
UpperCAmelCase : Union[str, Any] = [np.ones((1, 3, 5, 5) )]
UpperCAmelCase : Tuple = processor.post_process_masks(
snake_case , np.array(snake_case ) , np.array(snake_case ) , return_tensors="tf" )
self.assertEqual(masks[0].shape , (1, 3, 1_7_6_4, 2_6_4_6) )
UpperCAmelCase : Union[str, Any] = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
UpperCAmelCase : Any = processor.post_process_masks(
snake_case , np.array(snake_case ) , np.array(snake_case ) , return_tensors="tf" )
@require_vision
@require_torchvision
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Tuple = tempfile.mkdtemp()
UpperCAmelCase : Tuple = SamImageProcessor()
UpperCAmelCase : Optional[int] = SamProcessor(snake_case )
processor.save_pretrained(self.tmpdirname )
def A_ ( self , **snake_case ):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case ).image_processor
def A_ ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Any = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
UpperCAmelCase : int = [Image.fromarray(np.moveaxis(snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Tuple = self.get_image_processor()
UpperCAmelCase : int = SamProcessor(image_processor=snake_case )
UpperCAmelCase : int = np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa )
UpperCAmelCase : Tuple = [tf.convert_to_tensor(snake_case )]
UpperCAmelCase : Dict = [torch.tensor(snake_case )]
UpperCAmelCase : List[Any] = [[1_7_6_4, 2_6_4_6]]
UpperCAmelCase : List[Any] = [[6_8_3, 1_0_2_4]]
UpperCAmelCase : Union[str, Any] = processor.post_process_masks(
snake_case , snake_case , snake_case , return_tensors="tf" )
UpperCAmelCase : int = processor.post_process_masks(
snake_case , snake_case , snake_case , return_tensors="pt" )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : List[Any] = self.get_image_processor()
UpperCAmelCase : str = SamProcessor(image_processor=snake_case )
UpperCAmelCase : int = self.prepare_image_inputs()
UpperCAmelCase : Optional[Any] = image_processor(snake_case , return_tensors="pt" )["pixel_values"].numpy()
UpperCAmelCase : Optional[int] = processor(images=snake_case , return_tensors="pt" )["pixel_values"].numpy()
UpperCAmelCase : Optional[Any] = image_processor(snake_case , return_tensors="tf" )["pixel_values"].numpy()
UpperCAmelCase : Union[str, Any] = processor(images=snake_case , return_tensors="tf" )["pixel_values"].numpy()
self.assertTrue(np.allclose(snake_case , snake_case ) )
self.assertTrue(np.allclose(snake_case , snake_case ) )
self.assertTrue(np.allclose(snake_case , snake_case ) )
| 609
| 0
|
from __future__ import annotations
from collections import Counter
from random import random
class UpperCAmelCase :
def __init__(self : int ) -> Dict:
'''simple docstring'''
snake_case : Union[str, Any] = {}
def _SCREAMING_SNAKE_CASE (self : List[Any] , snake_case__ : str ) -> None:
'''simple docstring'''
snake_case : Tuple = {}
def _SCREAMING_SNAKE_CASE (self : Any , snake_case__ : str , snake_case__ : str , snake_case__ : float ) -> None:
'''simple docstring'''
if nodea not in self.connections:
self.add_node(snake_case__ )
if nodea not in self.connections:
self.add_node(snake_case__ )
snake_case : str = probability
def _SCREAMING_SNAKE_CASE (self : str ) -> list[str]:
'''simple docstring'''
return list(self.connections )
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : str ) -> str:
'''simple docstring'''
snake_case : int = 0
snake_case : Dict = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def UpperCamelCase ( __lowerCamelCase : str , __lowerCamelCase : list[tuple[str, str, float]] , __lowerCamelCase : int ):
snake_case : int = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
snake_case : Dict = Counter(graph.get_nodes() )
snake_case : Tuple = start
for _ in range(__lowerCamelCase ):
snake_case : Dict = graph.transition(__lowerCamelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 204
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
"""kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
"""kssteven/ibert-roberta-large-mnli""": (
"""https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
),
}
class UpperCAmelCase ( A_ ):
A__ : Optional[int] = "ibert"
def __init__(self : List[Any] , snake_case__ : int=3_05_22 , snake_case__ : int=7_68 , snake_case__ : Any=12 , snake_case__ : str=12 , snake_case__ : Optional[int]=30_72 , snake_case__ : Union[str, Any]="gelu" , snake_case__ : Any=0.1 , snake_case__ : List[Any]=0.1 , snake_case__ : str=5_12 , snake_case__ : Optional[int]=2 , snake_case__ : Any=0.02 , snake_case__ : List[Any]=1e-12 , snake_case__ : Optional[Any]=1 , snake_case__ : List[Any]=0 , snake_case__ : str=2 , snake_case__ : Tuple="absolute" , snake_case__ : List[str]=False , snake_case__ : Tuple="none" , **snake_case__ : Tuple , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
snake_case : str = vocab_size
snake_case : Tuple = hidden_size
snake_case : Optional[Any] = num_hidden_layers
snake_case : Optional[int] = num_attention_heads
snake_case : List[Any] = hidden_act
snake_case : Optional[Any] = intermediate_size
snake_case : Dict = hidden_dropout_prob
snake_case : List[str] = attention_probs_dropout_prob
snake_case : List[str] = max_position_embeddings
snake_case : Union[str, Any] = type_vocab_size
snake_case : Optional[int] = initializer_range
snake_case : Optional[int] = layer_norm_eps
snake_case : int = position_embedding_type
snake_case : Union[str, Any] = quant_mode
snake_case : Dict = force_dequant
class UpperCAmelCase ( A_ ):
@property
def _SCREAMING_SNAKE_CASE (self : Tuple ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 204
| 1
|
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCAmelCase( __lowerCamelCase ):
return EnvironmentCommand()
class a__ ( __snake_case ):
@staticmethod
def __SCREAMING_SNAKE_CASE ( UpperCAmelCase ) -> str:
__a = parser.add_parser('env' )
download_parser.set_defaults(func=UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
__a = huggingface_hub.__version__
__a = 'not installed'
__a = 'NA'
if is_torch_available():
import torch
__a = torch.__version__
__a = torch.cuda.is_available()
__a = 'not installed'
if is_transformers_available():
import transformers
__a = transformers.__version__
__a = 'not installed'
if is_accelerate_available():
import accelerate
__a = accelerate.__version__
__a = 'not installed'
if is_xformers_available():
import xformers
__a = xformers.__version__
__a = {
'`diffusers` version': version,
'Platform': platform.platform(),
'Python version': platform.python_version(),
'PyTorch version (GPU?)': f'''{pt_version} ({pt_cuda_available})''',
'Huggingface_hub version': hub_version,
'Transformers version': transformers_version,
'Accelerate version': accelerate_version,
'xFormers version': xformers_version,
'Using GPU in script?': '<fill in>',
'Using distributed or parallel set-up in script?': '<fill in>',
}
print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n' )
print(self.format_dict(UpperCAmelCase ) )
return info
@staticmethod
def __SCREAMING_SNAKE_CASE ( UpperCAmelCase ) -> int:
return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
| 700
|
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase_ : Optional[int] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
lowerCamelCase_ : Union[str, Any] = 250_004
lowerCamelCase_ : List[str] = 250_020
@require_sentencepiece
@require_tokenizers
class a__ ( __snake_case , unittest.TestCase ):
A__ : Optional[Any] = MBartTokenizer
A__ : Optional[Any] = MBartTokenizerFast
A__ : Dict = True
A__ : Dict = True
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
__a = MBartTokenizer(UpperCAmelCase , keep_accents=UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
__a = MBartTokenizer(UpperCAmelCase , keep_accents=UpperCAmelCase )
__a = tokenizer.tokenize('This is a test' )
self.assertListEqual(UpperCAmelCase , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
__a = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
UpperCAmelCase , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a = tokenizer.convert_tokens_to_ids(UpperCAmelCase )
self.assertListEqual(
UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__a = tokenizer.convert_ids_to_tokens(UpperCAmelCase )
self.assertListEqual(
UpperCAmelCase , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__a = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__a = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase , **UpperCAmelCase )
__a = self.tokenizer_class.from_pretrained(UpperCAmelCase , **UpperCAmelCase )
__a = tempfile.mkdtemp()
__a = tokenizer_r.save_pretrained(UpperCAmelCase )
__a = tokenizer_p.save_pretrained(UpperCAmelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__a = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(UpperCAmelCase , UpperCAmelCase )
# Checks everything loads correctly in the same way
__a = tokenizer_r.from_pretrained(UpperCAmelCase )
__a = tokenizer_p.from_pretrained(UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase , UpperCAmelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCAmelCase )
# Save tokenizer rust, legacy_format=True
__a = tempfile.mkdtemp()
__a = tokenizer_r.save_pretrained(UpperCAmelCase , legacy_format=UpperCAmelCase )
__a = tokenizer_p.save_pretrained(UpperCAmelCase )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase , UpperCAmelCase )
# Checks everything loads correctly in the same way
__a = tokenizer_r.from_pretrained(UpperCAmelCase )
__a = tokenizer_p.from_pretrained(UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase , UpperCAmelCase ) )
shutil.rmtree(UpperCAmelCase )
# Save tokenizer rust, legacy_format=False
__a = tempfile.mkdtemp()
__a = tokenizer_r.save_pretrained(UpperCAmelCase , legacy_format=UpperCAmelCase )
__a = tokenizer_p.save_pretrained(UpperCAmelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__a = tokenizer_r.from_pretrained(UpperCAmelCase )
__a = tokenizer_p.from_pretrained(UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase , UpperCAmelCase ) )
shutil.rmtree(UpperCAmelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
A__ : Optional[Any] = 'facebook/mbart-large-en-ro'
A__ : Union[str, Any] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
A__ : Tuple = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
A__ : Dict = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def __SCREAMING_SNAKE_CASE ( cls ) -> Optional[Any]:
__a = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
__a = 1
return cls
def __SCREAMING_SNAKE_CASE ( self ) -> str:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 2_5_0_0_2_0 )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
__a = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
self.assertIn(UpperCAmelCase , self.tokenizer.all_special_ids )
__a = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
__a = self.tokenizer.decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase )
__a = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase )
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
__a = ['this is gunna be a long sentence ' * 2_0]
assert isinstance(src_text[0] , UpperCAmelCase )
__a = 1_0
__a = self.tokenizer(UpperCAmelCase , max_length=UpperCAmelCase , truncation=UpperCAmelCase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , UpperCAmelCase )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
__a = tempfile.mkdtemp()
__a = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCAmelCase )
__a = MBartTokenizer.from_pretrained(UpperCAmelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCAmelCase )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
__a = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase , return_tensors='pt' )
__a = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
__a = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__a = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
__a = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
__a = self.tokenizer(self.src_text , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=3 , return_tensors='pt' )
__a = self.tokenizer(
text_target=self.tgt_text , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=1_0 , return_tensors='pt' )
__a = targets['input_ids']
__a = shift_tokens_right(UpperCAmelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
__a = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
# A, test, EOS, en_XX
'input_ids': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 2_5_0_0_0_1,
} , )
| 246
| 0
|
from __future__ import annotations
def __a ( A__ : list , A__ : int , A__ : int , A__ : int ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
SCREAMING_SNAKE_CASE = result + left + right
return input_list
def __a ( A__ : list ):
if len(A__ ) <= 1:
return input_list
SCREAMING_SNAKE_CASE = list(A__ )
# iteration for two-way merging
SCREAMING_SNAKE_CASE = 2
while p <= len(A__ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(A__ ) , A__ ):
SCREAMING_SNAKE_CASE = i
SCREAMING_SNAKE_CASE = i + p - 1
SCREAMING_SNAKE_CASE = (low + high + 1) // 2
SCREAMING_SNAKE_CASE = merge(A__ , A__ , A__ , A__ )
# final merge of last two parts
if p * 2 >= len(A__ ):
SCREAMING_SNAKE_CASE = i
SCREAMING_SNAKE_CASE = merge(A__ , 0 , A__ , len(A__ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
__A : Any = input('Enter numbers separated by a comma:\n').strip()
if user_input == "":
__A : Union[str, Any] = []
else:
__A : Optional[Any] = [int(item.strip()) for item in user_input.split(',')]
print(iter_merge_sort(unsorted))
| 16
|
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Any ):
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-t5"
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer("This is me" , return_tensors="pt" )
SCREAMING_SNAKE_CASE = model.to_bettertransformer()
self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
SCREAMING_SNAKE_CASE = model.generate(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.reverse_bettertransformer()
self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
self.assertFalse(
any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
SCREAMING_SNAKE_CASE = model_reloaded.generate(**__lowerCamelCase )
self.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase ) )
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-t5"
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(__lowerCamelCase ):
model.save_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.reverse_bettertransformer()
model.save_pretrained(__lowerCamelCase )
| 16
| 1
|
from math import factorial
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ):
A__ : Dict = real
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ : int = [1] * rank
else:
A__ : Optional[Any] = rank
def __repr__( self ):
return (
F"{self.real}+"
F"{'+'.join(str(UpperCamelCase__ )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}"
)
def __snake_case ( self ):
A__ : Optional[Any] = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , UpperCamelCase__ )
def __add__( self , UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
return Dual(self.real + other , self.duals )
A__ : int = self.duals.copy()
A__ : Dict = other.duals.copy()
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
o_dual.extend([1] * (len(UpperCamelCase__ ) - len(UpperCamelCase__ )) )
elif len(UpperCamelCase__ ) < len(UpperCamelCase__ ):
s_dual.extend([1] * (len(UpperCamelCase__ ) - len(UpperCamelCase__ )) )
A__ : Optional[Any] = []
for i in range(len(UpperCamelCase__ ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , UpperCamelCase__ )
_lowerCAmelCase = __add__
def __sub__( self , UpperCamelCase__ ):
return self + other * -1
def __mul__( self , UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ : int = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , UpperCamelCase__ )
A__ : Optional[int] = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , UpperCamelCase__ )
_lowerCAmelCase = __mul__
def __truediv__( self , UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ : Tuple = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , UpperCamelCase__ )
raise ValueError
def __floordiv__( self , UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ : Any = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , UpperCamelCase__ )
raise ValueError
def __pow__( self , UpperCamelCase__ ):
if n < 0 or isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''power must be a positive integer''' )
if n == 0:
return 1
if n == 1:
return self
A__ : Dict = self
for _ in range(n - 1 ):
x *= self
return x
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] , __UpperCamelCase : int , __UpperCamelCase : List[str] ) -> Dict:
"""simple docstring"""
if not callable(__UpperCamelCase ):
raise ValueError('''differentiate() requires a function as input for func''' )
if not isinstance(__UpperCamelCase , (float, int) ):
raise ValueError('''differentiate() requires a float as input for position''' )
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError('''differentiate() requires an int as input for order''' )
A__ : Dict = Dual(__UpperCamelCase , 1 )
A__ : Any = func(__UpperCamelCase )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
return y**2 * y**4
print(differentiate(f, 9, 2))
| 55
|
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
# TODO Update this
_SCREAMING_SNAKE_CASE : Optional[int] = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
_lowerCAmelCase = "esm"
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=1026 , UpperCamelCase__=0.0_2 , UpperCamelCase__=1e-12 , UpperCamelCase__="absolute" , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ , ):
super().__init__(pad_token_id=UpperCamelCase__ , mask_token_id=UpperCamelCase__ , **UpperCamelCase__ )
A__ : Optional[Any] = vocab_size
A__ : int = hidden_size
A__ : List[str] = num_hidden_layers
A__ : Tuple = num_attention_heads
A__ : str = intermediate_size
A__ : List[str] = hidden_dropout_prob
A__ : Optional[Any] = attention_probs_dropout_prob
A__ : int = max_position_embeddings
A__ : List[str] = initializer_range
A__ : List[Any] = layer_norm_eps
A__ : int = position_embedding_type
A__ : Optional[Any] = use_cache
A__ : Optional[int] = emb_layer_norm_before
A__ : List[str] = token_dropout
A__ : Tuple = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
A__ : List[Any] = EsmFoldConfig()
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ : Optional[int] = EsmFoldConfig(**UpperCamelCase__ )
A__ : int = esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
A__ : Any = get_default_vocab_list()
else:
A__ : Dict = vocab_list
else:
A__ : Optional[Any] = None
A__ : Tuple = None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCamelCase__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def __snake_case ( self ):
A__ : Optional[int] = super().to_dict()
if isinstance(self.esmfold_config , UpperCamelCase__ ):
A__ : Dict = self.esmfold_config.to_dict()
return output
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_lowerCAmelCase = None
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = 0
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = 128
_lowerCAmelCase = None
def __snake_case ( self ):
if self.trunk is None:
A__ : Tuple = TrunkConfig()
elif isinstance(self.trunk , UpperCamelCase__ ):
A__ : List[Any] = TrunkConfig(**self.trunk )
def __snake_case ( self ):
A__ : Optional[int] = asdict(self )
A__ : int = self.trunk.to_dict()
return output
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_lowerCAmelCase = 48
_lowerCAmelCase = 1_024
_lowerCAmelCase = 128
_lowerCAmelCase = 32
_lowerCAmelCase = 32
_lowerCAmelCase = 32
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = False
_lowerCAmelCase = 4
_lowerCAmelCase = 128
_lowerCAmelCase = None
def __snake_case ( self ):
if self.structure_module is None:
A__ : str = StructureModuleConfig()
elif isinstance(self.structure_module , UpperCamelCase__ ):
A__ : str = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
F" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
F" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
A__ : Tuple = self.sequence_state_dim // self.sequence_head_width
A__ : int = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
F" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
F" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(F"`dropout` should not be greater than 0.4, got {self.dropout}." )
def __snake_case ( self ):
A__ : List[Any] = asdict(self )
A__ : Optional[int] = self.structure_module.to_dict()
return output
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_lowerCAmelCase = 384
_lowerCAmelCase = 128
_lowerCAmelCase = 16
_lowerCAmelCase = 128
_lowerCAmelCase = 12
_lowerCAmelCase = 4
_lowerCAmelCase = 8
_lowerCAmelCase = 0.1
_lowerCAmelCase = 8
_lowerCAmelCase = 1
_lowerCAmelCase = 2
_lowerCAmelCase = 7
_lowerCAmelCase = 10
_lowerCAmelCase = 1e-8
_lowerCAmelCase = 1e5
def __snake_case ( self ):
return asdict(self )
def SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 55
| 1
|
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase = re.compile(R'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(a__ , a__ ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('''+918827897895'''))
| 4
|
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 __SCREAMING_SNAKE_CASE ( _a , _a ):
snake_case : int = """pixel_values"""
snake_case : List[Any] = False
snake_case : str = TimmBackboneConfig
def __init__( self , __lowerCAmelCase , **__lowerCAmelCase ):
requires_backends(self , """timm""" )
super().__init__(__lowerCAmelCase )
UpperCamelCase__ = 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(__lowerCAmelCase , """out_features""" ) and config.out_features is not None:
raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""" )
UpperCamelCase__ = getattr(__lowerCAmelCase , """use_pretrained_backbone""" , __lowerCAmelCase )
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.
UpperCamelCase__ = config.out_indices if getattr(__lowerCAmelCase , """out_indices""" , __lowerCAmelCase ) is not None else (-1,)
UpperCamelCase__ = timm.create_model(
config.backbone , pretrained=__lowerCAmelCase , features_only=config.features_only , in_chans=config.num_channels , out_indices=__lowerCAmelCase , **__lowerCAmelCase , )
# 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.
UpperCamelCase__ = self._backbone.return_layers
UpperCamelCase__ = {layer["""module"""]: str(__lowerCAmelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(__lowerCAmelCase )
@classmethod
def _lowerCamelCase ( cls , __lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase ):
requires_backends(cls , ["""vision""", """timm"""] )
from ...models.timm_backbone import TimmBackboneConfig
UpperCamelCase__ = kwargs.pop("""config""" , TimmBackboneConfig() )
UpperCamelCase__ = kwargs.pop("""use_timm_backbone""" , __lowerCAmelCase )
if not use_timm:
raise ValueError("""use_timm_backbone must be True for timm backbones""" )
UpperCamelCase__ = kwargs.pop("""num_channels""" , config.num_channels )
UpperCamelCase__ = kwargs.pop("""features_only""" , config.features_only )
UpperCamelCase__ = kwargs.pop("""use_pretrained_backbone""" , config.use_pretrained_backbone )
UpperCamelCase__ = kwargs.pop("""out_indices""" , config.out_indices )
UpperCamelCase__ = TimmBackboneConfig(
backbone=__lowerCAmelCase , num_channels=__lowerCAmelCase , features_only=__lowerCAmelCase , use_pretrained_backbone=__lowerCAmelCase , out_indices=__lowerCAmelCase , )
return super()._from_config(__lowerCAmelCase , **__lowerCAmelCase )
def _lowerCamelCase ( self , __lowerCAmelCase ):
pass
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase ):
UpperCamelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCamelCase__ = 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
UpperCamelCase__ = self._all_layers
UpperCamelCase__ = self._backbone(__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ = self._return_layers
UpperCamelCase__ = tuple(hidden_states[i] for i in self.out_indices )
else:
UpperCamelCase__ = self._backbone(__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ = None
UpperCamelCase__ = tuple(__lowerCAmelCase )
UpperCamelCase__ = tuple(__lowerCAmelCase ) if hidden_states is not None else None
if not return_dict:
UpperCamelCase__ = (feature_maps,)
if output_hidden_states:
UpperCamelCase__ = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=__lowerCAmelCase , hidden_states=__lowerCAmelCase , attentions=__lowerCAmelCase )
| 619
| 0
|
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , snake_case : List[Any] ):
'''simple docstring'''
A__ : int = str(id_ )
A__ : int = None
A__ : Union[str, Any] = None
A__ : Optional[Any] = []
A__ : Any = {} # {vertex:distance}
def __lt__( self : List[str] , snake_case : Dict ):
'''simple docstring'''
return self.key < other.key
def __repr__( self : Union[str, Any] ):
'''simple docstring'''
return self.id
def _UpperCamelCase ( self : List[str] , snake_case : Optional[Any] ):
'''simple docstring'''
self.neighbors.append(UpperCamelCase_ )
def _UpperCamelCase ( self : str , snake_case : List[Any] , snake_case : Union[str, Any] ):
'''simple docstring'''
A__ : Union[str, Any] = weight
def _lowerCAmelCase ( UpperCAmelCase__ : Optional[Any], UpperCAmelCase__ : Union[str, Any], UpperCAmelCase__ : Dict, UpperCAmelCase__ : Optional[int] ) ->Union[str, Any]:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1], lowerCamelCase__ )
graph[b - 1].add_edge(graph[a - 1], lowerCamelCase__ )
def _lowerCAmelCase ( UpperCAmelCase__ : List[str], UpperCAmelCase__ : str ) ->list:
A__ : Tuple = []
for u in graph:
A__ : Tuple = math.inf
A__ : Tuple = None
A__ : Tuple = 0
A__ : Tuple = graph[:]
while q:
A__ : Tuple = min(lowerCamelCase__ )
q.remove(lowerCamelCase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
A__ : List[Any] = u
A__ : str = u.edges[v.id]
for i in range(1, len(lowerCamelCase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _lowerCAmelCase ( UpperCAmelCase__ : str, UpperCAmelCase__ : Union[str, Any] ) ->Iterator[tuple]:
for u in graph:
A__ : Union[str, Any] = math.inf
A__ : List[str] = None
A__ : List[str] = 0
A__ : Optional[int] = list(lowerCamelCase__ )
hq.heapify(lowerCamelCase__ )
while h:
A__ : List[str] = hq.heappop(lowerCamelCase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
A__ : Any = u
A__ : List[Any] = u.edges[v.id]
hq.heapify(lowerCamelCase__ )
for i in range(1, len(lowerCamelCase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _lowerCAmelCase ( ) ->None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 710
|
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
def __init__( self : List[Any] , snake_case : NestedDataStructureLike[PathLike] , snake_case : Optional[NamedSplit] = None , snake_case : Optional[Features] = None , snake_case : str = None , snake_case : bool = False , snake_case : bool = False , snake_case : Optional[str] = None , snake_case : Optional[int] = None , **snake_case : Optional[int] , ):
'''simple docstring'''
super().__init__(
snake_case , split=snake_case , features=snake_case , cache_dir=snake_case , keep_in_memory=snake_case , streaming=snake_case , num_proc=snake_case , **snake_case , )
A__ : Optional[int] = field
A__ : Tuple = path_or_paths if isinstance(snake_case , snake_case ) else {self.split: path_or_paths}
A__ : List[Any] = Json(
cache_dir=snake_case , data_files=snake_case , features=snake_case , field=snake_case , **snake_case , )
def _UpperCamelCase ( self : Tuple ):
'''simple docstring'''
if self.streaming:
A__ : List[Any] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
A__ : int = None
A__ : Dict = None
A__ : Tuple = None
A__ : Optional[Any] = None
self.builder.download_and_prepare(
download_config=snake_case , download_mode=snake_case , verification_mode=snake_case , base_path=snake_case , num_proc=self.num_proc , )
A__ : str = self.builder.as_dataset(
split=self.split , verification_mode=snake_case , in_memory=self.keep_in_memory )
return dataset
class __SCREAMING_SNAKE_CASE :
def __init__( self : Any , snake_case : Dataset , snake_case : Union[PathLike, BinaryIO] , snake_case : Optional[int] = None , snake_case : Optional[int] = None , **snake_case : Union[str, Any] , ):
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(F'num_proc {num_proc} must be an integer > 0.' )
A__ : Union[str, Any] = dataset
A__ : Any = path_or_buf
A__ : str = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
A__ : str = num_proc
A__ : Union[str, Any] = """utf-8"""
A__ : Optional[int] = to_json_kwargs
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
A__ : Any = self.to_json_kwargs.pop("""path_or_buf""" , snake_case )
A__ : List[Any] = self.to_json_kwargs.pop("""orient""" , """records""" )
A__ : List[str] = self.to_json_kwargs.pop("""lines""" , True if orient == """records""" else False )
A__ : str = self.to_json_kwargs.pop("""index""" , False if orient in ["""split""", """table"""] else True )
A__ : Optional[int] = self.to_json_kwargs.pop("""compression""" , snake_case )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(F'`datasets` currently does not support {compression} compression' )
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf , """wb""" , compression=snake_case ) as buffer:
A__ : Tuple = self._write(file_obj=snake_case , orient=snake_case , lines=snake_case , index=snake_case , **self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
""" was passed. Please provide a local path instead.""" )
A__ : List[Any] = self._write(
file_obj=self.path_or_buf , orient=snake_case , lines=snake_case , index=snake_case , **self.to_json_kwargs )
return written
def _UpperCamelCase ( self : List[Any] , snake_case : List[str] ):
'''simple docstring'''
A__ , A__ , A__ , A__ , A__ : int = args
A__ : Any = query_table(
table=self.dataset.data , key=slice(snake_case , offset + self.batch_size ) , indices=self.dataset._indices , )
A__ : List[Any] = batch.to_pandas().to_json(
path_or_buf=snake_case , orient=snake_case , lines=snake_case , index=snake_case , **snake_case )
if not json_str.endswith("""\n""" ):
json_str += "\n"
return json_str.encode(self.encoding )
def _UpperCamelCase ( self : int , snake_case : BinaryIO , snake_case : List[Any] , snake_case : Tuple , snake_case : List[str] , **snake_case : str , ):
'''simple docstring'''
A__ : List[Any] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating json from Arrow format""" , ):
A__ : List[str] = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(snake_case )
else:
A__ , A__ : Optional[int] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , snake_case , snake_case )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating json from Arrow format""" , ):
written += file_obj.write(snake_case )
return written
| 498
| 0
|
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__lowerCAmelCase = 4
__lowerCAmelCase = 3
class __SCREAMING_SNAKE_CASE (lowerCAmelCase_ ):
"""simple docstring"""
pass
def __UpperCamelCase ( lowercase_ : List[Any] ):
"""simple docstring"""
for shard in shards:
for i in range(lowercase_ ):
yield {"i": i, "shard": shard}
def __UpperCamelCase ( ):
"""simple docstring"""
a_ = int(os.environ['RANK'] )
a_ = int(os.environ['WORLD_SIZE'] )
a_ = ArgumentParser()
parser.add_argument('--streaming' , type=lowercase_ )
parser.add_argument('--local_rank' , type=lowercase_ )
parser.add_argument('--num_workers' , type=lowercase_ , default=0 )
a_ = parser.parse_args()
a_ = args.streaming
a_ = args.num_workers
a_ = {'shards': [F'shard_{shard_idx}' for shard_idx in range(lowercase_ )]}
a_ = IterableDataset.from_generator(lowercase_ , gen_kwargs=lowercase_ )
if not streaming:
a_ = Dataset.from_list(list(lowercase_ ) )
a_ = split_dataset_by_node(lowercase_ , rank=lowercase_ , world_size=lowercase_ )
a_ = torch.utils.data.DataLoader(lowercase_ , num_workers=lowercase_ )
a_ = NUM_SHARDS * NUM_ITEMS_PER_SHARD
a_ = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
a_ = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(F'local_size {local_size} != expected_local_size {expected_local_size}' )
if __name__ == "__main__":
main()
| 536
|
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a : Dict = _symbol_database.Default()
_a : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_a : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_a : str = None
_a : Union[str, Any] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_a : Optional[int] = 4_5
_a : List[Any] = 1_5_8_1
_a : str = 1_5_1_7
_a : Optional[Any] = 1_5_7_0
_a : List[str] = 1_5_8_4
_a : List[Any] = 1_7_9_3
_a : Union[str, Any] = 1_7_9_5
_a : Tuple = 1_9_1_6
_a : List[Any] = 1_8_6_4
_a : Any = 1_9_0_5
_a : Optional[Any] = 1_9_1_9
_a : Optional[int] = 2_4_2_9
_a : Tuple = 2_2_0_8
_a : Optional[Any] = 2_4_1_8
_a : List[Any] = 2_3_2_3
_a : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 689
| 0
|
'''simple docstring'''
def UpperCamelCase_ ( __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(__SCREAMING_SNAKE_CASE ) * abs(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 92
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
a_ = TypeVar("T")
a_ = TypeVar("U")
class UpperCAmelCase_ ( Generic[T, U] ):
def __init__( self , lowercase_ , lowercase_):
snake_case_ : Any = key
snake_case_ : List[Any] = val
snake_case_ : DoubleLinkedListNode[T, U] | None = None
snake_case_ : DoubleLinkedListNode[T, U] | None = None
def __repr__( self):
return (
F'Node: key: {self.key}, val: {self.val}, '
F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
)
class UpperCAmelCase_ ( Generic[T, U] ):
def __init__( self):
snake_case_ : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(lowercase_ , lowercase_)
snake_case_ : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(lowercase_ , lowercase_)
snake_case_ , snake_case_ : Union[str, Any] = self.rear, self.head
def __repr__( self):
snake_case_ : Dict = ["DoubleLinkedList"]
snake_case_ : Dict = self.head
while node.next is not None:
rep.append(str(lowercase_))
snake_case_ : List[str] = node.next
rep.append(str(self.rear))
return ",\n ".join(lowercase_)
def snake_case__ ( self , lowercase_):
snake_case_ : List[str] = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
snake_case_ : Tuple = node
snake_case_ : str = previous
snake_case_ : Optional[Any] = node
snake_case_ : Any = self.rear
def snake_case__ ( self , lowercase_):
if node.prev is None or node.next is None:
return None
snake_case_ : Union[str, Any] = node.next
snake_case_ : Optional[int] = node.prev
snake_case_ : str = None
snake_case_ : int = None
return node
class UpperCAmelCase_ ( Generic[T, U] ):
UpperCAmelCase_ = {}
def __init__( self , lowercase_):
snake_case_ : DoubleLinkedList[T, U] = DoubleLinkedList()
snake_case_ : List[str] = capacity
snake_case_ : Any = 0
snake_case_ : Dict = 0
snake_case_ : Union[str, Any] = 0
snake_case_ : dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__( self):
return (
F'CacheInfo(hits={self.hits}, misses={self.miss}, '
F'capacity={self.capacity}, current size={self.num_keys})'
)
def __contains__( self , lowercase_):
return key in self.cache
def snake_case__ ( self , lowercase_):
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
snake_case_ : DoubleLinkedListNode[T, U] = self.cache[key]
snake_case_ : Optional[Any] = self.list.remove(self.cache[key])
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowercase_)
return node.val
self.miss += 1
return None
def snake_case__ ( self , lowercase_ , lowercase_):
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
snake_case_ : Optional[Any] = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowercase_) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
snake_case_ : Dict = DoubleLinkedListNode(lowercase_ , lowercase_)
self.list.add(self.cache[key])
self.num_keys += 1
else:
# bump node to the end of the list, update value
snake_case_ : List[Any] = self.list.remove(self.cache[key])
assert node is not None # node guaranteed to be in list
snake_case_ : Optional[Any] = value
self.list.add(lowercase_)
@classmethod
def snake_case__ ( cls , lowercase_ = 1_28):
def cache_decorator_inner(lowercase_) -> Callable[..., U]:
def cache_decorator_wrapper(*lowercase_) -> U:
if func not in cls.decorator_function_to_instance_map:
snake_case_ : List[str] = LRUCache(lowercase_)
snake_case_ : Optional[Any] = cls.decorator_function_to_instance_map[func].get(args[0])
if result is None:
snake_case_ : Any = func(*lowercase_)
cls.decorator_function_to_instance_map[func].put(args[0] , lowercase_)
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowercase_ , "cache_info" , lowercase_) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92
| 1
|
import json
import sys
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> int:
"""simple docstring"""
with open(_UpperCamelCase , encoding='utf-8') as f:
UpperCamelCase = json.load(_UpperCamelCase)
UpperCamelCase = ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']
for benchmark_name in sorted(_UpperCamelCase):
UpperCamelCase = results[benchmark_name]
UpperCamelCase = benchmark_name.split('/')[-1]
output_md.append(F'### Benchmark: {benchmark_file_name}')
UpperCamelCase = '| metric |'
UpperCamelCase = '|--------|'
UpperCamelCase = '| new / old (diff) |'
for metric_name in sorted(_UpperCamelCase):
UpperCamelCase = benchmark_res[metric_name]
UpperCamelCase = metric_vals['new']
UpperCamelCase = metric_vals.get('old' , _UpperCamelCase)
UpperCamelCase = metric_vals.get('diff' , _UpperCamelCase)
UpperCamelCase = F' {new_val:f}' if isinstance(_UpperCamelCase , (int, float)) else 'None'
if old_val is not None:
val_str += F' / {old_val:f}' if isinstance(_UpperCamelCase , (int, float)) else "None"
if dif_val is not None:
val_str += F' ({dif_val:f})' if isinstance(_UpperCamelCase , (int, float)) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append('</details>')
with open(_UpperCamelCase , 'w' , encoding='utf-8') as f:
f.writelines('\n'.join(_UpperCamelCase))
if __name__ == "__main__":
__magic_name__ : List[Any] = sys.argv[1]
__magic_name__ : List[Any] = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 280
|
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class A__ ( __snake_case ):
'''simple docstring'''
@slow
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' , 'prajjwal1/bert-tiny' )
UpperCamelCase = BertTokenizer.from_pretrained('bert-base-uncased' )
UpperCamelCase = bertabert.config.encoder.vocab_size
UpperCamelCase = tokenizer.sep_token_id
UpperCamelCase = tokenizer.cls_token_id
UpperCamelCase = 128
UpperCamelCase = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='train[:1%]' )
UpperCamelCase = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='validation[:1%]' )
UpperCamelCase = train_dataset.select(range(32 ) )
UpperCamelCase = val_dataset.select(range(16 ) )
UpperCamelCase = 4
def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE : Tuple ):
# Tokenizer will automatically set [BOS] <text> [EOS]
UpperCamelCase = tokenizer(batch['article'] , padding='max_length' , truncation=_SCREAMING_SNAKE_CASE , max_length=512 )
UpperCamelCase = tokenizer(batch['highlights'] , padding='max_length' , truncation=_SCREAMING_SNAKE_CASE , max_length=128 )
UpperCamelCase = inputs.input_ids
UpperCamelCase = inputs.attention_mask
UpperCamelCase = outputs.input_ids
UpperCamelCase = outputs.input_ids.copy()
UpperCamelCase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
]
UpperCamelCase = outputs.attention_mask
assert all(len(_SCREAMING_SNAKE_CASE ) == 512 for x in inputs.input_ids )
assert all(len(_SCREAMING_SNAKE_CASE ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_SCREAMING_SNAKE_CASE : str ):
UpperCamelCase = pred.label_ids
UpperCamelCase = pred.predictions
# all unnecessary tokens are removed
UpperCamelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
UpperCamelCase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )
return {"accuracy": accuracy}
# map train dataset
UpperCamelCase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , remove_columns=['article', 'highlights'] , )
train_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
# same for validation dataset
UpperCamelCase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , remove_columns=['article', 'highlights'] , )
val_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
UpperCamelCase = self.get_auto_remove_tmp_dir()
UpperCamelCase = SeqaSeqTrainingArguments(
output_dir=_SCREAMING_SNAKE_CASE , per_device_train_batch_size=_SCREAMING_SNAKE_CASE , per_device_eval_batch_size=_SCREAMING_SNAKE_CASE , predict_with_generate=_SCREAMING_SNAKE_CASE , evaluation_strategy='steps' , do_train=_SCREAMING_SNAKE_CASE , do_eval=_SCREAMING_SNAKE_CASE , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
UpperCamelCase = SeqaSeqTrainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , compute_metrics=_compute_metrics , train_dataset=_SCREAMING_SNAKE_CASE , eval_dataset=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , )
# start training
trainer.train()
| 280
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|
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_lowercase)
class __snake_case ( _lowercase):
snake_case__ : str = field(default="automatic-speech-recognition" , metadata={"include_in_asdict_even_if_is_default": True})
snake_case__ : ClassVar[Features] = Features({"audio": Audio()})
snake_case__ : ClassVar[Features] = Features({"transcription": Value("string")})
snake_case__ : str = "audio"
snake_case__ : str = "transcription"
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
if self.audio_column not in features:
raise ValueError(f'''Column {self.audio_column} is not present in features.''' )
if not isinstance(features[self.audio_column] , __lowerCAmelCase ):
raise ValueError(f'''Column {self.audio_column} is not an Audio type.''' )
_lowerCamelCase : int = copy.deepcopy(self )
_lowerCamelCase : int = self.input_schema.copy()
_lowerCamelCase : List[str] = features[self.audio_column]
_lowerCamelCase : Tuple = input_schema
return task_template
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 598
|
"""simple docstring"""
import os
import sys
import unittest
lowerCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCAmelCase__ = os.path.join(git_repo_path, '''src''', '''transformers''')
lowerCAmelCase__ = '''
{0} = None
'''
lowerCAmelCase__ = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
'''
lowerCAmelCase__ = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
class __snake_case ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[str] = find_backend(''' _import_structure["models.albert"].append("AlbertTokenizerFast")''' )
self.assertIsNone(__lowerCAmelCase )
_lowerCamelCase : List[str] = find_backend(''' if not is_tokenizers_available():''' )
self.assertEqual(__lowerCAmelCase , '''tokenizers''' )
_lowerCamelCase : Optional[int] = find_backend(''' if not is_tensorflow_text_available():''' )
self.assertEqual(__lowerCAmelCase , '''tensorflow_text''' )
_lowerCamelCase : Tuple = find_backend(''' if not (is_sentencepiece_available() and is_tokenizers_available()):''' )
self.assertEqual(__lowerCAmelCase , '''sentencepiece_and_tokenizers''' )
_lowerCamelCase : List[str] = find_backend(
''' if not (is_sentencepiece_available() and is_tensorflow_text_available()):''' )
self.assertEqual(__lowerCAmelCase , '''sentencepiece_and_tensorflow_text''' )
_lowerCamelCase : List[str] = find_backend(
''' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''' )
self.assertEqual(__lowerCAmelCase , '''sentencepiece_and_tokenizers_and_vision''' )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
_lowerCamelCase : Tuple = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , __lowerCAmelCase )
self.assertIn('''tensorflow_text''' , __lowerCAmelCase )
self.assertIn('''sentencepiece_and_tokenizers''' , __lowerCAmelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertModel''' , objects['''tf'''] )
self.assertIn('''FlaxBertModel''' , objects['''flax'''] )
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertTokenizer''' , objects['''tensorflow_text'''] )
self.assertIn('''convert_slow_tokenizer''' , objects['''sentencepiece_and_tokenizers'''] )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(__lowerCAmelCase , '''\nCONSTANT = None\n''' )
_lowerCamelCase : Union[str, Any] = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
__lowerCAmelCase , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
_lowerCamelCase : Union[str, Any] = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
'''
_lowerCamelCase : str = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
_lowerCamelCase : str = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
'''
_lowerCamelCase : Optional[Any] = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , __lowerCAmelCase )
| 598
| 1
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class UpperCAmelCase_ :
def __init__( self, __a, __a=2, __a=True, __a=False, __a=10, __a=3, __a=32 * 8, __a=32 * 8, __a=4, __a=64, ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = parent
_lowerCAmelCase : Optional[Any] = batch_size
_lowerCAmelCase : List[Any] = is_training
_lowerCAmelCase : int = use_auxiliary_loss
_lowerCAmelCase : List[str] = num_queries
_lowerCAmelCase : Optional[int] = num_channels
_lowerCAmelCase : Union[str, Any] = min_size
_lowerCAmelCase : Dict = max_size
_lowerCAmelCase : Optional[Any] = num_labels
_lowerCAmelCase : Any = hidden_dim
_lowerCAmelCase : List[str] = hidden_dim
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
lowercase_)
_lowerCAmelCase : List[str] = torch.ones([self.batch_size, self.min_size, self.max_size], device=lowercase_)
_lowerCAmelCase : Union[str, Any] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size], device=lowercase_) > 0.5
).float()
_lowerCAmelCase : Optional[int] = (torch.rand((self.batch_size, self.num_labels), device=lowercase_) > 0.5).long()
_lowerCAmelCase : Tuple = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = MaskaFormerConfig(
hidden_size=self.hidden_dim, )
_lowerCAmelCase : Dict = self.num_queries
_lowerCAmelCase : Optional[Any] = self.num_labels
_lowerCAmelCase : str = [1, 1, 1, 1]
_lowerCAmelCase : Dict = self.num_channels
_lowerCAmelCase : str = 64
_lowerCAmelCase : Any = 128
_lowerCAmelCase : Optional[int] = self.hidden_dim
_lowerCAmelCase : int = self.hidden_dim
_lowerCAmelCase : str = self.hidden_dim
return config
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : str = self.prepare_config_and_inputs()
_lowerCAmelCase : Dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = output.encoder_hidden_states
_lowerCAmelCase : Dict = output.pixel_decoder_hidden_states
_lowerCAmelCase : Tuple = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowercase_), len(config.backbone_config.depths))
self.parent.assertTrue(len(lowercase_), len(config.backbone_config.depths))
self.parent.assertTrue(len(lowercase_), config.decoder_layers)
def snake_case__ ( self, __a, __a, __a, __a=False):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase : Any = MaskaFormerModel(config=lowercase_)
model.to(lowercase_)
model.eval()
_lowerCAmelCase : str = model(pixel_values=lowercase_, pixel_mask=lowercase_)
_lowerCAmelCase : Dict = model(lowercase_, output_hidden_states=lowercase_)
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_dim), )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(output.encoder_last_hidden_state is not None)
if output_hidden_states:
self.check_output_hidden_state(lowercase_, lowercase_)
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = MaskaFormerForUniversalSegmentation(config=lowercase_)
model.to(lowercase_)
model.eval()
def comm_check_on_output(__a):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.encoder_last_hidden_state is not None)
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape, (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4), )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
with torch.no_grad():
_lowerCAmelCase : Tuple = model(pixel_values=lowercase_, pixel_mask=lowercase_)
_lowerCAmelCase : int = model(lowercase_)
comm_check_on_output(lowercase_)
_lowerCAmelCase : Any = model(
pixel_values=lowercase_, pixel_mask=lowercase_, mask_labels=lowercase_, class_labels=lowercase_)
comm_check_on_output(lowercase_)
self.parent.assertTrue(result.loss is not None)
self.parent.assertEqual(result.loss.shape, torch.Size([1]))
@require_torch
class UpperCAmelCase_ ( __a , __a , unittest.TestCase):
lowerCamelCase__ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
lowerCamelCase__ = {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = MaskaFormerModelTester(self)
_lowerCAmelCase : List[Any] = ConfigTester(self, config_class=lowercase_, has_text_modality=lowercase_)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowercase_, **lowercase_, output_hidden_states=lowercase_)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowercase_)
@unittest.skip(reason="Mask2Former does not use inputs_embeds")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip(reason="Mask2Former is not a generative model")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip(reason="Mask2Former does not use token embeddings")
def snake_case__ ( self):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase : Union[str, Any] = model_class(lowercase_)
_lowerCAmelCase : Tuple = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase : List[str] = [*signature.parameters.keys()]
_lowerCAmelCase : str = ["pixel_values"]
self.assertListEqual(arg_names[:1], lowercase_)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
_lowerCAmelCase : List[str] = MaskaFormerModel.from_pretrained(lowercase_)
self.assertIsNotNone(lowercase_)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = (self.model_tester.min_size,) * 2
_lowerCAmelCase : Any = {
"pixel_values": torch.randn((2, 3, *size), device=lowercase_),
"mask_labels": torch.randn((2, 10, *size), device=lowercase_),
"class_labels": torch.zeros(2, 10, device=lowercase_).long(),
}
_lowerCAmelCase : Optional[int] = self.model_tester.get_config()
_lowerCAmelCase : Any = MaskaFormerForUniversalSegmentation(lowercase_).to(lowercase_)
_lowerCAmelCase : int = model(**lowercase_)
self.assertTrue(outputs.loss is not None)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowercase_, **lowercase_, output_hidden_states=lowercase_)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase : List[str] = model_class(lowercase_).to(lowercase_)
_lowerCAmelCase : Any = model(**lowercase_, output_attentions=lowercase_)
self.assertTrue(outputs.attentions is not None)
def snake_case__ ( self):
'''simple docstring'''
if not self.model_tester.is_training:
return
_lowerCAmelCase : Any = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase : Optional[int] = model_class(lowercase_)
model.to(lowercase_)
model.train()
_lowerCAmelCase : Any = model(lowercase_, mask_labels=lowercase_, class_labels=lowercase_).loss
loss.backward()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase : str = True
_lowerCAmelCase : int = True
_lowerCAmelCase : Tuple = model_class(lowercase_).to(lowercase_)
model.train()
_lowerCAmelCase : Union[str, Any] = model(lowercase_, mask_labels=lowercase_, class_labels=lowercase_)
_lowerCAmelCase : Any = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_lowerCAmelCase : Optional[Any] = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
_lowerCAmelCase : int = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_lowerCAmelCase : Dict = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowercase_)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(pixel_decoder_hidden_states.grad)
self.assertIsNotNone(transformer_decoder_hidden_states.grad)
self.assertIsNotNone(attentions.grad)
_snake_case = 1e-4
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def snake_case__ ( self):
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def snake_case__ ( self):
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = MaskaFormerModel.from_pretrained(self.model_checkpoints).to(lowercase_)
_lowerCAmelCase : Optional[Any] = self.default_image_processor
_lowerCAmelCase : Union[str, Any] = prepare_img()
_lowerCAmelCase : Optional[int] = image_processor(lowercase_, return_tensors="pt").to(lowercase_)
_lowerCAmelCase : Optional[Any] = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(lowercase_, (1, 3, 384, 384))
with torch.no_grad():
_lowerCAmelCase : int = model(**lowercase_)
_lowerCAmelCase : List[str] = torch.tensor(
[[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]).to(lowercase_)
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3], lowercase_, atol=lowercase_))
_lowerCAmelCase : Optional[Any] = torch.tensor(
[[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]).to(lowercase_)
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], lowercase_, atol=lowercase_))
_lowerCAmelCase : Optional[int] = torch.tensor(
[[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]).to(lowercase_)
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3], lowercase_, atol=lowercase_))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(lowercase_).eval()
_lowerCAmelCase : Tuple = self.default_image_processor
_lowerCAmelCase : str = prepare_img()
_lowerCAmelCase : Dict = image_processor(lowercase_, return_tensors="pt").to(lowercase_)
_lowerCAmelCase : Optional[Any] = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(lowercase_, (1, 3, 384, 384))
with torch.no_grad():
_lowerCAmelCase : str = model(**lowercase_)
# masks_queries_logits
_lowerCAmelCase : Optional[int] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape, (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
_lowerCAmelCase : Dict = [
[-8.7_839, -9.0_056, -8.8_121],
[-7.4_104, -7.0_313, -6.5_401],
[-6.6_105, -6.3_427, -6.4_675],
]
_lowerCAmelCase : Dict = torch.tensor(lowercase_).to(lowercase_)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], lowercase_, atol=lowercase_))
# class_queries_logits
_lowerCAmelCase : Tuple = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape, (1, model.config.num_queries, model.config.num_labels + 1))
_lowerCAmelCase : List[str] = torch.tensor(
[
[1.8_324, -8.0_835, -4.1_922],
[0.8_450, -9.0_050, -3.6_053],
[0.3_045, -7.7_293, -3.0_275],
]).to(lowercase_)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], lowercase_, atol=lowercase_))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(lowercase_).eval()
_lowerCAmelCase : str = self.default_image_processor
_lowerCAmelCase : Optional[Any] = image_processor(
[np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))], segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)], return_tensors="pt", )
_lowerCAmelCase : List[str] = inputs["pixel_values"].to(lowercase_)
_lowerCAmelCase : List[str] = [el.to(lowercase_) for el in inputs["mask_labels"]]
_lowerCAmelCase : Any = [el.to(lowercase_) for el in inputs["class_labels"]]
with torch.no_grad():
_lowerCAmelCase : Tuple = model(**lowercase_)
self.assertTrue(outputs.loss is not None)
| 500
|
'''simple docstring'''
import math
def A_ ( SCREAMING_SNAKE_CASE_ ) ->int:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = f"""Input value of [number={number}] must be an integer"""
raise TypeError(SCREAMING_SNAKE_CASE_ )
if number < 1:
lowercase_ = f"""Input value of [number={number}] must be > 0"""
raise ValueError(SCREAMING_SNAKE_CASE_ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
lowercase_ = int(math.log(number // 3 , 2 ) ) + 2
lowercase_ = [3, 5]
lowercase_ = 2
lowercase_ = 3
for block in range(1 , SCREAMING_SNAKE_CASE_ ):
for _ in range(SCREAMING_SNAKE_CASE_ ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
__snake_case = 0
try:
__snake_case = proth(number)
except ValueError:
print(f'''ValueError: there is no {number}th Proth number''')
continue
print(f'''The {number}th Proth number: {value}''')
| 451
| 0
|
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
__UpperCAmelCase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def _lowerCamelCase ( A_ : int , A_ : Union[str, Any] , A_ : Any , A_ : Optional[int] , A_ : Union[str, Any] ) -> Any:
'''simple docstring'''
for attribute in key.split("." ):
UpperCamelCase__ : List[Any] =getattr(A_ , A_ )
if weight_type is not None:
UpperCamelCase__ : str =getattr(A_ , A_ ).shape
else:
UpperCamelCase__ : Union[str, Any] =hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
UpperCamelCase__ : Any =value
elif weight_type == "weight_g":
UpperCamelCase__ : str =value
elif weight_type == "weight_v":
UpperCamelCase__ : Tuple =value
elif weight_type == "bias":
UpperCamelCase__ : Optional[int] =value
else:
UpperCamelCase__ : Any =value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def _lowerCamelCase ( A_ : int , A_ : List[str] ) -> int:
'''simple docstring'''
UpperCamelCase__ : List[str] =[]
UpperCamelCase__ : Optional[Any] =fairseq_model.state_dict()
UpperCamelCase__ : Optional[Any] =hf_model.feature_extractor
UpperCamelCase__ : Any =hf_model.adapter
for name, value in fairseq_dict.items():
UpperCamelCase__ : Tuple =False
if "conv_layers" in name:
load_conv_layer(
A_ , A_ , A_ , A_ , hf_model.config.feat_extract_norm == "group" , )
UpperCamelCase__ : Optional[int] =True
elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."] ):
load_adapter(A_ , A_ , A_ , A_ )
UpperCamelCase__ : List[str] =True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
UpperCamelCase__ : Optional[int] =True
if "*" in mapped_key:
UpperCamelCase__ : Tuple =name.split(A_ )[0].split("." )[-2]
UpperCamelCase__ : Any =mapped_key.replace("*" , A_ )
if "weight_g" in name:
UpperCamelCase__ : Dict ="weight_g"
elif "weight_v" in name:
UpperCamelCase__ : List[str] ="weight_v"
elif "bias" in name:
UpperCamelCase__ : Dict ="bias"
elif "weight" in name:
UpperCamelCase__ : Union[str, Any] ="weight"
else:
UpperCamelCase__ : Optional[int] =None
set_recursively(A_ , A_ , A_ , A_ , A_ )
continue
if not is_used:
unused_weights.append(A_ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def _lowerCamelCase ( A_ : Tuple , A_ : List[Any] , A_ : Tuple , A_ : List[str] , A_ : Any ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Dict =full_name.split("conv_layers." )[-1]
UpperCamelCase__ : Optional[Any] =name.split("." )
UpperCamelCase__ : Tuple =int(items[0] )
UpperCamelCase__ : str =int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
UpperCamelCase__ : List[Any] =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
UpperCamelCase__ : Union[str, Any] =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
UpperCamelCase__ : Optional[int] =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
UpperCamelCase__ : List[Any] =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(A_ )
def _lowerCamelCase ( A_ : List[Any] , A_ : Optional[int] , A_ : Tuple , A_ : Optional[int] ) -> int:
'''simple docstring'''
UpperCamelCase__ : Any =full_name.split("adaptor." )[-1]
UpperCamelCase__ : Union[str, Any] =name.split("." )
if items[1].isdigit():
UpperCamelCase__ : List[Any] =int(items[1] )
else:
UpperCamelCase__ : str =None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
UpperCamelCase__ : Optional[Any] =value
logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
UpperCamelCase__ : Union[str, Any] =value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
UpperCamelCase__ : str =value
logger.info(f'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
UpperCamelCase__ : Union[str, Any] =value
logger.info(f'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(A_ , A_ ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
UpperCamelCase__ : int =value
logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
UpperCamelCase__ : List[Any] =value
logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(A_ )
def _lowerCamelCase ( A_ : List[Any] ) -> str:
'''simple docstring'''
UpperCamelCase__ : int =emb.weight.shape
UpperCamelCase__ : Tuple =nn.Linear(A_ , A_ , bias=A_ )
UpperCamelCase__ : Optional[int] =emb.weight.data
return lin_layer
@torch.no_grad()
def _lowerCamelCase ( A_ : Optional[Any] , A_ : Optional[int] , A_ : List[Any] , A_ : Optional[Any] , A_ : List[str] , A_ : Any , A_ : Union[str, Any] , A_ : Optional[int] , A_ : Optional[int] , A_ : str , A_ : Optional[Any] , ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Any =WavaVecaConfig.from_pretrained(
A_ , add_adapter=A_ , adapter_stride=A_ , adapter_kernel_size=A_ , use_auth_token=A_ , output_hidden_size=A_ , )
UpperCamelCase__ : List[str] =MBartConfig.from_pretrained(A_ )
# load model
UpperCamelCase__ : str =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
"config_yaml": config_yaml_path,
"data": "/".join(dict_path.split("/" )[:-1] ),
"w2v_path": checkpoint_path,
"load_pretrained_decoder_from": None,
} , )
UpperCamelCase__ : Tuple =model[0].eval()
# load feature extractor
UpperCamelCase__ : Tuple =WavaVecaFeatureExtractor.from_pretrained(A_ , use_auth_token=A_ )
# set weights for wav2vec2 encoder
UpperCamelCase__ : str =WavaVecaModel(A_ )
recursively_load_weights_wavaveca(model.encoder , A_ )
# load decoder weights
UpperCamelCase__ : int =MBartForCausalLM(A_ )
UpperCamelCase__ : Any =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=A_ )
logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
UpperCamelCase__ : str =SpeechEncoderDecoderModel(encoder=A_ , decoder=A_ )
UpperCamelCase__ : Optional[int] =False
UpperCamelCase__ : int =MBartaaTokenizer(A_ )
tokenizer.save_pretrained(A_ )
UpperCamelCase__ : Union[str, Any] =hf_wavavec.config.to_dict()
UpperCamelCase__ : Any =tokenizer.pad_token_id
UpperCamelCase__ : List[Any] =tokenizer.bos_token_id
UpperCamelCase__ : Optional[int] =tokenizer.eos_token_id
UpperCamelCase__ : Optional[Any] ="mbart50"
UpperCamelCase__ : str ="wav2vec2"
UpperCamelCase__ : Optional[int] =tokenizer.eos_token_id
UpperCamelCase__ : Optional[Any] =2_5_0_0_0_4
UpperCamelCase__ : int =tokenizer.eos_token_id
UpperCamelCase__ : int =SpeechEncoderDecoderConfig.from_dict(A_ )
hf_wavavec.save_pretrained(A_ )
feature_extractor.save_pretrained(A_ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
parser.add_argument(
"""--encoder_config_path""",
default="""facebook/wav2vec2-xls-r-1b""",
type=str,
help="""Path to hf encoder wav2vec2 checkpoint config""",
)
parser.add_argument(
"""--decoder_config_path""",
default="""facebook/mbart-large-50-one-to-many-mmt""",
type=str,
help="""Path to hf decoder checkpoint config""",
)
parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 721
|
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def _lowerCamelCase ( A_ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return {key.lstrip("-" ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def _lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : Tuple =ArgumentParser(
"HuggingFace Datasets CLI tool" , usage="datasets-cli <command> [<args>]" , allow_abbrev=A_ )
UpperCamelCase__ : Tuple =parser.add_subparsers(help="datasets-cli command helpers" )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(A_ )
EnvironmentCommand.register_subcommand(A_ )
TestCommand.register_subcommand(A_ )
RunBeamCommand.register_subcommand(A_ )
DummyDataCommand.register_subcommand(A_ )
# Parse args
UpperCamelCase__ , UpperCamelCase__ : List[Any] =parser.parse_known_args()
if not hasattr(A_ , "func" ):
parser.print_help()
exit(1 )
UpperCamelCase__ : Union[str, Any] =parse_unknown_args(A_ )
# Run
UpperCamelCase__ : Tuple =args.func(A_ , **A_ )
service.run()
if __name__ == "__main__":
main()
| 582
| 0
|
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: Dict = prime_factors(__SCREAMING_SNAKE_CASE )
if is_square_free(__SCREAMING_SNAKE_CASE ):
return -1 if len(__SCREAMING_SNAKE_CASE ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 346
|
"""simple docstring"""
from __future__ import annotations
def a__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
__lowerCAmelCase: str = [True] * limit
__lowerCAmelCase: List[Any] = False
__lowerCAmelCase: List[str] = False
__lowerCAmelCase: int = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
__lowerCAmelCase: Tuple = i * 2
while index < limit:
__lowerCAmelCase: List[Any] = False
__lowerCAmelCase: Optional[int] = index + i
__lowerCAmelCase: Tuple = [2]
for i in range(3 , __SCREAMING_SNAKE_CASE , 2 ):
if is_prime[i]:
primes.append(__SCREAMING_SNAKE_CASE )
return primes
def a__ ( __SCREAMING_SNAKE_CASE = 1_0_0_0_0_0_0 ) -> int:
__lowerCAmelCase: Tuple = prime_sieve(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = 0
__lowerCAmelCase: str = 0
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
for j in range(i + length , len(__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase: Optional[Any] = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
__lowerCAmelCase: str = j - i
__lowerCAmelCase: List[str] = sol
return largest
if __name__ == "__main__":
print(F'''{solution() = }''')
| 346
| 1
|
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def A__ ( A : int):
'''simple docstring'''
UpperCamelCase : int = int(number**0.5)
return number == sq * sq
def A__ ( A : int , A : int , A : int , A : int , A : int , A : int):
'''simple docstring'''
UpperCamelCase : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase : int = x_den * y_den * z_den
UpperCamelCase : int = gcd(A , A)
top //= hcf
bottom //= hcf
return top, bottom
def A__ ( A : int = 35):
'''simple docstring'''
UpperCamelCase : set = set()
UpperCamelCase : int
UpperCamelCase : Fraction = Fraction(0)
UpperCamelCase : tuple[int, int]
for x_num in range(1 , order + 1):
for x_den in range(x_num + 1 , order + 1):
for y_num in range(1 , order + 1):
for y_den in range(y_num + 1 , order + 1):
# n=1
UpperCamelCase : Optional[int] = x_num * y_den + x_den * y_num
UpperCamelCase : str = x_den * y_den
UpperCamelCase : Dict = gcd(A , A)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : Dict = add_three(
A , A , A , A , A , A)
unique_s.add(A)
# n=2
UpperCamelCase : Optional[Any] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase : List[str] = x_den * x_den * y_den * y_den
if is_sq(A) and is_sq(A):
UpperCamelCase : Dict = int(sqrt(A))
UpperCamelCase : Union[str, Any] = int(sqrt(A))
UpperCamelCase : Union[str, Any] = gcd(A , A)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : List[str] = add_three(
A , A , A , A , A , A)
unique_s.add(A)
# n=-1
UpperCamelCase : Union[str, Any] = x_num * y_num
UpperCamelCase : List[str] = x_den * y_num + x_num * y_den
UpperCamelCase : List[str] = gcd(A , A)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : List[str] = add_three(
A , A , A , A , A , A)
unique_s.add(A)
# n=2
UpperCamelCase : int = x_num * x_num * y_num * y_num
UpperCamelCase : Any = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(A) and is_sq(A):
UpperCamelCase : Optional[int] = int(sqrt(A))
UpperCamelCase : Union[str, Any] = int(sqrt(A))
UpperCamelCase : str = gcd(A , A)
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : Union[str, Any] = add_three(
A , A , A , A , A , A)
unique_s.add(A)
for num, den in unique_s:
total += Fraction(A , A)
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"""{solution() = }""")
| 700
|
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = StableUnCLIPPipeline
__SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_PARAMS
__SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_BATCH_PARAMS
__SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
__SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
__SCREAMING_SNAKE_CASE = False
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
UpperCamelCase : Union[str, Any] = 32
UpperCamelCase : List[str] = embedder_hidden_size
# prior components
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
UpperCamelCase : Tuple = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCamelCase , projection_dim=lowerCamelCase , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) )
torch.manual_seed(0 )
UpperCamelCase : Dict = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=lowerCamelCase , num_layers=1 , )
torch.manual_seed(0 )
UpperCamelCase : str = DDPMScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=10_00 , clip_sample=lowerCamelCase , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , )
# regular denoising components
torch.manual_seed(0 )
UpperCamelCase : Dict = StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase )
UpperCamelCase : Tuple = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
UpperCamelCase : Any = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
UpperCamelCase : Optional[Any] = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCamelCase , projection_dim=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 , ) )
torch.manual_seed(0 )
UpperCamelCase : Optional[Any] = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCamelCase , layers_per_block=1 , upcast_attention=lowerCamelCase , use_linear_projection=lowerCamelCase , )
torch.manual_seed(0 )
UpperCamelCase : Tuple = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.00085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=lowerCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
UpperCamelCase : Union[str, Any] = AutoencoderKL()
UpperCamelCase : Dict = {
# prior components
"prior_tokenizer": prior_tokenizer,
"prior_text_encoder": prior_text_encoder,
"prior": prior,
"prior_scheduler": prior_scheduler,
# image noising components
"image_normalizer": image_normalizer,
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder,
"unet": unet,
"scheduler": scheduler,
"vae": vae,
}
return components
def SCREAMING_SNAKE_CASE__ ( self , lowerCamelCase , lowerCamelCase=0 ) -> int:
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
UpperCamelCase : Tuple = torch.manual_seed(lowerCamelCase )
else:
UpperCamelCase : str = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
UpperCamelCase : Any = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"prior_num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : Union[str, Any] = torch_device == "cpu"
self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : List[str] = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
UpperCamelCase : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )
UpperCamelCase : Optional[int] = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCamelCase : Tuple = torch.Generator(device="cpu" ).manual_seed(0 )
UpperCamelCase : Tuple = pipe("anime turle" , generator=lowerCamelCase , output_type="np" )
UpperCamelCase : Union[str, Any] = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCamelCase : Any = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
UpperCamelCase : Dict = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCamelCase : Optional[Any] = pipe(
"anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , )
UpperCamelCase : Dict = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 435
| 0
|
"""simple docstring"""
class snake_case__ :
def __init__( self : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase : Any = val
UpperCAmelCase : List[str] = None
UpperCAmelCase : str = None
def __lowerCAmelCase ( self : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
if self.val:
if val < self.val:
if self.left is None:
UpperCAmelCase : Dict = Node(lowercase )
else:
self.left.insert(lowercase )
elif val > self.val:
if self.right is None:
UpperCAmelCase : Dict = Node(lowercase )
else:
self.right.insert(lowercase )
else:
UpperCAmelCase : Optional[Any] = val
def lowercase_ ( _lowercase : Any , _lowercase : str ):
'''simple docstring'''
if root:
inorder(root.left , _lowercase )
res.append(root.val )
inorder(root.right , _lowercase )
def lowercase_ ( _lowercase : List[Any] ):
'''simple docstring'''
if len(_lowercase ) == 0:
return arr
UpperCAmelCase : Union[str, Any] = Node(arr[0] )
for i in range(1 , len(_lowercase ) ):
root.insert(arr[i] )
# Traverse BST in order.
UpperCAmelCase : Optional[Any] = []
inorder(_lowercase , _lowercase )
return res
if __name__ == "__main__":
print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))
| 595
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class snake_case__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE__ = MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE__ = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = pipeline(task="text-generation" , model="sshleifer/tiny-ctrl" , framework="pt" )
# Using `do_sample=False` to force deterministic output
UpperCAmelCase : int = text_generator("This is a test" , do_sample=lowercase )
self.assertEqual(
lowercase , [
{
"generated_text": (
"This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
" oscope. FiliFili@@"
)
}
] , )
UpperCAmelCase : List[Any] = text_generator(["This is a test", "This is a second test"] )
self.assertEqual(
lowercase , [
[
{
"generated_text": (
"This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
" oscope. FiliFili@@"
)
}
],
[
{
"generated_text": (
"This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
" oscope. oscope. FiliFili@@"
)
}
],
] , )
UpperCAmelCase : Any = text_generator("This is a test" , do_sample=lowercase , num_return_sequences=2 , return_tensors=lowercase )
self.assertEqual(
lowercase , [
{"generated_token_ids": ANY(lowercase )},
{"generated_token_ids": ANY(lowercase )},
] , )
UpperCAmelCase : Dict = text_generator.model.config.eos_token_id
UpperCAmelCase : List[str] = "<pad>"
UpperCAmelCase : List[str] = text_generator(
["This is a test", "This is a second test"] , do_sample=lowercase , num_return_sequences=2 , batch_size=2 , return_tensors=lowercase , )
self.assertEqual(
lowercase , [
[
{"generated_token_ids": ANY(lowercase )},
{"generated_token_ids": ANY(lowercase )},
],
[
{"generated_token_ids": ANY(lowercase )},
{"generated_token_ids": ANY(lowercase )},
],
] , )
@require_tf
def __lowerCAmelCase ( self : str ):
'''simple docstring'''
UpperCAmelCase : Tuple = pipeline(task="text-generation" , model="sshleifer/tiny-ctrl" , framework="tf" )
# Using `do_sample=False` to force deterministic output
UpperCAmelCase : Union[str, Any] = text_generator("This is a test" , do_sample=lowercase )
self.assertEqual(
lowercase , [
{
"generated_text": (
"This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
" please,"
)
}
] , )
UpperCAmelCase : List[str] = text_generator(["This is a test", "This is a second test"] , do_sample=lowercase )
self.assertEqual(
lowercase , [
[
{
"generated_text": (
"This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
" please,"
)
}
],
[
{
"generated_text": (
"This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
" Cannes 閲閲Cannes Cannes Cannes 攵 please,"
)
}
],
] , )
def __lowerCAmelCase ( self : str , lowercase : str , lowercase : str , lowercase : str ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = TextGenerationPipeline(model=lowercase , tokenizer=lowercase )
return text_generator, ["This is a test", "Another test"]
def __lowerCAmelCase ( self : int ):
'''simple docstring'''
UpperCAmelCase : Tuple = "Hello I believe in"
UpperCAmelCase : Dict = pipeline("text-generation" , model="hf-internal-testing/tiny-random-gpt2" )
UpperCAmelCase : Union[str, Any] = text_generator(lowercase )
self.assertEqual(
lowercase , [{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}] , )
UpperCAmelCase : Optional[int] = text_generator(lowercase , stop_sequence=" fe" )
self.assertEqual(lowercase , [{"generated_text": "Hello I believe in fe"}] )
def __lowerCAmelCase ( self : str , lowercase : int , lowercase : str ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = text_generator.model
UpperCAmelCase : Tuple = text_generator.tokenizer
UpperCAmelCase : Tuple = text_generator("This is a test" )
self.assertEqual(lowercase , [{"generated_text": ANY(lowercase )}] )
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )
UpperCAmelCase : int = text_generator("This is a test" , return_full_text=lowercase )
self.assertEqual(lowercase , [{"generated_text": ANY(lowercase )}] )
self.assertNotIn("This is a test" , outputs[0]["generated_text"] )
UpperCAmelCase : Tuple = pipeline(task="text-generation" , model=lowercase , tokenizer=lowercase , return_full_text=lowercase )
UpperCAmelCase : Any = text_generator("This is a test" )
self.assertEqual(lowercase , [{"generated_text": ANY(lowercase )}] )
self.assertNotIn("This is a test" , outputs[0]["generated_text"] )
UpperCAmelCase : int = text_generator("This is a test" , return_full_text=lowercase )
self.assertEqual(lowercase , [{"generated_text": ANY(lowercase )}] )
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )
UpperCAmelCase : Union[str, Any] = text_generator(["This is great !", "Something else"] , num_return_sequences=2 , do_sample=lowercase )
self.assertEqual(
lowercase , [
[{"generated_text": ANY(lowercase )}, {"generated_text": ANY(lowercase )}],
[{"generated_text": ANY(lowercase )}, {"generated_text": ANY(lowercase )}],
] , )
if text_generator.tokenizer.pad_token is not None:
UpperCAmelCase : int = text_generator(
["This is great !", "Something else"] , num_return_sequences=2 , batch_size=2 , do_sample=lowercase )
self.assertEqual(
lowercase , [
[{"generated_text": ANY(lowercase )}, {"generated_text": ANY(lowercase )}],
[{"generated_text": ANY(lowercase )}, {"generated_text": ANY(lowercase )}],
] , )
with self.assertRaises(lowercase ):
UpperCAmelCase : Optional[int] = text_generator("test" , return_full_text=lowercase , return_text=lowercase )
with self.assertRaises(lowercase ):
UpperCAmelCase : Tuple = text_generator("test" , return_full_text=lowercase , return_tensors=lowercase )
with self.assertRaises(lowercase ):
UpperCAmelCase : List[Any] = text_generator("test" , return_text=lowercase , return_tensors=lowercase )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
UpperCAmelCase : List[str] = text_generator("" )
self.assertEqual(lowercase , [{"generated_text": ANY(lowercase )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
UpperCAmelCase : Dict = text_generator("" )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
UpperCAmelCase : Union[str, Any] = ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
if (
tokenizer.model_max_length < 1_00_00
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator("This is a test" * 5_00 , max_new_tokens=20 )
UpperCAmelCase : Tuple = text_generator("This is a test" * 5_00 , handle_long_generation="hole" , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(lowercase ):
text_generator(
"This is a test" * 5_00 , handle_long_generation="hole" , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
import torch
# Classic `model_kwargs`
UpperCAmelCase : List[Any] = pipeline(
model="hf-internal-testing/tiny-random-bloom" , model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
UpperCAmelCase : Union[str, Any] = pipe("This is a test" )
self.assertEqual(
lowercase , [
{
"generated_text": (
"This is a test test test test test test test test test test test test test test test test"
" test"
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
UpperCAmelCase : List[Any] = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
UpperCAmelCase : Optional[int] = pipe("This is a test" )
self.assertEqual(
lowercase , [
{
"generated_text": (
"This is a test test test test test test test test test test test test test test test test"
" test"
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
UpperCAmelCase : List[str] = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
UpperCAmelCase : List[Any] = pipe("This is a test" )
self.assertEqual(
lowercase , [
{
"generated_text": (
"This is a test test test test test test test test test test test test test test test test"
" test"
)
}
] , )
@require_torch
@require_torch_gpu
def __lowerCAmelCase ( self : str ):
'''simple docstring'''
import torch
UpperCAmelCase : Tuple = pipeline(model="hf-internal-testing/tiny-random-bloom" , device=0 , torch_dtype=torch.floataa )
pipe("This is a test" )
@require_torch
@require_accelerate
@require_torch_gpu
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
import torch
UpperCAmelCase : Any = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" , torch_dtype=torch.floataa )
pipe("This is a test" , do_sample=lowercase , top_p=0.5 )
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = "Hello world"
UpperCAmelCase : Optional[int] = pipeline("text-generation" , model="hf-internal-testing/tiny-random-gpt2" )
if text_generator.model.framework == "tf":
UpperCAmelCase : Optional[int] = logging.get_logger("transformers.generation.tf_utils" )
else:
UpperCAmelCase : List[str] = logging.get_logger("transformers.generation.utils" )
UpperCAmelCase : List[Any] = "Both `max_new_tokens`" # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(lowercase ) as cl:
UpperCAmelCase : str = text_generator(lowercase , max_length=10 , max_new_tokens=1 )
self.assertIn(lowercase , cl.out )
# The user only sets one -> no warning
with CaptureLogger(lowercase ) as cl:
UpperCAmelCase : List[str] = text_generator(lowercase , max_new_tokens=1 )
self.assertNotIn(lowercase , cl.out )
with CaptureLogger(lowercase ) as cl:
UpperCAmelCase : int = text_generator(lowercase , max_length=10 )
self.assertNotIn(lowercase , cl.out )
| 595
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase :str = logging.get_logger(__name__)
__lowercase :Optional[int] = {
"kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
"kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
"kssteven/ibert-roberta-large-mnli": (
"https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
),
}
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = "ibert"
def __init__( self : Optional[Any] , a : Optional[int]=3_05_22 , a : Tuple=7_68 , a : Tuple=12 , a : List[str]=12 , a : str=30_72 , a : str="gelu" , a : Union[str, Any]=0.1 , a : Any=0.1 , a : Tuple=5_12 , a : Dict=2 , a : str=0.02 , a : Union[str, Any]=1E-12 , a : Tuple=1 , a : List[Any]=0 , a : Optional[Any]=2 , a : List[Any]="absolute" , a : str=False , a : Any="none" , **a : Any , ) ->List[Any]:
super().__init__(pad_token_id=a , bos_token_id=a , eos_token_id=a , **a )
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : str = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : int = max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : List[Any] = position_embedding_type
SCREAMING_SNAKE_CASE__ : Union[str, Any] = quant_mode
SCREAMING_SNAKE_CASE__ : Union[str, Any] = force_dequant
class _a ( lowercase__ ):
"""simple docstring"""
@property
def A_ ( self : Tuple ) ->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ : str = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE__ : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 26
|
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
@require_torch
def A_ ( self : Dict ) ->str:
SCREAMING_SNAKE_CASE__ : Any = pipeline(
task="zero-shot-audio-classification" , model="hf-internal-testing/tiny-clap-htsat-unfused" )
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset("ashraq/esc50" )
SCREAMING_SNAKE_CASE__ : Optional[int] = dataset["train"]["audio"][-1]["array"]
SCREAMING_SNAKE_CASE__ : int = audio_classifier(a , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(a ) , [{"score": 0.501, "label": "Sound of a dog"}, {"score": 0.499, "label": "Sound of vaccum cleaner"}] , )
@unittest.skip("No models are available in TF" )
def A_ ( self : int ) ->Union[str, Any]:
pass
@slow
@require_torch
def A_ ( self : int ) ->str:
SCREAMING_SNAKE_CASE__ : List[str] = pipeline(
task="zero-shot-audio-classification" , model="laion/clap-htsat-unfused" , )
# This is an audio of a dog
SCREAMING_SNAKE_CASE__ : int = load_dataset("ashraq/esc50" )
SCREAMING_SNAKE_CASE__ : str = dataset["train"]["audio"][-1]["array"]
SCREAMING_SNAKE_CASE__ : List[Any] = audio_classifier(a , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(a ) , [
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = audio_classifier([audio] * 5 , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(a ) , [
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 , )
SCREAMING_SNAKE_CASE__ : int = audio_classifier(
[audio] * 5 , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] , batch_size=5 )
self.assertEqual(
nested_simplify(a ) , [
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 , )
@unittest.skip("No models are available in TF" )
def A_ ( self : Optional[int] ) ->Union[str, Any]:
pass
| 26
| 1
|
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
lowerCAmelCase_ : List[str] = ['bert-base-uncased', 'bert-base-cased']
lowerCAmelCase_ : int = 'hf-internal-testing/tiny-bert-tf-only'
if is_tf_available():
class lowerCamelCase_ ( tf.keras.Model ):
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[Any] ):
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE : List[str] = tokenizer
SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_config(lowerCAmelCase__ )
def __lowercase ( self : Tuple , lowerCAmelCase__ : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = self.bert(**lowerCAmelCase__ )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class lowerCamelCase_ ( unittest.TestCase ):
def __lowercase ( self : str ):
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE : int = [
BertTokenizer.from_pretrained(lowerCAmelCase__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
SCREAMING_SNAKE_CASE : Dict = [TFBertTokenizer.from_pretrained(lowerCAmelCase__ ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(lowerCAmelCase__ , use_fast_bert_tokenizer=lowerCAmelCase__ )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
SCREAMING_SNAKE_CASE : Any = [
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
SCREAMING_SNAKE_CASE : int = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def __lowercase ( self : Any ):
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
SCREAMING_SNAKE_CASE : Tuple = tokenizer(lowerCAmelCase__ , return_tensors='''tf''' , padding='''longest''' )
SCREAMING_SNAKE_CASE : List[Any] = tf_tokenizer(lowerCAmelCase__ )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def __lowercase ( self : Dict ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE : Union[str, Any] = tf_tokenizer(self.paired_sentences )
SCREAMING_SNAKE_CASE : Union[str, Any] = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def __lowercase ( self : Union[str, Any] ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE : Any = tf.function(lowerCAmelCase__ )
for test_inputs in (self.test_sentences, self.paired_sentences):
SCREAMING_SNAKE_CASE : int = tf.constant(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = compiled_tokenizer(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = tf_tokenizer(lowerCAmelCase__ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __lowercase ( self : int ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE : int = ModelToSave(tokenizer=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = tf.convert_to_tensor(self.test_sentences )
SCREAMING_SNAKE_CASE : Optional[Any] = model(lowerCAmelCase__ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE : Optional[int] = Path(lowerCAmelCase__ ) / '''saved.model'''
model.save(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.keras.models.load_model(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = loaded_model(lowerCAmelCase__ )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1e-5 )
| 527
|
'''simple docstring'''
from collections import defaultdict
def UpperCAmelCase ( A : int ):
SCREAMING_SNAKE_CASE : List[Any] = 1
SCREAMING_SNAKE_CASE : Dict = True
for v in tree[start]:
if v not in visited:
ret += dfs(A )
if ret % 2 == 0:
cuts.append(A )
return ret
def UpperCAmelCase ( ):
dfs(1 )
if __name__ == "__main__":
lowerCAmelCase_ , lowerCAmelCase_ : Dict = 10, 9
lowerCAmelCase_ : Dict = defaultdict(list)
lowerCAmelCase_ : dict[int, bool] = {}
lowerCAmelCase_ : list[int] = []
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Dict = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 527
| 1
|
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return (pow(lowerCamelCase__ , 2 ) + step) % modulus
for _ in range(lowerCamelCase__ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ = seed
lowerCAmelCase__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ = gcd(hare - tortoise , lowerCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__lowerCAmelCase : Optional[Any] = parser.parse_args()
__lowerCAmelCase : List[str] = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
__lowerCAmelCase : Optional[int] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 714
|
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
while b:
lowerCAmelCase__ , lowerCAmelCase__ = b, a % b
return a
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase__ , a % b )
def _UpperCAmelCase ( ):
"""simple docstring"""
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 674
| 0
|
'''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_bert import BertTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/vocab.txt''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/vocab.txt''',
'''bert-base-multilingual-uncased''': (
'''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'''
),
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
),
'''bert-base-cased-finetuned-mrpc''': (
'''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'''
),
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt''',
'''bert-base-german-dbmdz-uncased''': (
'''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'''
),
'''wietsedv/bert-base-dutch-cased''': (
'''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json''',
'''bert-base-multilingual-uncased''': (
'''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'''
),
'''bert-base-multilingual-cased''': (
'''https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
),
'''bert-base-cased-finetuned-mrpc''': (
'''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'''
),
'''bert-base-german-dbmdz-cased''': (
'''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'''
),
'''bert-base-german-dbmdz-uncased''': (
'''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'''
),
'''wietsedv/bert-base-dutch-cased''': (
'''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''bert-base-uncased''': 512,
'''bert-large-uncased''': 512,
'''bert-base-cased''': 512,
'''bert-large-cased''': 512,
'''bert-base-multilingual-uncased''': 512,
'''bert-base-multilingual-cased''': 512,
'''bert-base-chinese''': 512,
'''bert-base-german-cased''': 512,
'''bert-large-uncased-whole-word-masking''': 512,
'''bert-large-cased-whole-word-masking''': 512,
'''bert-large-uncased-whole-word-masking-finetuned-squad''': 512,
'''bert-large-cased-whole-word-masking-finetuned-squad''': 512,
'''bert-base-cased-finetuned-mrpc''': 512,
'''bert-base-german-dbmdz-cased''': 512,
'''bert-base-german-dbmdz-uncased''': 512,
'''TurkuNLP/bert-base-finnish-cased-v1''': 512,
'''TurkuNLP/bert-base-finnish-uncased-v1''': 512,
'''wietsedv/bert-base-dutch-cased''': 512,
}
__snake_case = {
'''bert-base-uncased''': {'''do_lower_case''': True},
'''bert-large-uncased''': {'''do_lower_case''': True},
'''bert-base-cased''': {'''do_lower_case''': False},
'''bert-large-cased''': {'''do_lower_case''': False},
'''bert-base-multilingual-uncased''': {'''do_lower_case''': True},
'''bert-base-multilingual-cased''': {'''do_lower_case''': False},
'''bert-base-chinese''': {'''do_lower_case''': False},
'''bert-base-german-cased''': {'''do_lower_case''': False},
'''bert-large-uncased-whole-word-masking''': {'''do_lower_case''': True},
'''bert-large-cased-whole-word-masking''': {'''do_lower_case''': False},
'''bert-large-uncased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': True},
'''bert-large-cased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': False},
'''bert-base-cased-finetuned-mrpc''': {'''do_lower_case''': False},
'''bert-base-german-dbmdz-cased''': {'''do_lower_case''': False},
'''bert-base-german-dbmdz-uncased''': {'''do_lower_case''': True},
'''TurkuNLP/bert-base-finnish-cased-v1''': {'''do_lower_case''': False},
'''TurkuNLP/bert-base-finnish-uncased-v1''': {'''do_lower_case''': True},
'''wietsedv/bert-base-dutch-cased''': {'''do_lower_case''': False},
}
class lowercase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_INIT_CONFIGURATION
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = BertTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_="[UNK]" , UpperCamelCase_="[SEP]" , UpperCamelCase_="[PAD]" , UpperCamelCase_="[CLS]" , UpperCamelCase_="[MASK]" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(
__lowerCamelCase , tokenizer_file=__lowerCamelCase , do_lower_case=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , pad_token=__lowerCamelCase , cls_token=__lowerCamelCase , mask_token=__lowerCamelCase , tokenize_chinese_chars=__lowerCamelCase , strip_accents=__lowerCamelCase , **__lowerCamelCase , )
UpperCamelCase__ :Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowerCamelCase ) != tokenize_chinese_chars
):
UpperCamelCase__ :Optional[Any] = getattr(__lowerCamelCase , normalizer_state.pop('''type''' ) )
UpperCamelCase__ :Optional[Any] = do_lower_case
UpperCamelCase__ :str = strip_accents
UpperCamelCase__ :Optional[int] = tokenize_chinese_chars
UpperCamelCase__ :List[Any] = normalizer_class(**__lowerCamelCase )
UpperCamelCase__ :List[Any] = do_lower_case
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :Tuple = [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 lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = [self.sep_token_id]
UpperCamelCase__ :List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :str = self._tokenizer.model.save(__lowerCamelCase , name=__lowerCamelCase )
return tuple(__lowerCamelCase )
| 189
|
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class __snake_case ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = AlbertTokenizer
_lowerCamelCase = AlbertTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
_lowerCamelCase = True
def UpperCamelCase__( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__A : str = AlbertTokenizer(__lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Tuple = '''this is a test'''
__A : Union[str, Any] = '''this is a test'''
return input_text, output_text
def UpperCamelCase__( self ):
'''simple docstring'''
__A : int = '''<pad>'''
__A : Optional[int] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ) , __lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ) , __lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''▁eloquent''' )
self.assertEqual(len(__lowerCamelCase ) , 3_0000 )
def UpperCamelCase__( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase__( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__A : Dict = self.get_tokenizer()
__A : List[Any] = self.get_rust_tokenizer()
__A : int = '''I was born in 92000, and this is falsé.'''
__A : Any = tokenizer.tokenize(__lowerCamelCase )
__A : Dict = rust_tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
__A : List[Any] = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
__A : List[str] = rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
__A : List[Any] = self.get_rust_tokenizer()
__A : Union[str, Any] = tokenizer.encode(__lowerCamelCase )
__A : List[Any] = rust_tokenizer.encode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : List[str] = AlbertTokenizer(__lowerCamelCase , keep_accents=__lowerCamelCase )
__A : Optional[int] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowerCamelCase , ['''▁this''', '''▁is''', '''▁a''', '''▁test'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [48, 25, 21, 1289] )
__A : Tuple = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowerCamelCase , ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''] )
__A : Optional[Any] = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )
__A : Optional[Any] = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase , ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.'''] , )
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Optional[Any] = AlbertTokenizer(__lowerCamelCase )
__A : List[Any] = tokenizer.encode('''sequence builders''' )
__A : str = tokenizer.encode('''multi-sequence build''' )
__A : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
__A : Optional[int] = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase , __lowerCamelCase )
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
]
@slow
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Dict = {'''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''input_ids''': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__lowerCamelCase , model_name='''albert-base-v2''' , revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e''' , )
| 177
| 0
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
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
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def a_ ( self : Tuple ):
"""simple docstring"""
__lowerCamelCase : str = 1
__lowerCamelCase : List[Any] = 3
__lowerCamelCase : Tuple = (32, 32)
__lowerCamelCase : str = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(A__ )
return image
@property
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCamelCase : List[str] = 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 a_ ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCamelCase : List[Any] = 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 a_ ( self : List[str] ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCamelCase : Dict = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5006 , )
return RobertaSeriesModelWithTransformation(A__ )
@property
def a_ ( self : List[str] ):
"""simple docstring"""
def extract(*A__ : List[str] , **A__ : Optional[Any] ):
class SCREAMING_SNAKE_CASE :
def __init__( self : Dict ):
"""simple docstring"""
__lowerCamelCase : Tuple = torch.ones([0] )
def a_ ( self : Optional[int] , A__ : Dict ):
"""simple docstring"""
self.pixel_values.to(A__ )
return self
return Out()
return extract
def a_ ( self : Any ):
"""simple docstring"""
__lowerCamelCase : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase : Union[str, Any] = self.dummy_cond_unet
__lowerCamelCase : Any = PNDMScheduler(skip_prk_steps=A__ )
__lowerCamelCase : List[str] = self.dummy_vae
__lowerCamelCase : Optional[Any] = self.dummy_text_encoder
__lowerCamelCase : List[Any] = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
__lowerCamelCase : Tuple = 77
__lowerCamelCase : int = self.dummy_image.to(A__ )
__lowerCamelCase : Optional[Any] = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
__lowerCamelCase : str = AltDiffusionImgaImgPipeline(
unet=A__ , scheduler=A__ , vae=A__ , text_encoder=A__ , tokenizer=A__ , safety_checker=A__ , feature_extractor=self.dummy_extractor , )
__lowerCamelCase : int = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=A__ )
__lowerCamelCase : str = alt_pipe.to(A__ )
alt_pipe.set_progress_bar_config(disable=A__ )
__lowerCamelCase : Any = """A painting of a squirrel eating a burger"""
__lowerCamelCase : List[Any] = torch.Generator(device=A__ ).manual_seed(0 )
__lowerCamelCase : List[str] = alt_pipe(
[prompt] , generator=A__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=A__ , )
__lowerCamelCase : Tuple = output.images
__lowerCamelCase : int = torch.Generator(device=A__ ).manual_seed(0 )
__lowerCamelCase : str = alt_pipe(
[prompt] , generator=A__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , image=A__ , return_dict=A__ , )[0]
__lowerCamelCase : int = image[0, -3:, -3:, -1]
__lowerCamelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase : int = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5e-3
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = self.dummy_cond_unet
__lowerCamelCase : Optional[int] = PNDMScheduler(skip_prk_steps=A__ )
__lowerCamelCase : Dict = self.dummy_vae
__lowerCamelCase : Optional[int] = self.dummy_text_encoder
__lowerCamelCase : int = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
__lowerCamelCase : List[Any] = 77
__lowerCamelCase : int = self.dummy_image.to(A__ )
# put models in fp16
__lowerCamelCase : List[str] = unet.half()
__lowerCamelCase : Optional[Any] = vae.half()
__lowerCamelCase : Tuple = bert.half()
# make sure here that pndm scheduler skips prk
__lowerCamelCase : List[Any] = AltDiffusionImgaImgPipeline(
unet=A__ , scheduler=A__ , vae=A__ , text_encoder=A__ , tokenizer=A__ , safety_checker=A__ , feature_extractor=self.dummy_extractor , )
__lowerCamelCase : Optional[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=A__ )
__lowerCamelCase : int = alt_pipe.to(A__ )
alt_pipe.set_progress_bar_config(disable=A__ )
__lowerCamelCase : Optional[int] = """A painting of a squirrel eating a burger"""
__lowerCamelCase : int = torch.manual_seed(0 )
__lowerCamelCase : Optional[Any] = alt_pipe(
[prompt] , generator=A__ , num_inference_steps=2 , output_type="""np""" , image=A__ , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def a_ ( self : str ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
# resize to resolution that is divisible by 8 but not 16 or 32
__lowerCamelCase : str = init_image.resize((760, 504) )
__lowerCamelCase : Any = """BAAI/AltDiffusion"""
__lowerCamelCase : str = AltDiffusionImgaImgPipeline.from_pretrained(
A__ , safety_checker=A__ , )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
pipe.enable_attention_slicing()
__lowerCamelCase : Dict = """A fantasy landscape, trending on artstation"""
__lowerCamelCase : str = torch.manual_seed(0 )
__lowerCamelCase : List[str] = pipe(
prompt=A__ , image=A__ , strength=0.75 , guidance_scale=7.5 , generator=A__ , output_type="""np""" , )
__lowerCamelCase : Optional[Any] = output.images[0]
__lowerCamelCase : Tuple = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
__lowerCamelCase : Dict = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def a_ ( self : Any ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
__lowerCamelCase : int = init_image.resize((768, 512) )
__lowerCamelCase : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""" )
__lowerCamelCase : Dict = """BAAI/AltDiffusion"""
__lowerCamelCase : List[str] = AltDiffusionImgaImgPipeline.from_pretrained(
A__ , safety_checker=A__ , )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
pipe.enable_attention_slicing()
__lowerCamelCase : int = """A fantasy landscape, trending on artstation"""
__lowerCamelCase : Optional[Any] = torch.manual_seed(0 )
__lowerCamelCase : str = pipe(
prompt=A__ , image=A__ , strength=0.75 , guidance_scale=7.5 , generator=A__ , output_type="""np""" , )
__lowerCamelCase : List[Any] = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1e-2
| 483
|
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :Optional[int] = logging.get_logger(__name__)
UpperCAmelCase__ :List[Any] = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
snake_case__ : Dict = 'sew-d'
def __init__( self : Tuple , A__ : Optional[int]=32 , A__ : Optional[int]=768 , A__ : Any=12 , A__ : List[str]=12 , A__ : List[Any]=3072 , A__ : str=2 , A__ : Dict=512 , A__ : Optional[Any]=256 , A__ : Optional[Any]=True , A__ : Any=True , A__ : List[str]=("p2c", "c2p") , A__ : List[Any]="layer_norm" , A__ : Union[str, Any]="gelu_python" , A__ : int=0.1 , A__ : Dict=0.1 , A__ : int=0.1 , A__ : Dict=0.0 , A__ : Optional[Any]=0.1 , A__ : Dict=0.02 , A__ : Dict=1e-7 , A__ : List[Any]=1e-5 , A__ : Any="group" , A__ : Any="gelu" , A__ : Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , A__ : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , A__ : str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , A__ : Optional[Any]=False , A__ : Any=128 , A__ : Optional[Any]=16 , A__ : Union[str, Any]=True , A__ : Any=0.05 , A__ : List[str]=10 , A__ : Union[str, Any]=2 , A__ : Dict=0.0 , A__ : str=10 , A__ : Tuple=0 , A__ : Any="mean" , A__ : Optional[int]=False , A__ : int=False , A__ : List[str]=256 , A__ : Union[str, Any]=0 , A__ : int=1 , A__ : Optional[Any]=2 , **A__ : List[Any] , ):
"""simple docstring"""
super().__init__(**A__ , pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ )
__lowerCamelCase : List[str] = hidden_size
__lowerCamelCase : Tuple = feat_extract_norm
__lowerCamelCase : Tuple = feat_extract_activation
__lowerCamelCase : List[Any] = list(A__ )
__lowerCamelCase : int = list(A__ )
__lowerCamelCase : Optional[Any] = list(A__ )
__lowerCamelCase : Tuple = conv_bias
__lowerCamelCase : List[str] = num_conv_pos_embeddings
__lowerCamelCase : Tuple = num_conv_pos_embedding_groups
__lowerCamelCase : int = len(self.conv_dim )
__lowerCamelCase : Optional[int] = num_hidden_layers
__lowerCamelCase : Dict = intermediate_size
__lowerCamelCase : Optional[int] = squeeze_factor
__lowerCamelCase : Union[str, Any] = max_position_embeddings
__lowerCamelCase : int = position_buckets
__lowerCamelCase : Tuple = share_att_key
__lowerCamelCase : Any = relative_attention
__lowerCamelCase : Any = norm_rel_ebd
__lowerCamelCase : Dict = list(A__ )
__lowerCamelCase : Optional[Any] = hidden_act
__lowerCamelCase : List[str] = num_attention_heads
__lowerCamelCase : List[str] = hidden_dropout
__lowerCamelCase : Union[str, Any] = attention_dropout
__lowerCamelCase : Tuple = activation_dropout
__lowerCamelCase : Union[str, Any] = feat_proj_dropout
__lowerCamelCase : Union[str, Any] = final_dropout
__lowerCamelCase : List[str] = layer_norm_eps
__lowerCamelCase : Tuple = feature_layer_norm_eps
__lowerCamelCase : Union[str, Any] = initializer_range
__lowerCamelCase : List[str] = vocab_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)`,"""
f"but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"
f"= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase : Any = apply_spec_augment
__lowerCamelCase : str = mask_time_prob
__lowerCamelCase : Tuple = mask_time_length
__lowerCamelCase : Optional[int] = mask_time_min_masks
__lowerCamelCase : Dict = mask_feature_prob
__lowerCamelCase : Optional[Any] = mask_feature_length
__lowerCamelCase : str = mask_feature_min_masks
# ctc loss
__lowerCamelCase : Any = ctc_loss_reduction
__lowerCamelCase : str = ctc_zero_infinity
# sequence classification
__lowerCamelCase : Dict = use_weighted_layer_sum
__lowerCamelCase : List[Any] = classifier_proj_size
@property
def a_ ( self : Optional[int] ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 483
| 1
|
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A = logging.get_logger(__name__)
def lowerCamelCase_ ( UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Union[int, Iterable[int]] , UpperCamelCase__ : bool , UpperCamelCase__ : int ) -> Tuple[int, int]:
"""simple docstring"""
def constraint_to_multiple_of(UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Optional[Any]=0 , UpperCamelCase__ : Optional[Any]=None ):
__lowerCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
__lowerCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
__lowerCamelCase = math.ceil(val / multiple ) * multiple
return x
__lowerCamelCase = (output_size, output_size) if isinstance(__UpperCamelCase , __UpperCamelCase ) else output_size
__lowerCamelCase , __lowerCamelCase = get_image_size(__UpperCamelCase )
__lowerCamelCase , __lowerCamelCase = output_size
# determine new height and width
__lowerCamelCase = output_height / input_height
__lowerCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
__lowerCamelCase = scale_width
else:
# fit height
__lowerCamelCase = scale_height
__lowerCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__UpperCamelCase )
__lowerCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__UpperCamelCase )
return (new_height, new_width)
class __lowerCAmelCase ( _A ):
"""simple docstring"""
snake_case_ = ['pixel_values']
def __init__( self , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = PILImageResampling.BILINEAR , lowerCamelCase__ = False , lowerCamelCase__ = 1 , lowerCamelCase__ = True , lowerCamelCase__ = 1 / 255 , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__snake_case )
__lowerCamelCase = size if size is not None else {'height': 384, 'width': 384}
__lowerCamelCase = get_size_dict(__snake_case )
__lowerCamelCase = do_resize
__lowerCamelCase = size
__lowerCamelCase = keep_aspect_ratio
__lowerCamelCase = ensure_multiple_of
__lowerCamelCase = resample
__lowerCamelCase = do_rescale
__lowerCamelCase = rescale_factor
__lowerCamelCase = do_normalize
__lowerCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__lowerCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False , lowerCamelCase__ = 1 , lowerCamelCase__ = PILImageResampling.BICUBIC , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}""" )
__lowerCamelCase = get_resize_output_image_size(
__snake_case , output_size=(size['height'], size['width']) , keep_aspect_ratio=__snake_case , multiple=__snake_case , )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Dict:
'''simple docstring'''
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Dict:
'''simple docstring'''
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = ChannelDimension.FIRST , **lowerCamelCase__ , ) -> int:
'''simple docstring'''
__lowerCamelCase = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase = size if size is not None else self.size
__lowerCamelCase = get_size_dict(__snake_case )
__lowerCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
__lowerCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
__lowerCamelCase = resample if resample is not None else self.resample
__lowerCamelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase = image_std if image_std is not None else self.image_std
__lowerCamelCase = make_list_of_images(__snake_case )
if not valid_images(__snake_case ):
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 or resample is None:
raise ValueError('Size and resample must be specified if do_resize 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.
__lowerCamelCase = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
__lowerCamelCase = [self.resize(image=__snake_case , size=__snake_case , resample=__snake_case ) for image in images]
if do_rescale:
__lowerCamelCase = [self.rescale(image=__snake_case , scale=__snake_case ) for image in images]
if do_normalize:
__lowerCamelCase = [self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case ) for image in images]
__lowerCamelCase = [to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
__lowerCamelCase = {'pixel_values': images}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> int:
'''simple docstring'''
__lowerCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__snake_case ) != len(__snake_case ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(__snake_case ):
__lowerCamelCase = target_sizes.numpy()
__lowerCamelCase = []
for idx in range(len(__snake_case ) ):
__lowerCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=__snake_case )
__lowerCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__snake_case )
else:
__lowerCamelCase = logits.argmax(dim=1 )
__lowerCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 469
|
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] ) -> Optional[Any]:
if height >= 1:
move_tower(height - 1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
move_disk(__UpperCamelCase , __UpperCamelCase )
move_tower(height - 1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] , __UpperCamelCase : Union[str, Any] ) -> List[str]:
print('''moving disk from''' , __UpperCamelCase , '''to''' , __UpperCamelCase )
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
UpperCAmelCase_ = int(input('''Height of hanoi: ''' ).strip() )
move_tower(__UpperCamelCase , '''A''' , '''B''' , '''C''' )
if __name__ == "__main__":
main()
| 144
| 0
|
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __a :
'''simple docstring'''
UpperCAmelCase__ : List[str]
UpperCAmelCase__ : Optional[str] = None
# Automatically constructed
UpperCAmelCase__ : ClassVar[str] = "dict"
UpperCAmelCase__ : ClassVar[Any] = None
UpperCAmelCase__ : str = field(default="""Translation""" , init=__A , repr=__A )
def __call__( self ):
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def __snake_case ( self ):
from .features import Value
return {k: Value('string' ) for k in sorted(self.languages )}
@dataclass
class __a :
'''simple docstring'''
UpperCAmelCase__ : Optional[List] = None
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : Optional[str] = None
# Automatically constructed
UpperCAmelCase__ : ClassVar[str] = "dict"
UpperCAmelCase__ : ClassVar[Any] = None
UpperCAmelCase__ : str = field(default="""TranslationVariableLanguages""" , init=__A , repr=__A )
def __snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Any = sorted(set(self.languages ) ) if self.languages else None
SCREAMING_SNAKE_CASE_ : Dict = len(self.languages ) if self.languages else None
def __call__( self ):
return pa.struct({'language': pa.list_(pa.string() ), 'translation': pa.list_(pa.string() )} )
def __snake_case ( self , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = set(self.languages )
if self.languages and set(UpperCamelCase__ ) - lang_set:
raise ValueError(
F'''Some languages in example ({', '.join(sorted(set(UpperCamelCase__ ) - lang_set ) )}) are not in valid set ({', '.join(UpperCamelCase__ )}).''' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
SCREAMING_SNAKE_CASE_ : List[str] = []
for lang, text in translation_dict.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = zip(*sorted(UpperCamelCase__ ) )
return {"language": languages, "translation": translations}
def __snake_case ( self ):
from .features import Sequence, Value
return {
"language": Sequence(Value('string' ) ),
"translation": Sequence(Value('string' ) ),
}
| 97
|
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __a ( __A ):
'''simple docstring'''
def __init__( self , *UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ):
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = eval_examples
SCREAMING_SNAKE_CASE_ : int = post_process_function
def __snake_case ( self , UpperCamelCase__ = None , UpperCamelCase__=None , UpperCamelCase__ = None , UpperCamelCase__ = "eval" , **UpperCamelCase__ , ):
SCREAMING_SNAKE_CASE_ : int = gen_kwargs.copy()
SCREAMING_SNAKE_CASE_ : Tuple = (
gen_kwargs['max_length'] if gen_kwargs.get('max_length' ) is not None else self.args.generation_max_length
)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
gen_kwargs['num_beams'] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = gen_kwargs
SCREAMING_SNAKE_CASE_ : Tuple = self.eval_dataset if eval_dataset is None else eval_dataset
SCREAMING_SNAKE_CASE_ : int = self.get_eval_dataloader(UpperCamelCase__ )
SCREAMING_SNAKE_CASE_ : Any = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.compute_metrics
SCREAMING_SNAKE_CASE_ : Optional[Any] = None
SCREAMING_SNAKE_CASE_ : Union[str, Any] = time.time()
SCREAMING_SNAKE_CASE_ : int = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE_ : List[Any] = eval_loop(
UpperCamelCase__ , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , metric_key_prefix=UpperCamelCase__ , )
finally:
SCREAMING_SNAKE_CASE_ : Optional[int] = compute_metrics
SCREAMING_SNAKE_CASE_ : Dict = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
UpperCamelCase__ , UpperCamelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
SCREAMING_SNAKE_CASE_ : List[str] = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = metrics.pop(UpperCamelCase__ )
metrics.update(output.metrics )
else:
SCREAMING_SNAKE_CASE_ : Tuple = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(UpperCamelCase__ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
SCREAMING_SNAKE_CASE_ : Dict = self.callback_handler.on_evaluate(self.args , self.state , self.control , UpperCamelCase__ )
return metrics
def __snake_case ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__ = "test" , **UpperCamelCase__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = gen_kwargs.copy()
SCREAMING_SNAKE_CASE_ : Any = self.get_test_dataloader(UpperCamelCase__ )
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE_ : Tuple = self.compute_metrics
SCREAMING_SNAKE_CASE_ : str = None
SCREAMING_SNAKE_CASE_ : Optional[Any] = time.time()
SCREAMING_SNAKE_CASE_ : List[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE_ : List[str] = eval_loop(
UpperCamelCase__ , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , metric_key_prefix=UpperCamelCase__ , )
finally:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = compute_metrics
SCREAMING_SNAKE_CASE_ : List[str] = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
UpperCamelCase__ , UpperCamelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
SCREAMING_SNAKE_CASE_ : Optional[int] = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , 'predict' )
SCREAMING_SNAKE_CASE_ : List[Any] = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
SCREAMING_SNAKE_CASE_ : Optional[int] = metrics.pop(UpperCamelCase__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=UpperCamelCase__ )
| 97
| 1
|
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=None , _lowerCAmelCase : int=None , _lowerCAmelCase : List[Any]="resnet50" , _lowerCAmelCase : List[Any]=3 , _lowerCAmelCase : str=3_2 , _lowerCAmelCase : str=3 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : List[Any]=True , ):
'''simple docstring'''
__lowercase =parent
__lowercase =out_indices if out_indices is not None else [4]
__lowercase =stage_names
__lowercase =out_features
__lowercase =backbone
__lowercase =batch_size
__lowercase =image_size
__lowercase =num_channels
__lowercase =use_pretrained_backbone
__lowercase =is_training
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__lowercase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__lowercase =self.get_config()
return config, pixel_values
def __lowerCamelCase ( self : str):
'''simple docstring'''
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def __lowerCamelCase ( self : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any]):
'''simple docstring'''
__lowercase =TimmBackbone(config=_lowerCAmelCase)
model.to(_lowerCAmelCase)
model.eval()
with torch.no_grad():
__lowercase =model(_lowerCAmelCase)
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 1_4, 1_4) , )
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase =self.prepare_config_and_inputs()
__lowercase , __lowercase =config_and_inputs
__lowercase ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class _UpperCamelCase ( A , A , A , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = (TimmBackbone,) if is_torch_available() else ()
lowerCAmelCase__ = {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def __lowerCamelCase ( self : int):
'''simple docstring'''
__lowercase =TimmBackboneModelTester(self)
__lowercase =ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase)
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase ='resnet18'
__lowercase ='microsoft/resnet-18'
__lowercase =AutoBackbone.from_pretrained(_lowerCAmelCase , use_timm_backbone=_lowerCAmelCase)
__lowercase =AutoBackbone.from_pretrained(_lowerCAmelCase)
self.assertEqual(len(timm_model.out_features) , len(transformers_model.out_features))
self.assertEqual(len(timm_model.stage_names) , len(transformers_model.stage_names))
self.assertEqual(timm_model.channels , transformers_model.channels)
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,))
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names) - 1])
__lowercase =AutoBackbone.from_pretrained(_lowerCAmelCase , use_timm_backbone=_lowerCAmelCase , out_indices=[1, 2, 3])
__lowercase =AutoBackbone.from_pretrained(_lowerCAmelCase , out_indices=[1, 2, 3])
self.assertEqual(timm_model.out_indices , transformers_model.out_indices)
self.assertEqual(len(timm_model.out_features) , len(transformers_model.out_features))
self.assertEqual(timm_model.channels , transformers_model.channels)
@unittest.skip('TimmBackbone doesn\'t support feed forward chunking')
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute')
def __lowerCamelCase ( self : Any):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone initialization is managed on the timm side')
def __lowerCamelCase ( self : int):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
def __lowerCamelCase ( self : int):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint')
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.')
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.')
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
def __lowerCamelCase ( self : Dict):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.')
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
pass
@unittest.skip('TimmBackbone doesn\'t support output_attentions.')
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
pass
@unittest.skip('Safetensors is not supported by timm.')
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def __lowerCamelCase ( self : Any):
'''simple docstring'''
pass
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
__lowercase , __lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase =model_class(_lowerCAmelCase)
__lowercase =inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase =[*signature.parameters.keys()]
__lowercase =['pixel_values']
self.assertListEqual(arg_names[:1] , _lowerCAmelCase)
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase , __lowercase =self.model_tester.prepare_config_and_inputs_for_common()
__lowercase =True
__lowercase =self.has_attentions
# no need to test all models as different heads yield the same functionality
__lowercase =self.all_model_classes[0]
__lowercase =model_class(_lowerCAmelCase)
model.to(_lowerCAmelCase)
__lowercase =self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase)
__lowercase =model(**_lowerCAmelCase)
__lowercase =outputs[0][-1]
# Encoder-/Decoder-only models
__lowercase =outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
__lowercase =outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=_lowerCAmelCase)
self.assertIsNotNone(hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(attentions.grad)
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
__lowercase , __lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase =model_class(_lowerCAmelCase)
model.to(_lowerCAmelCase)
model.eval()
__lowercase =model(**_lowerCAmelCase)
self.assertEqual(len(result.feature_maps) , len(config.out_indices))
self.assertEqual(len(model.channels) , len(config.out_indices))
# Check output of last stage is taken if out_features=None, out_indices=None
__lowercase =copy.deepcopy(_lowerCAmelCase)
__lowercase =None
__lowercase =model_class(_lowerCAmelCase)
model.to(_lowerCAmelCase)
model.eval()
__lowercase =model(**_lowerCAmelCase)
self.assertEqual(len(result.feature_maps) , 1)
self.assertEqual(len(model.channels) , 1)
# Check backbone can be initialized with fresh weights
__lowercase =copy.deepcopy(_lowerCAmelCase)
__lowercase =False
__lowercase =model_class(_lowerCAmelCase)
model.to(_lowerCAmelCase)
model.eval()
__lowercase =model(**_lowerCAmelCase)
| 474
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = """microsoft/speecht5_tts"""
lowerCAmelCase__ = (
"""This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
"""text to read (in English) and returns a waveform object containing the sound."""
)
lowerCAmelCase__ = """text_reader"""
lowerCAmelCase__ = SpeechTaProcessor
lowerCAmelCase__ = SpeechTaForTextToSpeech
lowerCAmelCase__ = SpeechTaHifiGan
lowerCAmelCase__ = ["""text"""]
lowerCAmelCase__ = ["""audio"""]
def __lowerCamelCase ( self : Any):
'''simple docstring'''
if self.post_processor is None:
__lowercase ='microsoft/speecht5_hifigan'
super().setup()
def __lowerCamelCase ( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : List[Any]=None):
'''simple docstring'''
__lowercase =self.pre_processor(text=_lowerCAmelCase , return_tensors='pt' , truncation=_lowerCAmelCase)
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError('Datasets needs to be installed if not passing speaker embeddings.')
__lowercase =load_dataset('Matthijs/cmu-arctic-xvectors' , split='validation')
__lowercase =torch.tensor(embeddings_dataset[7_3_0_5]['xvector']).unsqueeze(0)
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def __lowerCamelCase ( self : Optional[Any] , _lowerCAmelCase : Dict):
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**_lowerCAmelCase)
def __lowerCamelCase ( self : Any , _lowerCAmelCase : int):
'''simple docstring'''
with torch.no_grad():
return self.post_processor(_lowerCAmelCase).cpu().detach()
| 474
| 1
|
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
UpperCAmelCase_ = '''0.12''' # assumed parallelism: 8
@require_flax
@is_staging_test
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def lowerCAmelCase ( cls : Any ):
"""simple docstring"""
_UpperCamelCase: Union[str, Any] = TOKEN
HfFolder.save_token(_lowercase )
@classmethod
def lowerCAmelCase ( cls : str ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-model-flax''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' )
except HTTPError:
pass
def lowerCAmelCase ( self : int ):
"""simple docstring"""
_UpperCamelCase: int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
_UpperCamelCase: int = FlaxBertModel(_lowercase )
model.push_to_hub('''test-model-flax''' , use_auth_token=self._token )
_UpperCamelCase: Dict = FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax""" )
_UpperCamelCase: Any = flatten_dict(unfreeze(model.params ) )
_UpperCamelCase: Dict = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_UpperCamelCase: int = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1E-3 , msg=f"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''test-model-flax''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_lowercase , repo_id='''test-model-flax''' , push_to_hub=_lowercase , use_auth_token=self._token )
_UpperCamelCase: Tuple = FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax""" )
_UpperCamelCase: str = flatten_dict(unfreeze(model.params ) )
_UpperCamelCase: Any = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_UpperCamelCase: List[Any] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1E-3 , msg=f"""{key} not identical""" )
def lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
_UpperCamelCase: List[str] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
_UpperCamelCase: Optional[Any] = FlaxBertModel(_lowercase )
model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token )
_UpperCamelCase: Any = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
_UpperCamelCase: List[str] = flatten_dict(unfreeze(model.params ) )
_UpperCamelCase: Any = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_UpperCamelCase: Tuple = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1E-3 , msg=f"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
_lowercase , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=_lowercase , use_auth_token=self._token )
_UpperCamelCase: List[Any] = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
_UpperCamelCase: Optional[Any] = flatten_dict(unfreeze(model.params ) )
_UpperCamelCase: List[str] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_UpperCamelCase: int = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1E-3 , msg=f"""{key} not identical""" )
def lowerCAmelCase_ ( lowercase: Any , lowercase: Optional[int] ) -> Any:
'''simple docstring'''
_UpperCamelCase: int = True
_UpperCamelCase: Any = flatten_dict(modela.params )
_UpperCamelCase: Any = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
_UpperCamelCase: str = False
return models_are_equal
@require_flax
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Dict ):
"""simple docstring"""
_UpperCamelCase: Union[str, Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
_UpperCamelCase: Optional[int] = FlaxBertModel(_lowercase )
_UpperCamelCase: Union[str, Any] = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_lowercase , _lowercase ) )
with self.assertRaises(_lowercase ):
_UpperCamelCase: List[str] = FlaxBertModel.from_pretrained(_lowercase )
_UpperCamelCase: Any = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertTrue(check_models_equal(_lowercase , _lowercase ) )
def lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
_UpperCamelCase: int = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
_UpperCamelCase: Union[str, Any] = FlaxBertModel(_lowercase )
_UpperCamelCase: Dict = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_lowercase , _lowercase ) , max_shard_size='''10KB''' )
with self.assertRaises(_lowercase ):
_UpperCamelCase: List[str] = FlaxBertModel.from_pretrained(_lowercase )
_UpperCamelCase: Union[str, Any] = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertTrue(check_models_equal(_lowercase , _lowercase ) )
def lowerCAmelCase ( self : Any ):
"""simple docstring"""
_UpperCamelCase: List[Any] = '''bert'''
_UpperCamelCase: List[str] = '''hf-internal-testing/tiny-random-bert-subfolder'''
with self.assertRaises(_lowercase ):
_UpperCamelCase: List[Any] = FlaxBertModel.from_pretrained(_lowercase )
_UpperCamelCase: Dict = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertIsNotNone(_lowercase )
def lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
_UpperCamelCase: str = '''bert'''
_UpperCamelCase: Optional[Any] = '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
with self.assertRaises(_lowercase ):
_UpperCamelCase: Union[str, Any] = FlaxBertModel.from_pretrained(_lowercase )
_UpperCamelCase: str = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertIsNotNone(_lowercase )
| 264
|
from __future__ import annotations
UpperCAmelCase_ = 1.6_0_2_1E-1_9 # units = C
def lowerCAmelCase_ ( lowercase: float , lowercase: float , lowercase: float , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 264
| 1
|
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class UpperCamelCase_ :
def __init__( self : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any]=99 , lowerCAmelCase_ : Optional[Any]=13 , lowerCAmelCase_ : List[str]=7 , lowerCAmelCase_ : List[str]=9 , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : int=False , lowerCAmelCase_ : Tuple=32 , lowerCAmelCase_ : Optional[Any]=5 , lowerCAmelCase_ : List[Any]=4 , lowerCAmelCase_ : List[Any]=37 , lowerCAmelCase_ : Dict=8 , lowerCAmelCase_ : int=0.1 , lowerCAmelCase_ : Dict=0.0_0_2 , lowerCAmelCase_ : Dict=1 , lowerCAmelCase_ : Tuple=0 , lowerCAmelCase_ : List[str]=0 , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : str=None , ) -> List[Any]:
UpperCAmelCase_ : int = parent
UpperCAmelCase_ : Union[str, Any] = batch_size
UpperCAmelCase_ : Dict = encoder_seq_length
UpperCAmelCase_ : Tuple = decoder_seq_length
# For common tests
UpperCAmelCase_ : Optional[int] = self.decoder_seq_length
UpperCAmelCase_ : Optional[int] = is_training
UpperCAmelCase_ : List[str] = use_attention_mask
UpperCAmelCase_ : Tuple = use_labels
UpperCAmelCase_ : Optional[int] = vocab_size
UpperCAmelCase_ : Union[str, Any] = hidden_size
UpperCAmelCase_ : Optional[Any] = num_hidden_layers
UpperCAmelCase_ : List[str] = num_attention_heads
UpperCAmelCase_ : Optional[Any] = d_ff
UpperCAmelCase_ : Dict = relative_attention_num_buckets
UpperCAmelCase_ : int = dropout_rate
UpperCAmelCase_ : Optional[int] = initializer_factor
UpperCAmelCase_ : str = eos_token_id
UpperCAmelCase_ : Optional[Any] = pad_token_id
UpperCAmelCase_ : Dict = decoder_start_token_id
UpperCAmelCase_ : Union[str, Any] = None
UpperCAmelCase_ : Optional[Any] = decoder_layers
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
return TaConfig.from_pretrained("google/umt5-base" )
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : List[Any]=None , ) -> Union[str, Any]:
if attention_mask is None:
UpperCAmelCase_ : int = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
UpperCAmelCase_ : List[str] = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
UpperCAmelCase_ : List[Any] = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=__lowercase )
if decoder_head_mask is None:
UpperCAmelCase_ : Tuple = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=__lowercase )
if cross_attn_head_mask is None:
UpperCAmelCase_ : List[str] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=__lowercase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
UpperCAmelCase_ : Union[str, Any] = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
UpperCAmelCase_ : int = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
UpperCAmelCase_ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase_ : int = decoder_input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase_ : Tuple = self.get_config()
UpperCAmelCase_ : List[str] = config.num_attention_heads
UpperCAmelCase_ : List[str] = self.prepare_inputs_dict(__lowercase , __lowercase , __lowercase )
return config, input_dict
def _SCREAMING_SNAKE_CASE ( self : int ) -> Tuple:
UpperCAmelCase_ : List[str] = self.prepare_config_and_inputs()
return config, inputs_dict
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[int]:
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _SCREAMING_SNAKE_CASE ( self : str ) -> int:
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , ) -> Optional[int]:
UpperCAmelCase_ : Optional[Any] = UMTaModel(config=__lowercase )
model.to(__lowercase )
model.eval()
UpperCAmelCase_ : List[str] = model(
input_ids=__lowercase , decoder_input_ids=__lowercase , attention_mask=__lowercase , decoder_attention_mask=__lowercase , )
UpperCAmelCase_ : Tuple = model(input_ids=__lowercase , decoder_input_ids=__lowercase )
UpperCAmelCase_ : Optional[int] = result.last_hidden_state
UpperCAmelCase_ : Dict = result.past_key_values
UpperCAmelCase_ : Dict = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(__lowercase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int , ) -> Optional[Any]:
UpperCAmelCase_ : str = UMTaModel(config=__lowercase ).get_decoder().to(__lowercase ).eval()
# first forward pass
UpperCAmelCase_ : Tuple = model(__lowercase , use_cache=__lowercase )
UpperCAmelCase_ : Union[str, Any] = model(__lowercase )
UpperCAmelCase_ : Optional[int] = model(__lowercase , use_cache=__lowercase )
self.parent.assertTrue(len(__lowercase ) == len(__lowercase ) )
self.parent.assertTrue(len(__lowercase ) == len(__lowercase ) + 1 )
UpperCAmelCase_ : int = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase_ : Union[str, Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
UpperCAmelCase_ : List[str] = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase_ : Tuple = model(__lowercase )['''last_hidden_state''']
UpperCAmelCase_ : Tuple = model(__lowercase , past_key_values=__lowercase )['''last_hidden_state''']
# select random slice
UpperCAmelCase_ : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase_ : Any = output_from_no_past[:, -1, random_slice_idx].detach()
UpperCAmelCase_ : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowercase , __lowercase , atol=1e-3 ) )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int , ) -> Optional[int]:
UpperCAmelCase_ : Any = UMTaModel(config=__lowercase ).to(__lowercase ).half().eval()
UpperCAmelCase_ : List[str] = model(**__lowercase )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(__lowercase ).any().item() )
@require_torch
class UpperCamelCase_ (__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__magic_name__ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
__magic_name__ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
__magic_name__ = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = True
__magic_name__ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
__magic_name__ = [0.8, 0.9]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
UpperCAmelCase_ : int = UMTaModelTester(self )
@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Any:
UpperCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : Optional[Any] = UMTaModel(config_and_inputs[0] ).to(__lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__lowercase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f"""{tmpdirname}/t5_test.onnx""" , export_params=__lowercase , opset_version=9 , input_names=["input_ids", "decoder_input_ids"] , )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
UpperCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__lowercase )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
UpperCAmelCase_ : int = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : int = config_and_inputs[0]
UpperCAmelCase_ : Optional[int] = UMTaForConditionalGeneration(__lowercase ).eval()
model.to(__lowercase )
UpperCAmelCase_ : str = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=__lowercase ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__lowercase ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__lowercase ),
}
for attn_name, (name, mask) in zip(__lowercase , head_masking.items() ):
UpperCAmelCase_ : int = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
UpperCAmelCase_ : Any = torch.ones(
config.num_decoder_layers , config.num_heads , device=__lowercase )
UpperCAmelCase_ : Union[str, Any] = model.generate(
config_and_inputs[1]["input_ids"] , num_beams=1 , max_length=3 , output_attentions=__lowercase , return_dict_in_generate=__lowercase , **__lowercase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
UpperCAmelCase_ : Optional[Any] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase_ (unittest.TestCase ):
@slow
@unittest.skip(
"Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
UpperCAmelCase_ : Tuple = UMTaForConditionalGeneration.from_pretrained("google/umt5-small" , return_dict=__lowercase ).to(__lowercase )
UpperCAmelCase_ : Any = AutoTokenizer.from_pretrained("google/umt5-small" , use_fast=__lowercase , legacy=__lowercase )
UpperCAmelCase_ : str = [
'''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
'''No se como puedo <extra_id_0>.''',
'''This is the reason why we <extra_id_0> them.''',
'''The <extra_id_0> walks in <extra_id_1>, seats''',
'''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
]
UpperCAmelCase_ : Union[str, Any] = tokenizer(__lowercase , return_tensors="pt" , padding=__lowercase ).input_ids
# fmt: off
UpperCAmelCase_ : List[Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(__lowercase , __lowercase )
UpperCAmelCase_ : Any = model.generate(input_ids.to(__lowercase ) )
UpperCAmelCase_ : str = [
'''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
'''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
]
UpperCAmelCase_ : List[Any] = tokenizer.batch_decode(__lowercase )
self.assertEqual(__lowercase , __lowercase )
| 95
|
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase__ :Tuple = logging.get_logger(__name__)
lowercase__ :List[Any] = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : Optional[int] = 'mctct'
def __init__( self : List[Any] , __lowercase : Optional[int]=8_065 , __lowercase : Union[str, Any]=1_536 , __lowercase : str=36 , __lowercase : Optional[int]=6_144 , __lowercase : Union[str, Any]=4 , __lowercase : str=384 , __lowercase : str=920 , __lowercase : List[str]=1e-5 , __lowercase : str=0.3 , __lowercase : Union[str, Any]="relu" , __lowercase : List[str]=0.0_2 , __lowercase : List[Any]=0.3 , __lowercase : Tuple=0.3 , __lowercase : int=1 , __lowercase : str=0 , __lowercase : Union[str, Any]=2 , __lowercase : Optional[Any]=1 , __lowercase : Any=0.3 , __lowercase : int=1 , __lowercase : Optional[Any]=(7,) , __lowercase : List[str]=(3,) , __lowercase : int=80 , __lowercase : Any=1 , __lowercase : Union[str, Any]=None , __lowercase : Optional[Any]="sum" , __lowercase : int=False , **__lowercase : Tuple , ):
'''simple docstring'''
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase )
__UpperCAmelCase : List[Any] = vocab_size
__UpperCAmelCase : Any = hidden_size
__UpperCAmelCase : Optional[Any] = num_hidden_layers
__UpperCAmelCase : List[str] = intermediate_size
__UpperCAmelCase : int = num_attention_heads
__UpperCAmelCase : Tuple = attention_head_dim
__UpperCAmelCase : List[str] = max_position_embeddings
__UpperCAmelCase : List[str] = layer_norm_eps
__UpperCAmelCase : Optional[int] = layerdrop
__UpperCAmelCase : List[Any] = hidden_act
__UpperCAmelCase : Optional[Any] = initializer_range
__UpperCAmelCase : Optional[Any] = hidden_dropout_prob
__UpperCAmelCase : List[str] = attention_probs_dropout_prob
__UpperCAmelCase : Dict = pad_token_id
__UpperCAmelCase : Union[str, Any] = bos_token_id
__UpperCAmelCase : Optional[int] = eos_token_id
__UpperCAmelCase : Optional[Any] = conv_glu_dim
__UpperCAmelCase : List[str] = conv_dropout
__UpperCAmelCase : str = num_conv_layers
__UpperCAmelCase : int = input_feat_per_channel
__UpperCAmelCase : Any = input_channels
__UpperCAmelCase : int = conv_channels
__UpperCAmelCase : List[Any] = ctc_loss_reduction
__UpperCAmelCase : Union[str, Any] = ctc_zero_infinity
# prevents config testing fail with exporting to json
__UpperCAmelCase : str = list(__lowercase )
__UpperCAmelCase : Union[str, Any] = list(__lowercase )
if len(self.conv_kernel ) != self.num_conv_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
f'''but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, '''
f'''`config.num_conv_layers = {self.num_conv_layers}`.''' )
| 522
| 0
|
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
_UpperCamelCase : List[Any] = float("""nan""")
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , UpperCAmelCase )-> Union[str, Any]:
__A = sys.stdout
__A = open(UpperCAmelCase , '''a''' )
def __getattr__( self , UpperCAmelCase )-> Union[str, Any]:
return getattr(self.stdout , UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase )-> Optional[int]:
self.stdout.write(UpperCAmelCase )
# strip tqdm codes
self.file.write(re.sub(r'''^.*\r''' , '''''' , UpperCAmelCase , 0 , re.M ) )
def __UpperCamelCase ( snake_case=8_0 , snake_case=False ) -> Optional[Any]:
'''simple docstring'''
__A = []
# deal with critical env vars
__A = ['''CUDA_VISIBLE_DEVICES''']
for key in env_keys:
__A = os.environ.get(snake_case , snake_case )
if val is not None:
cmd.append(F"{key}={val}" )
# python executable (not always needed if the script is executable)
__A = sys.executable if full_python_path else sys.executable.split('''/''' )[-1]
cmd.append(snake_case )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
__A = []
__A = ''''''
while len(snake_case ) > 0:
current_line += F"{cmd.pop(0 )} "
if len(snake_case ) == 0 or len(snake_case ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(snake_case )
__A = ''''''
return "\\\n".join(snake_case )
def __UpperCamelCase ( snake_case , snake_case ) -> List[Any]:
'''simple docstring'''
__A = re.sub(R'''[\\\n]+''' , ''' ''' , args.base_cmd )
# remove --output_dir if any and set our own
__A = re.sub('''--output_dir\s+[^\s]+''' , '''''' , args.base_cmd )
args.base_cmd += F" --output_dir {output_dir}"
# ensure we have --overwrite_output_dir
__A = re.sub('''--overwrite_output_dir\s+''' , '''''' , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def __UpperCamelCase ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Optional[int]:
'''simple docstring'''
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 1_0_0 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6_666, 222.22_222_222] )} , )
__A = subprocess.run(snake_case , capture_output=snake_case , text=snake_case )
if verbose:
print('''STDOUT''' , result.stdout )
print('''STDERR''' , result.stderr )
# save the streams
__A = variation.replace(''' ''' , '''-''' )
with open(Path(snake_case ) / F"log.{prefix}.stdout.txt" , '''w''' ) as f:
f.write(result.stdout )
with open(Path(snake_case ) / F"log.{prefix}.stderr.txt" , '''w''' ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print('''failed''' )
return {target_metric_key: nan}
with io.open(F"{output_dir}/all_results.json" , '''r''' , encoding='''utf-8''' ) as f:
__A = json.load(snake_case )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def __UpperCamelCase ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ) -> Optional[int]:
'''simple docstring'''
__A = []
__A = []
__A = F"{id}: {variation:<{longest_variation_len}}"
__A = F"{preamble}: "
__A = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(snake_case ) , desc=snake_case , leave=snake_case ):
__A = process_run_single(
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case )
__A = single_run_metrics[target_metric_key]
if not math.isnan(snake_case ):
metrics.append(snake_case )
results.append(snake_case )
outcome += "✓"
else:
outcome += "✘"
__A = F"\33[2K\r{outcome}"
if len(snake_case ) > 0:
__A = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
__A = round(mean_metrics[target_metric_key] , 2 )
__A = F"{outcome} {mean_target}"
if len(snake_case ) > 1:
results_str += F" {tuple(round(snake_case , 2 ) for x in results )}"
print(snake_case )
__A = variation
return mean_metrics
else:
print(snake_case )
return {variation_key: variation, target_metric_key: nan}
def __UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
__A = torch.cuda.get_device_properties(torch.device('''cuda''' ) )
return F"\nDatetime : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch : {torch.__version__}\ncuda : {torch.version.cuda}\npython : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**3_0:0.2f}GB\n"
def __UpperCamelCase ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> Tuple:
'''simple docstring'''
__A = pd.DataFrame(snake_case )
__A = '''variation'''
__A = '''diff_%'''
__A = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
__A = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(snake_case ):
# as a fallback, use the minimal value as the sentinel
__A = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(snake_case ):
__A = df.apply(
lambda snake_case : round(1_0_0 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis='''columns''' , )
# re-order columns
__A = [variation_key, target_metric_key, diff_key, *report_metric_keys]
__A = df.reindex(snake_case , axis='''columns''' ) # reorder cols
# capitalize
__A = df.rename(str.capitalize , axis='''columns''' )
# make the cols as narrow as possible
__A = df.rename(lambda snake_case : c.replace('''_''' , '''<br>''' ) , axis='''columns''' )
__A = df.rename(lambda snake_case : c.replace('''_''' , '''\n''' ) , axis='''columns''' )
__A = ['''''', '''Copy between the cut-here-lines and paste as is to github or a forum''']
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=snake_case , floatfmt='''.2f''' )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=snake_case , floatfmt='''.2f''' )]
print('''\n\n'''.join(snake_case ) )
def __UpperCamelCase ( ) -> int:
'''simple docstring'''
__A = argparse.ArgumentParser()
parser.add_argument(
'''--base-cmd''' , default=snake_case , type=snake_case , required=snake_case , help='''Base cmd''' , )
parser.add_argument(
'''--variations''' , default=snake_case , type=snake_case , nargs='''+''' , required=snake_case , help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'''' , )
parser.add_argument(
'''--base-variation''' , default=snake_case , type=snake_case , help='''Baseline variation to compare to. if None the minimal target value will be used to compare against''' , )
parser.add_argument(
'''--target-metric-key''' , default=snake_case , type=snake_case , required=snake_case , help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second''' , )
parser.add_argument(
'''--report-metric-keys''' , default='''''' , type=snake_case , help='''Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples''' , )
parser.add_argument(
'''--repeat-times''' , default=1 , type=snake_case , help='''How many times to re-run each variation - an average will be reported''' , )
parser.add_argument(
'''--output_dir''' , default='''output_benchmark''' , type=snake_case , help='''The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked''' , )
parser.add_argument(
'''--verbose''' , default=snake_case , action='''store_true''' , help='''Whether to show the outputs of each run or just the benchmark progress''' , )
__A = parser.parse_args()
__A = args.output_dir
Path(snake_case ).mkdir(exist_ok=snake_case )
__A = get_base_command(snake_case , snake_case )
# split each dimension into its --foo variations
__A = [list(map(str.strip , re.split(R'''\|''' , snake_case ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
__A = list(map(str.strip , map(''' '''.join , itertools.product(*snake_case ) ) ) )
__A = max(len(snake_case ) for x in variations )
# split wanted keys
__A = args.report_metric_keys.split()
# capture prints into a log file for convenience
__A = F"benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S' )}.txt"
print(F"\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt" )
print(F"and this script's output is also piped into {report_fn}" )
__A = Tee(snake_case )
print(F"\n*** Running {len(snake_case )} benchmarks:" )
print(F"Base command: {' '.join(snake_case )}" )
__A = '''variation'''
__A = []
for id, variation in enumerate(tqdm(snake_case , desc='''Total completion: ''' , leave=snake_case ) ):
__A = base_cmd + variation.split()
results.append(
process_run(
id + 1 , snake_case , snake_case , snake_case , snake_case , args.target_metric_key , snake_case , args.repeat_times , snake_case , args.verbose , ) )
process_results(snake_case , args.target_metric_key , snake_case , args.base_variation , snake_case )
if __name__ == "__main__":
main()
| 341
|
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase : List[Any] = logging.get_logger()
@dataclass
class _lowerCAmelCase:
"""simple docstring"""
lowerCamelCase__ = 42
lowerCamelCase__ = field(default_factory=_a)
lowerCamelCase__ = field(default_factory=_a)
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> str:
__A = len(list(m.modules() ) ) == 1 or isinstance(UpperCAmelCase , nn.Convad ) or isinstance(UpperCAmelCase , nn.BatchNormad )
if has_not_submodules:
self.traced.append(UpperCAmelCase )
def __call__( self , UpperCAmelCase )-> int:
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(UpperCAmelCase )
[x.remove() for x in self.handles]
return self
@property
def SCREAMING_SNAKE_CASE__ ( self )-> List[str]:
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda UpperCAmelCase : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class _lowerCAmelCase:
"""simple docstring"""
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = 1
lowerCamelCase__ = field(default_factory=_a)
lowerCamelCase__ = field(default_factory=_a)
lowerCamelCase__ = True
def __call__( self , UpperCAmelCase )-> Optional[Any]:
__A = Tracker(self.dest )(UpperCAmelCase ).parametrized
__A = Tracker(self.src )(UpperCAmelCase ).parametrized
__A = list(filter(lambda UpperCAmelCase : type(UpperCAmelCase ) not in self.src_skip , UpperCAmelCase ) )
__A = list(filter(lambda UpperCAmelCase : type(UpperCAmelCase ) not in self.dest_skip , UpperCAmelCase ) )
if len(UpperCAmelCase ) != len(UpperCAmelCase ) and self.raise_if_mismatch:
raise Exception(
f"Numbers of operations are different. Source module has {len(UpperCAmelCase )} operations while"
f" destination module has {len(UpperCAmelCase )}." )
for dest_m, src_m in zip(UpperCAmelCase , UpperCAmelCase ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"Transfered from={src_m} to={dest_m}" )
class _lowerCAmelCase( nn.Module):
"""simple docstring"""
def __init__( self , UpperCAmelCase )-> Dict:
super().__init__()
__A = []
# - get the stem
feature_blocks.append(('''conv1''', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('''block''' ), f"Unexpected layer name {k}"
__A = len(UpperCAmelCase ) + 1
feature_blocks.append((f"res{block_index}", v) )
__A = nn.ModuleDict(UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase )-> List[Any]:
return get_trunk_forward_outputs(
UpperCAmelCase , out_feat_keys=UpperCAmelCase , feature_blocks=self._feature_blocks , )
class _lowerCAmelCase( _a):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase )-> str:
__A = x.split('''-''' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self , UpperCAmelCase )-> Callable[[], Tuple[nn.Module, Dict]]:
# default to timm!
if x not in self:
__A = self.convert_name_to_timm(UpperCAmelCase )
__A = partial(lambda: (timm.create_model(UpperCAmelCase , pretrained=UpperCAmelCase ).eval(), None) )
else:
__A = super().__getitem__(UpperCAmelCase )
return val
class _lowerCAmelCase( _a):
"""simple docstring"""
def __getitem__( self , UpperCAmelCase )-> Callable[[], nn.Module]:
if "seer" in x and "in1k" not in x:
__A = RegNetModel
else:
__A = RegNetForImageClassification
return val
def __UpperCamelCase ( snake_case , snake_case , snake_case ) -> int:
'''simple docstring'''
for from_key, to_key in keys:
__A = from_state_dict[from_key].clone()
print(F"Copied key={from_key} to={to_key}" )
return to_state_dict
def __UpperCamelCase ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case = True , ) -> Optional[int]:
'''simple docstring'''
print(F"Converting {name}..." )
with torch.no_grad():
__A , __A = from_model_func()
__A = our_model_func(snake_case ).eval()
__A = ModuleTransfer(src=snake_case , dest=snake_case , raise_if_mismatch=snake_case )
__A = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(snake_case )
if from_state_dict is not None:
__A = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
__A = [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
__A = manually_copy_vissl_head(snake_case , our_model.state_dict() , snake_case )
our_model.load_state_dict(snake_case )
__A = our_model(snake_case , output_hidden_states=snake_case )
__A = (
our_outputs.logits if isinstance(snake_case , snake_case ) else our_outputs.last_hidden_state
)
__A = from_model(snake_case )
__A = from_output[-1] if type(snake_case ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
__A = our_outputs.hidden_states[-1]
assert torch.allclose(snake_case , snake_case ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='''Add model''' , use_temp_dir=snake_case , )
__A = 2_2_4 if '''seer''' not in name else 3_8_4
# we can use the convnext one
__A = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' , size=snake_case )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='''Add image processor''' , use_temp_dir=snake_case , )
print(F"Pushed {name}" )
def __UpperCamelCase ( snake_case , snake_case = None , snake_case = True ) -> Union[str, Any]:
'''simple docstring'''
__A = '''imagenet-1k-id2label.json'''
__A = 1_0_0_0
__A = (1, num_labels)
__A = '''huggingface/label-files'''
__A = num_labels
__A = json.load(open(cached_download(hf_hub_url(snake_case , snake_case , repo_type='''dataset''' ) ) , '''r''' ) )
__A = {int(snake_case ): v for k, v in idalabel.items()}
__A = idalabel
__A = {v: k for k, v in idalabel.items()}
__A = partial(snake_case , num_labels=snake_case , idalabel=snake_case , labelaid=snake_case )
__A = {
'''regnet-x-002''': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8] , groups_width=8 , layer_type='''x''' ),
'''regnet-x-004''': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 1_2] , hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4] , groups_width=1_6 , layer_type='''x''' ),
'''regnet-x-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8] , groups_width=2_4 , layer_type='''x''' ),
'''regnet-x-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2] , groups_width=1_6 , layer_type='''x''' ),
'''regnet-x-016''': ImageNetPreTrainedConfig(
depths=[2, 4, 1_0, 2] , hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2] , groups_width=2_4 , layer_type='''x''' ),
'''regnet-x-032''': ImageNetPreTrainedConfig(
depths=[2, 6, 1_5, 2] , hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8] , groups_width=4_8 , layer_type='''x''' ),
'''regnet-x-040''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_4, 2] , hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0] , groups_width=4_0 , layer_type='''x''' ),
'''regnet-x-064''': ImageNetPreTrainedConfig(
depths=[2, 4, 1_0, 1] , hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4] , groups_width=5_6 , layer_type='''x''' ),
'''regnet-x-080''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_5, 1] , hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0] , groups_width=1_2_0 , layer_type='''x''' ),
'''regnet-x-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_1, 1] , hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0] , groups_width=1_1_2 , layer_type='''x''' ),
'''regnet-x-160''': ImageNetPreTrainedConfig(
depths=[2, 6, 1_3, 1] , hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8] , groups_width=1_2_8 , layer_type='''x''' ),
'''regnet-x-320''': ImageNetPreTrainedConfig(
depths=[2, 7, 1_3, 1] , hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0] , groups_width=1_6_8 , layer_type='''x''' ),
# y variant
'''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8] , groups_width=8 ),
'''regnet-y-004''': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0] , groups_width=8 ),
'''regnet-y-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8] , groups_width=1_6 ),
'''regnet-y-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8] , groups_width=1_6 ),
'''regnet-y-016''': ImageNetPreTrainedConfig(
depths=[2, 6, 1_7, 2] , hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8] , groups_width=2_4 ),
'''regnet-y-032''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_3, 1] , hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2] , groups_width=2_4 ),
'''regnet-y-040''': ImageNetPreTrainedConfig(
depths=[2, 6, 1_2, 2] , hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8] , groups_width=6_4 ),
'''regnet-y-064''': ImageNetPreTrainedConfig(
depths=[2, 7, 1_4, 2] , hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6] , groups_width=7_2 ),
'''regnet-y-080''': ImageNetPreTrainedConfig(
depths=[2, 4, 1_0, 1] , hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6] , groups_width=5_6 ),
'''regnet-y-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_1, 1] , hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0] , groups_width=1_1_2 ),
'''regnet-y-160''': ImageNetPreTrainedConfig(
depths=[2, 4, 1_1, 1] , hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4] , groups_width=1_1_2 ),
'''regnet-y-320''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_2, 1] , hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2] , groups_width=2_3_2 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 1_2, 1] , hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2] , groups_width=2_3_2 ),
'''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 1_2, 1] , hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0] , groups_width=3_2_8 ),
'''regnet-y-1280-seer''': RegNetConfig(
depths=[2, 7, 1_7, 1] , hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2] , groups_width=2_6_4 ),
'''regnet-y-2560-seer''': RegNetConfig(
depths=[3, 7, 1_6, 1] , hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8] , groups_width=6_4_0 ),
'''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
depths=[2, 7, 1_7, 1] , hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] , groups_width=1_0_1_0 ),
# finetuned on imagenet
'''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_2, 1] , hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2] , groups_width=2_3_2 ),
'''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 1_2, 1] , hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0] , groups_width=3_2_8 ),
'''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 1_7, 1] , hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2] , groups_width=2_6_4 ),
'''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
depths=[3, 7, 1_6, 1] , hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8] , groups_width=6_4_0 ),
'''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 1_7, 1] , hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] , groups_width=1_0_1_0 ),
}
__A = NameToOurModelFuncMap()
__A = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(snake_case , snake_case ) -> Tuple[nn.Module, Dict]:
__A = torch.hub.load_state_dict_from_url(snake_case , model_dir=str(snake_case ) , map_location='''cpu''' )
__A = model_func()
# check if we have a head, if yes add it
__A = files['''classy_state_dict''']['''base_model''']['''model''']
__A = model_state_dict['''trunk''']
model.load_state_dict(snake_case )
return model.eval(), model_state_dict["heads"]
# pretrained
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=2_7 , group_width=1_0_1_0 , w_a=1_7_4_4 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
__A = partial(
snake_case , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=2_7 , group_width=1_0_1_0 , w_a=1_7_4_4 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
snake_case , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , snake_case , snake_case , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
snake_case , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , snake_case , snake_case , snake_case , )
return config, expected_shape
if __name__ == "__main__":
_UpperCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
""" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
_UpperCamelCase : List[str] = parser.parse_args()
_UpperCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 341
| 1
|
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _snake_case ( _A , _A ):
@register_to_config
def __init__( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ) -> List[str]:
super().__init__()
snake_case__ :List[str] = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
snake_case__ :str = torch.zeros(UpperCamelCase ,UpperCamelCase )
else:
snake_case__ :List[Any] = None
snake_case__ :Union[str, Any] = torch.nn.Parameter(UpperCamelCase )
class _snake_case ( _A ):
_A = 42
_A = 42
_A = 42
_A = 42
_A = 42
_A = 42
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,) -> Optional[int]:
super().__init__()
self.register_modules(
vqvae=UpperCamelCase ,transformer=UpperCamelCase ,text_encoder=UpperCamelCase ,tokenizer=UpperCamelCase ,scheduler=UpperCamelCase ,learned_classifier_free_sampling_embeddings=UpperCamelCase ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> str:
snake_case__ :int = len(UpperCamelCase ) if isinstance(UpperCamelCase ,UpperCamelCase ) else 1
# get prompt text embeddings
snake_case__ :Any = self.tokenizer(
UpperCamelCase ,padding="max_length" ,max_length=self.tokenizer.model_max_length ,return_tensors="pt" ,)
snake_case__ :List[str] = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
snake_case__ :int = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
snake_case__ :str = text_input_ids[:, : self.tokenizer.model_max_length]
snake_case__ :Optional[Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
snake_case__ :List[Any] = prompt_embeds / prompt_embeds.norm(dim=-1 ,keepdim=UpperCamelCase )
# duplicate text embeddings for each generation per prompt
snake_case__ :Tuple = prompt_embeds.repeat_interleave(UpperCamelCase ,dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
snake_case__ :List[Any] = self.learned_classifier_free_sampling_embeddings.embeddings
snake_case__ :List[str] = negative_prompt_embeds.unsqueeze(0 ).repeat(UpperCamelCase ,1 ,1 )
else:
snake_case__ :Any = [""] * batch_size
snake_case__ :List[str] = text_input_ids.shape[-1]
snake_case__ :str = self.tokenizer(
UpperCamelCase ,padding="max_length" ,max_length=UpperCamelCase ,truncation=UpperCamelCase ,return_tensors="pt" ,)
snake_case__ :List[Any] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
snake_case__ :List[str] = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 ,keepdim=UpperCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case__ :Dict = negative_prompt_embeds.shape[1]
snake_case__ :int = negative_prompt_embeds.repeat(1 ,UpperCamelCase ,1 )
snake_case__ :Dict = negative_prompt_embeds.view(batch_size * num_images_per_prompt ,UpperCamelCase ,-1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case__ :Dict = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self ,UpperCamelCase ,UpperCamelCase = 100 ,UpperCamelCase = 5.0 ,UpperCamelCase = 1.0 ,UpperCamelCase = 1 ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = "pil" ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = 1 ,) -> Union[ImagePipelineOutput, Tuple]:
if isinstance(UpperCamelCase ,UpperCamelCase ):
snake_case__ :int = 1
elif isinstance(UpperCamelCase ,UpperCamelCase ):
snake_case__ :List[str] = len(UpperCamelCase )
else:
raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase )}' )
snake_case__ :List[str] = batch_size * num_images_per_prompt
snake_case__ :Optional[int] = guidance_scale > 1.0
snake_case__ :List[Any] = self._encode_prompt(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(UpperCamelCase ,UpperCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
f' {type(UpperCamelCase )}.' )
# get the initial completely masked latents unless the user supplied it
snake_case__ :str = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
snake_case__ :List[Any] = self.transformer.num_vector_embeds - 1
snake_case__ :Any = torch.full(UpperCamelCase ,UpperCamelCase ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
"Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
f' {self.transformer.num_vector_embeds - 1} (inclusive).' )
snake_case__ :Tuple = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(UpperCamelCase ,device=self.device )
snake_case__ :List[str] = self.scheduler.timesteps.to(self.device )
snake_case__ :Any = latents
for i, t in enumerate(self.progress_bar(UpperCamelCase ) ):
# expand the sample if we are doing classifier free guidance
snake_case__ :Any = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
snake_case__ :List[str] = self.transformer(UpperCamelCase ,encoder_hidden_states=UpperCamelCase ,timestep=UpperCamelCase ).sample
if do_classifier_free_guidance:
snake_case__ , snake_case__ :List[str] = model_output.chunk(2 )
snake_case__ :str = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(UpperCamelCase ,dim=1 ,keepdim=UpperCamelCase )
snake_case__ :Optional[Any] = self.truncate(UpperCamelCase ,UpperCamelCase )
# remove `log(0)`'s (`-inf`s)
snake_case__ :List[Any] = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
snake_case__ :Optional[Any] = self.scheduler.step(UpperCamelCase ,timestep=UpperCamelCase ,sample=UpperCamelCase ,generator=UpperCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
snake_case__ :str = self.vqvae.config.vq_embed_dim
snake_case__ :Optional[Any] = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
snake_case__ :Optional[int] = self.vqvae.quantize.get_codebook_entry(UpperCamelCase ,shape=UpperCamelCase )
snake_case__ :Any = self.vqvae.decode(UpperCamelCase ,force_not_quantize=UpperCamelCase ).sample
snake_case__ :List[str] = (image / 2 + 0.5).clamp(0 ,1 )
snake_case__ :Any = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
snake_case__ :Dict = self.numpy_to_pil(UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> torch.FloatTensor:
snake_case__ , snake_case__ :int = torch.sort(UpperCamelCase ,1 ,descending=UpperCamelCase )
snake_case__ :str = torch.exp(UpperCamelCase )
snake_case__ :Optional[Any] = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
snake_case__ :Optional[int] = torch.full_like(keep_mask[:, 0:1, :] ,UpperCamelCase )
snake_case__ :int = torch.cat((all_true, keep_mask) ,dim=1 )
snake_case__ :Tuple = keep_mask[:, :-1, :]
snake_case__ :Any = keep_mask.gather(1 ,indices.argsort(1 ) )
snake_case__ :Optional[Any] = log_p_x_0.clone()
snake_case__ :str = -torch.inf # -inf = log(0)
return rv
| 241
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__UpperCAmelCase : Optional[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[Any] = ["GPTNeoXTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Union[str, Any] = [
"GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXLayer",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 241
| 1
|
"""simple docstring"""
from __future__ import annotations
import math
class lowercase_ :
def __init__( self , a_ ) ->List[Any]:
'''simple docstring'''
_a = size
# approximate the overall size of segment tree with given value
_a = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
_a = [0 for i in range(0 , 4 * size )]
_a = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowerCamelCase__ ( self , a_ ) ->Optional[int]:
'''simple docstring'''
return idx * 2
def lowerCamelCase__ ( self , a_ ) ->Tuple:
'''simple docstring'''
return idx * 2 + 1
def lowerCamelCase__ ( self , a_ , a_ , a_ , a_ ) ->Union[str, Any]:
'''simple docstring'''
if left_element == right_element:
_a = a[left_element - 1]
else:
_a = (left_element + right_element) // 2
self.build(self.left(_lowercase ) , _lowercase , _lowercase , _lowercase )
self.build(self.right(_lowercase ) , mid + 1 , _lowercase , _lowercase )
_a = max(
self.segment_tree[self.left(_lowercase )] , self.segment_tree[self.right(_lowercase )] )
def lowerCamelCase__ ( self , a_ , a_ , a_ , a_ , a_ , a_ ) ->str:
'''simple docstring'''
if self.flag[idx] is True:
_a = self.lazy[idx]
_a = False
if left_element != right_element:
_a = self.lazy[idx]
_a = self.lazy[idx]
_a = True
_a = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
_a = val
if left_element != right_element:
_a = val
_a = val
_a = True
_a = True
return True
_a = (left_element + right_element) // 2
self.update(self.left(_lowercase ) , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
self.update(self.right(_lowercase ) , mid + 1 , _lowercase , _lowercase , _lowercase , _lowercase )
_a = max(
self.segment_tree[self.left(_lowercase )] , self.segment_tree[self.right(_lowercase )] )
return True
def lowerCamelCase__ ( self , a_ , a_ , a_ , a_ , a_ ) ->Dict:
'''simple docstring'''
if self.flag[idx] is True:
_a = self.lazy[idx]
_a = False
if left_element != right_element:
_a = self.lazy[idx]
_a = self.lazy[idx]
_a = True
_a = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
_a = (left_element + right_element) // 2
_a = self.query(self.left(_lowercase ) , _lowercase , _lowercase , _lowercase , _lowercase )
_a = self.query(self.right(_lowercase ) , mid + 1 , _lowercase , _lowercase , _lowercase )
return max(_lowercase , _lowercase )
def __str__( self ) ->Optional[Any]:
'''simple docstring'''
return str([self.query(1 , 1 , self.size , _lowercase , _lowercase ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
UpperCamelCase = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
UpperCamelCase = 15
UpperCamelCase = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| 703
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowercase_ (_UpperCAmelCase ):
def __init__( self , *a_ , **a_ ) ->None:
'''simple docstring'''
warnings.warn(
"The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use YolosImageProcessor instead." , a_ , )
super().__init__(*a_ , **a_ )
| 612
| 0
|
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : Optional[int] ) -> Optional[int]:
__A : List[str] = 0
__A : Optional[int] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _lowerCAmelCase ( __snake_case : Tuple ) -> Optional[Any]:
if len(__snake_case ) <= 1:
return arr, 0
__A : List[Any] = len(__snake_case ) // 2
__A : int = arr[0:mid]
__A : List[Any] = arr[mid:]
__A ,__A : Optional[Any] = count_inversions_recursive(__snake_case )
__A ,__A : Any = count_inversions_recursive(__snake_case )
__A ,__A : str = _count_cross_inversions(__snake_case , __snake_case )
__A : int = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _lowerCAmelCase ( __snake_case : Union[str, Any] , __snake_case : List[str] ) -> Tuple:
__A : List[str] = []
__A : Optional[int] = 0
while i < len(__snake_case ) and j < len(__snake_case ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__snake_case ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__snake_case ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _lowerCAmelCase ( ) -> Any:
__A : Optional[Any] = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__A : Tuple = count_inversions_bf(__snake_case )
__A ,__A : Optional[Any] = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' , __snake_case )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__A : str = count_inversions_bf(__snake_case )
__A ,__A : Any = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , __snake_case )
# an empty list should also have zero inversions
__A : Union[str, Any] = []
__A : List[Any] = count_inversions_bf(__snake_case )
__A ,__A : List[Any] = count_inversions_recursive(__snake_case )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , __snake_case )
if __name__ == "__main__":
main()
| 8
|
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 548
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
a_ : List[str] = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
a_ : List[Any] = TaTokenizerFast
a_ : str = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Union[str, Any] = [
'MT5EncoderModel',
'MT5ForConditionalGeneration',
'MT5ForQuestionAnswering',
'MT5Model',
'MT5PreTrainedModel',
'MT5Stack',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Union[str, Any] = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Tuple = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
a_ : int = _LazyModule(
__name__,
globals()['__file__'],
_import_structure,
extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
module_spec=__spec__,
)
| 707
|
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
def _a (self ):
'''simple docstring'''
lowerCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
lowerCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
lowerCamelCase = "xvjiarui/stable-diffusion-2-inpainting"
lowerCamelCase , lowerCamelCase = FlaxStableDiffusionInpaintPipeline.from_pretrained(__a , safety_checker=__a )
lowerCamelCase = "Face of a yellow cat, high resolution, sitting on a park bench"
lowerCamelCase = jax.random.PRNGKey(0 )
lowerCamelCase = 50
lowerCamelCase = jax.device_count()
lowerCamelCase = num_samples * [prompt]
lowerCamelCase = num_samples * [init_image]
lowerCamelCase = num_samples * [mask_image]
lowerCamelCase , lowerCamelCase , lowerCamelCase = pipeline.prepare_inputs(__a , __a , __a )
# shard inputs and rng
lowerCamelCase = replicate(__a )
lowerCamelCase = jax.random.split(__a , jax.device_count() )
lowerCamelCase = shard(__a )
lowerCamelCase = shard(__a )
lowerCamelCase = shard(__a )
lowerCamelCase = pipeline(
__a , __a , __a , __a , __a , __a , jit=__a )
lowerCamelCase = output.images.reshape(__a , 5_12 , 5_12 , 3 )
lowerCamelCase = images[0, 2_53:2_56, 2_53:2_56, -1]
lowerCamelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCamelCase = jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084] )
print(F"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 484
| 0
|
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_UpperCAmelCase = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :List[str] , SCREAMING_SNAKE_CASE :List[str] ) -> List[str]:
__lowerCAmelCase : List[str] = b.T
__lowerCAmelCase : List[str] = np.sum(np.square(SCREAMING_SNAKE_CASE ) , axis=1 )
__lowerCAmelCase : Optional[Any] = np.sum(np.square(SCREAMING_SNAKE_CASE ) , axis=0 )
__lowerCAmelCase : Dict = np.matmul(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Tuple , SCREAMING_SNAKE_CASE :List[Any] ) -> Union[str, Any]:
__lowerCAmelCase : List[str] = x.reshape(-1 , 3 )
__lowerCAmelCase : Tuple = squared_euclidean_distance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return np.argmin(SCREAMING_SNAKE_CASE , axis=1 )
class snake_case_ ( __lowercase ):
A_ = ['pixel_values']
def __init__( self : Optional[int] , _snake_case : Optional[Union[List[List[int]], np.ndarray]] = None , _snake_case : bool = True , _snake_case : Dict[str, int] = None , _snake_case : PILImageResampling = PILImageResampling.BILINEAR , _snake_case : bool = True , _snake_case : bool = True , **_snake_case : str , )->None:
'''simple docstring'''
super().__init__(**_snake_case )
__lowerCAmelCase : Optional[Any] = size if size is not None else {"""height""": 256, """width""": 256}
__lowerCAmelCase : List[Any] = get_size_dict(_snake_case )
__lowerCAmelCase : int = np.array(_snake_case ) if clusters is not None else None
__lowerCAmelCase : List[str] = do_resize
__lowerCAmelCase : List[str] = size
__lowerCAmelCase : Optional[Any] = resample
__lowerCAmelCase : Optional[int] = do_normalize
__lowerCAmelCase : Dict = do_color_quantize
def UpperCAmelCase__ ( self : Union[str, Any] , _snake_case : np.ndarray , _snake_case : Dict[str, int] , _snake_case : PILImageResampling = PILImageResampling.BILINEAR , _snake_case : Optional[Union[str, ChannelDimension]] = None , **_snake_case : List[Any] , )->np.ndarray:
'''simple docstring'''
__lowerCAmelCase : Tuple = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}''' )
return resize(
_snake_case , size=(size["""height"""], size["""width"""]) , resample=_snake_case , data_format=_snake_case , **_snake_case )
def UpperCAmelCase__ ( self : List[Any] , _snake_case : np.ndarray , _snake_case : Optional[Union[str, ChannelDimension]] = None , )->np.ndarray:
'''simple docstring'''
__lowerCAmelCase : Tuple = rescale(image=_snake_case , scale=1 / 127.5 , data_format=_snake_case )
__lowerCAmelCase : str = image - 1
return image
def UpperCAmelCase__ ( self : List[Any] , _snake_case : ImageInput , _snake_case : bool = None , _snake_case : Dict[str, int] = None , _snake_case : PILImageResampling = None , _snake_case : bool = None , _snake_case : Optional[bool] = None , _snake_case : Optional[Union[List[List[int]], np.ndarray]] = None , _snake_case : Optional[Union[str, TensorType]] = None , _snake_case : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **_snake_case : str , )->PIL.Image.Image:
'''simple docstring'''
__lowerCAmelCase : List[str] = do_resize if do_resize is not None else self.do_resize
__lowerCAmelCase : Optional[Any] = size if size is not None else self.size
__lowerCAmelCase : List[Any] = get_size_dict(_snake_case )
__lowerCAmelCase : str = resample if resample is not None else self.resample
__lowerCAmelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
__lowerCAmelCase : Any = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
__lowerCAmelCase : Any = clusters if clusters is not None else self.clusters
__lowerCAmelCase : int = np.array(_snake_case )
__lowerCAmelCase : str = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
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 or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_color_quantize and clusters is None:
raise ValueError("""Clusters must be specified if do_color_quantize is True.""" )
# All transformations expect numpy arrays.
__lowerCAmelCase : Tuple = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
__lowerCAmelCase : int = [self.resize(image=_snake_case , size=_snake_case , resample=_snake_case ) for image in images]
if do_normalize:
__lowerCAmelCase : int = [self.normalize(image=_snake_case ) for image in images]
if do_color_quantize:
__lowerCAmelCase : int = [to_channel_dimension_format(_snake_case , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
__lowerCAmelCase : Any = np.array(_snake_case )
__lowerCAmelCase : Optional[Any] = color_quantize(_snake_case , _snake_case ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
__lowerCAmelCase : Union[str, Any] = images.shape[0]
__lowerCAmelCase : str = images.reshape(_snake_case , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
__lowerCAmelCase : Union[str, Any] = list(_snake_case )
else:
__lowerCAmelCase : Optional[int] = [to_channel_dimension_format(_snake_case , _snake_case ) for image in images]
__lowerCAmelCase : Optional[int] = {"""input_ids""": images}
return BatchFeature(data=_snake_case , tensor_type=_snake_case )
| 504
|
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
_UpperCAmelCase = sys.version_info >= (3, 10)
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Dict=None , SCREAMING_SNAKE_CASE :str=None ) -> Union[str, Any]:
return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE )
@dataclass
class snake_case_ :
A_ = 42
A_ = 42
A_ = 42
A_ = 42
@dataclass
class snake_case_ :
A_ = 42
A_ = field(default='toto' ,metadata={'help': 'help message'} )
@dataclass
class snake_case_ :
A_ = False
A_ = True
A_ = None
class snake_case_ ( __lowercase ):
A_ = 'titi'
A_ = 'toto'
class snake_case_ ( __lowercase ):
A_ = 'titi'
A_ = 'toto'
A_ = 42
@dataclass
class snake_case_ :
A_ = "toto"
def UpperCAmelCase__ ( self : Optional[int] )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = BasicEnum(self.foo )
@dataclass
class snake_case_ :
A_ = "toto"
def UpperCAmelCase__ ( self : Dict )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : Tuple = MixedTypeEnum(self.foo )
@dataclass
class snake_case_ :
A_ = None
A_ = field(default=__lowercase ,metadata={'help': 'help message'} )
A_ = None
A_ = list_field(default=[] )
A_ = list_field(default=[] )
@dataclass
class snake_case_ :
A_ = list_field(default=[] )
A_ = list_field(default=[1, 2, 3] )
A_ = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
A_ = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class snake_case_ :
A_ = field()
A_ = field()
A_ = field()
def UpperCAmelCase__ ( self : int )->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : int = BasicEnum(self.required_enum )
@dataclass
class snake_case_ :
A_ = 42
A_ = field()
A_ = None
A_ = field(default='toto' ,metadata={'help': 'help message'} )
A_ = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class snake_case_ :
A_ = False
A_ = True
A_ = None
@dataclass
class snake_case_ :
A_ = None
A_ = field(default=__lowercase ,metadata={'help': 'help message'} )
A_ = None
A_ = list_field(default=[] )
A_ = list_field(default=[] )
class snake_case_ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Union[str, Any] , _snake_case : argparse.ArgumentParser , _snake_case : argparse.ArgumentParser )->Optional[int]:
'''simple docstring'''
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
__lowerCAmelCase : Dict = {k: v for k, v in vars(_snake_case ).items() if k != """container"""}
__lowerCAmelCase : Any = {k: v for k, v in vars(_snake_case ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , _snake_case ) and yy.get("""choices""" , _snake_case ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](_snake_case ) , yy["""type"""](_snake_case ) )
del xx["type"], yy["type"]
self.assertEqual(_snake_case , _snake_case )
def UpperCAmelCase__ ( self : Optional[int] )->Any:
'''simple docstring'''
__lowerCAmelCase : int = HfArgumentParser(_snake_case )
__lowerCAmelCase : Dict = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=_snake_case , required=_snake_case )
expected.add_argument("""--bar""" , type=_snake_case , required=_snake_case )
expected.add_argument("""--baz""" , type=_snake_case , required=_snake_case )
expected.add_argument("""--flag""" , type=_snake_case , default=_snake_case , const=_snake_case , nargs="""?""" )
self.argparsersEqual(_snake_case , _snake_case )
__lowerCAmelCase : Dict = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
((__lowerCAmelCase) , ) : Dict = parser.parse_args_into_dataclasses(_snake_case , look_for_args_file=_snake_case )
self.assertFalse(example.flag )
def UpperCAmelCase__ ( self : Union[str, Any] )->Dict:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = HfArgumentParser(_snake_case )
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=42 , type=_snake_case )
expected.add_argument("""--baz""" , default="""toto""" , type=_snake_case , help="""help message""" )
self.argparsersEqual(_snake_case , _snake_case )
def UpperCAmelCase__ ( self : Optional[Any] )->Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase : str = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=_snake_case , default=_snake_case , const=_snake_case , nargs="""?""" )
expected.add_argument("""--baz""" , type=_snake_case , default=_snake_case , const=_snake_case , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=_snake_case , dest="""baz""" )
expected.add_argument("""--opt""" , type=_snake_case , default=_snake_case )
__lowerCAmelCase : int = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_snake_case )
for dataclass_type in dataclass_types:
__lowerCAmelCase : Dict = HfArgumentParser(_snake_case )
self.argparsersEqual(_snake_case , _snake_case )
__lowerCAmelCase : List[str] = parser.parse_args([] )
self.assertEqual(_snake_case , Namespace(foo=_snake_case , baz=_snake_case , opt=_snake_case ) )
__lowerCAmelCase : str = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(_snake_case , Namespace(foo=_snake_case , baz=_snake_case , opt=_snake_case ) )
__lowerCAmelCase : List[Any] = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(_snake_case , Namespace(foo=_snake_case , baz=_snake_case , opt=_snake_case ) )
__lowerCAmelCase : int = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(_snake_case , Namespace(foo=_snake_case , baz=_snake_case , opt=_snake_case ) )
__lowerCAmelCase : int = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(_snake_case , Namespace(foo=_snake_case , baz=_snake_case , opt=_snake_case ) )
def UpperCAmelCase__ ( self : Optional[Any] )->str:
'''simple docstring'''
__lowerCAmelCase : List[Any] = HfArgumentParser(_snake_case )
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 42] , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(_snake_case , _snake_case )
__lowerCAmelCase : Union[str, Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__lowerCAmelCase : Dict = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
__lowerCAmelCase : Any = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__lowerCAmelCase : int = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
__lowerCAmelCase : str = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
__lowerCAmelCase : List[str] = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def UpperCAmelCase__ ( self : Optional[int] )->Tuple:
'''simple docstring'''
@dataclass
class snake_case_ :
A_ = "toto"
__lowerCAmelCase : str = HfArgumentParser(_snake_case )
__lowerCAmelCase : int = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 42) , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(_snake_case , _snake_case )
__lowerCAmelCase : Optional[Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__lowerCAmelCase : List[str] = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__lowerCAmelCase : List[str] = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
def UpperCAmelCase__ ( self : List[str] )->Dict:
'''simple docstring'''
__lowerCAmelCase : int = HfArgumentParser(_snake_case )
__lowerCAmelCase : Any = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=_snake_case )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=_snake_case )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=_snake_case )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=_snake_case )
self.argparsersEqual(_snake_case , _snake_case )
__lowerCAmelCase : Any = parser.parse_args([] )
self.assertEqual(
_snake_case , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
__lowerCAmelCase : Optional[Any] = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(_snake_case , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def UpperCAmelCase__ ( self : List[str] )->List[str]:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=_snake_case , type=_snake_case )
expected.add_argument("""--bar""" , default=_snake_case , type=_snake_case , help="""help message""" )
expected.add_argument("""--baz""" , default=_snake_case , type=_snake_case )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=_snake_case )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=_snake_case )
__lowerCAmelCase : Dict = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_snake_case )
for dataclass_type in dataclass_types:
__lowerCAmelCase : Optional[Any] = HfArgumentParser(_snake_case )
self.argparsersEqual(_snake_case , _snake_case )
__lowerCAmelCase : Optional[Any] = parser.parse_args([] )
self.assertEqual(_snake_case , Namespace(foo=_snake_case , bar=_snake_case , baz=_snake_case , ces=[] , des=[] ) )
__lowerCAmelCase : Union[str, Any] = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(_snake_case , Namespace(foo=12 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def UpperCAmelCase__ ( self : Optional[int] )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : Optional[int] = HfArgumentParser(_snake_case )
__lowerCAmelCase : Any = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=_snake_case , required=_snake_case )
expected.add_argument("""--required_str""" , type=_snake_case , required=_snake_case )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=_snake_case , )
self.argparsersEqual(_snake_case , _snake_case )
def UpperCAmelCase__ ( self : str )->Dict:
'''simple docstring'''
__lowerCAmelCase : Dict = HfArgumentParser(_snake_case )
__lowerCAmelCase : Any = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=_snake_case , required=_snake_case )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=_snake_case , )
expected.add_argument("""--opt""" , type=_snake_case , default=_snake_case )
expected.add_argument("""--baz""" , default="""toto""" , type=_snake_case , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=_snake_case )
self.argparsersEqual(_snake_case , _snake_case )
def UpperCAmelCase__ ( self : int )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = HfArgumentParser(_snake_case )
__lowerCAmelCase : Dict = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
__lowerCAmelCase : str = parser.parse_dict(_snake_case )[0]
__lowerCAmelCase : Optional[int] = BasicExample(**_snake_case )
self.assertEqual(_snake_case , _snake_case )
def UpperCAmelCase__ ( self : List[str] )->Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase : List[Any] = HfArgumentParser(_snake_case )
__lowerCAmelCase : Dict = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 42,
}
self.assertRaises(_snake_case , parser.parse_dict , _snake_case , allow_extra_keys=_snake_case )
def UpperCAmelCase__ ( self : Union[str, Any] )->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = HfArgumentParser(_snake_case )
__lowerCAmelCase : List[Any] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCAmelCase : Optional[int] = os.path.join(_snake_case , """temp_json""" )
os.mkdir(_snake_case )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(_snake_case , _snake_case )
__lowerCAmelCase : Optional[int] = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
__lowerCAmelCase : Tuple = BasicExample(**_snake_case )
self.assertEqual(_snake_case , _snake_case )
def UpperCAmelCase__ ( self : Optional[Any] )->Optional[Any]:
'''simple docstring'''
__lowerCAmelCase : Dict = HfArgumentParser(_snake_case )
__lowerCAmelCase : Dict = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCAmelCase : List[Any] = os.path.join(_snake_case , """temp_yaml""" )
os.mkdir(_snake_case )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(_snake_case , _snake_case )
__lowerCAmelCase : Optional[Any] = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
__lowerCAmelCase : List[Any] = BasicExample(**_snake_case )
self.assertEqual(_snake_case , _snake_case )
def UpperCAmelCase__ ( self : Dict )->Optional[Any]:
'''simple docstring'''
__lowerCAmelCase : List[str] = HfArgumentParser(_snake_case )
self.assertIsNotNone(_snake_case )
| 504
| 1
|
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ : Tuple = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
lowercase__ : Optional[Any] = parser.parse_args()
lowercase__ : int = """cpu"""
lowercase__ : List[str] = """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""
lowercase__ : Optional[Any] = """path-to-your-trained-model"""
lowercase__ : int = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
lowercase__ : int = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__ : str = pipe.to(device)
# to channels last
lowercase__ : Union[str, Any] = pipe.unet.to(memory_format=torch.channels_last)
lowercase__ : int = pipe.vae.to(memory_format=torch.channels_last)
lowercase__ : Union[str, Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
lowercase__ : Any = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
lowercase__ : List[Any] = torch.randn(2, 4, 6_4, 6_4)
lowercase__ : Optional[Any] = torch.rand(1) * 9_9_9
lowercase__ : Tuple = torch.randn(2, 7_7, 7_6_8)
lowercase__ : Tuple = (sample, timestep, encoder_hidden_status)
try:
lowercase__ : List[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
lowercase__ : Optional[int] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__ : int = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__ : List[str] = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
lowercase__ : List[str] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
lowercase__ : List[Any] = 6_6_6
lowercase__ : Optional[int] = torch.Generator(device).manual_seed(seed)
lowercase__ : str = {"""generator""": generator}
if args.steps is not None:
lowercase__ : Optional[int] = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
lowercase__ : int = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save("""generated.png""")
| 317
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ : List[str] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Tuple = TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ : Optional[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = AutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : int = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ ,lowerCAmelCase_ : List[str] = TFAutoModelForCausalLM.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ ,lowerCAmelCase_ : Dict = AutoModelForCausalLM.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Optional[int] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Tuple = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Tuple = AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Optional[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ ,lowerCAmelCase_ : List[Any] = TFAutoModelForMaskedLM.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = AutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ ,lowerCAmelCase_ : Tuple = AutoModelForMaskedLM.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Optional[int] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ ,lowerCAmelCase_ : List[str] = TFAutoModelForSeqaSeqLM.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ ,lowerCAmelCase_ : Any = AutoModelForSeqaSeqLM.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ : List[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[str] = AutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : int ):
lowerCAmelCase_ : str = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_ ) , 1_4_4_1_0 )
lowerCAmelCase_ : str = AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_ ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : List[Any] = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ , from_pt=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_ ) , 1_4_4_1_0 )
lowerCAmelCase_ : Tuple = AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ , from_tf=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_ ) , 1_4_4_1_0 )
| 317
| 1
|
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class SCREAMING_SNAKE_CASE__ ( snake_case_ ):
"""simple docstring"""
def __init__( self , A="" , A="train" ) -> List[Any]:
assert os.path.isdir(A )
A: Optional[Any] = []
A: Optional[int] = os.listdir(A )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
A: Dict = os.path.join(A , A )
if not os.path.isfile(A ):
continue
self.documents.append(A )
def __len__( self ) -> List[Any]:
return len(self.documents )
def __getitem__( self , A ) -> Union[str, Any]:
A: Tuple = self.documents[idx]
A: int = document_path.split("""/""" )[-1]
with open(A , encoding="""utf-8""" ) as source:
A: int = source.read()
A , A: List[Any] = process_story(A )
return document_name, story_lines, summary_lines
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : Any ):
'''simple docstring'''
A: List[str] = list(filter(lambda lowerCamelCase__ : len(lowerCamelCase__ ) != 0 , [line.strip() for line in raw_story.split("""\n""" )] ) )
# for some unknown reason some lines miss a period, add it
A: Union[str, Any] = [_add_missing_period(lowerCamelCase__ ) for line in nonempty_lines]
# gather article lines
A: Optional[Any] = []
A: int = deque(lowerCamelCase__ )
while True:
try:
A: Optional[Any] = lines.popleft()
if element.startswith("""@highlight""" ):
break
story_lines.append(lowerCamelCase__ )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
A: Any = list(filter(lambda lowerCamelCase__ : not t.startswith("""@highlight""" ) , lowerCamelCase__ ) )
return story_lines, summary_lines
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
A: Optional[Any] = [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
if line.startswith("""@highlight""" ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Tuple ):
'''simple docstring'''
if len(lowerCamelCase__ ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(lowerCamelCase__ )) )
return sequence
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : Any , lowerCamelCase__ : List[str] ):
'''simple docstring'''
A: int = torch.ones_like(lowerCamelCase__ )
A: Optional[int] = sequence == pad_token_id
A: Optional[int] = 0
return mask
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : List[str] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : str ):
'''simple docstring'''
A: Tuple = [tokenizer.encode(lowerCamelCase__ ) for line in story_lines]
A: Tuple = [token for sentence in story_lines_token_ids for token in sentence]
A: List[str] = [tokenizer.encode(lowerCamelCase__ ) for line in summary_lines]
A: Optional[Any] = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Any ):
'''simple docstring'''
A: Tuple = []
for sequence in batch:
A: Optional[int] = -1
A: List[Any] = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(lowerCamelCase__ )
return torch.tensor(lowerCamelCase__ )
| 135
|
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
@property
def a__ ( self ) -> Optional[Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def a__ ( self ) -> int:
A: int = ort.SessionOptions()
A: List[str] = False
return options
def a__ ( self ) -> List[str]:
A: Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
A: str = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
A: Optional[int] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy""" )
# using the PNDM scheduler by default
A: Any = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=A , feature_extractor=A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A )
A: Union[str, Any] = """A red cat sitting on a park bench"""
A: List[str] = np.random.RandomState(0 )
A: str = pipe(
prompt=A , image=A , mask_image=A , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=A , output_type="""np""" , )
A: List[Any] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 1e-2
| 135
| 1
|
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
A : int = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def lowercase_ ( lowercase__ ) ->Optional[Any]:
for pegasus_name, hf_name in PATTERNS:
_snake_case: str = k.replace(lowercase__ , lowercase__ )
return k
def lowercase_ ( lowercase__ , lowercase__ ) ->PegasusForConditionalGeneration:
_snake_case: Tuple = DEFAULTS.copy()
cfg_kwargs.update(lowercase__ )
_snake_case: str = PegasusConfig(**lowercase__ )
_snake_case: Union[str, Any] = PegasusForConditionalGeneration(lowercase__ )
_snake_case: Any = torch_model.model.state_dict()
_snake_case: Any = {}
for k, v in tf_weights.items():
_snake_case: int = rename_state_dict_key(lowercase__ )
if new_k not in sd:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if "dense" in k or "proj" in new_k:
_snake_case: str = v.T
_snake_case: Optional[int] = torch.tensor(lowercase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
# make sure embedding.padding_idx is respected
_snake_case: Optional[Any] = torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
_snake_case: Any = mapping['shared.weight']
_snake_case: List[Any] = mapping['shared.weight']
_snake_case: List[str] = {k: torch.zeros_like(lowercase__ ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
mapping.update(**lowercase__ )
_snake_case , _snake_case: Dict = torch_model.model.load_state_dict(lowercase__ , strict=lowercase__ )
_snake_case: List[Any] = [
k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
]
assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], F'''no matches found for the following tf keys {extra}'''
return torch_model
def lowercase_ ( lowercase__="./ckpt/aeslc/model.ckpt-32000" ) ->Dict:
_snake_case: str = tf.train.list_variables(lowercase__ )
_snake_case: Union[str, Any] = {}
_snake_case: Dict = ['Adafactor', 'global_step']
for name, shape in tqdm(lowercase__ , desc='converting tf checkpoint to dict' ):
_snake_case: Dict = any(pat in name for pat in ignore_name )
if skip_key:
continue
_snake_case: Any = tf.train.load_variable(lowercase__ , lowercase__ )
_snake_case: Optional[int] = array
return tf_weights
def lowercase_ ( lowercase__ , lowercase__ ) ->Any:
# save tokenizer first
_snake_case: Optional[int] = Path(lowercase__ ).parent.name
_snake_case: Tuple = task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
_snake_case: List[Any] = PegasusTokenizer.from_pretrained('sshleifer/pegasus' , model_max_length=lowercase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(lowercase__ )
# convert model
_snake_case: Optional[Any] = get_tf_weights_as_numpy(lowercase__ )
_snake_case: Optional[int] = task_specific_params[F'''summarization_{dataset}''']
if dataset == "large":
_snake_case: Union[str, Any] = task_specific_params
_snake_case: List[Any] = convert_pegasus(lowercase__ , lowercase__ )
torch_model.save_pretrained(lowercase__ )
_snake_case: List[str] = torch_model.state_dict()
sd.pop('model.decoder.embed_positions.weight' )
sd.pop('model.encoder.embed_positions.weight' )
torch.save(lowercase__ , Path(lowercase__ ) / 'pytorch_model.bin' )
if __name__ == "__main__":
A : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
A : Optional[int] = parser.parse_args()
if args.save_dir is None:
A : List[Any] = Path(args.tf_ckpt_path).parent.name
A : Any = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 273
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Optional[Any] = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = ['DeiTFeatureExtractor']
A : Optional[int] = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
A : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 273
| 1
|
"""simple docstring"""
from random import randint, random
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = 5 , )-> list:
_SCREAMING_SNAKE_CASE : Union[str, Any] = [[-1] * number_of_cells] # Create a highway without any car
_SCREAMING_SNAKE_CASE : List[Any] = 0
_SCREAMING_SNAKE_CASE : str = max(__SCREAMING_SNAKE_CASE , 0 )
while i < number_of_cells:
_SCREAMING_SNAKE_CASE : Optional[int] = (
randint(0 , __SCREAMING_SNAKE_CASE ) if random_speed else initial_speed
) # Place the cars
i += (
randint(1 , max_speed * 2 ) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> int:
_SCREAMING_SNAKE_CASE : Optional[Any] = 0
_SCREAMING_SNAKE_CASE : Tuple = highway_now[car_index + 1 :]
for cell in range(len(__SCREAMING_SNAKE_CASE ) ): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(__SCREAMING_SNAKE_CASE , -1 )
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> list:
_SCREAMING_SNAKE_CASE : Tuple = len(__SCREAMING_SNAKE_CASE )
# Beforce calculations, the highway is empty
_SCREAMING_SNAKE_CASE : List[str] = [-1] * number_of_cells
for car_index in range(__SCREAMING_SNAKE_CASE ):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
_SCREAMING_SNAKE_CASE : Dict = min(highway_now[car_index] + 1 , __SCREAMING_SNAKE_CASE )
# Number of empty cell before the next car
_SCREAMING_SNAKE_CASE : Union[str, Any] = get_distance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) - 1
# We can't have the car causing an accident
_SCREAMING_SNAKE_CASE : Any = min(next_highway[car_index] , __SCREAMING_SNAKE_CASE )
if random() < probability:
# Randomly, a driver will slow down
_SCREAMING_SNAKE_CASE : Optional[Any] = max(next_highway[car_index] - 1 , 0 )
return next_highway
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> list:
_SCREAMING_SNAKE_CASE : int = len(highway[0] )
for i in range(__SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : List[Any] = update(highway[i] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : int = [-1] * number_of_cells
for car_index in range(__SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : Any = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
_SCREAMING_SNAKE_CASE : Optional[Any] = (car_index + speed) % number_of_cells
# Commit the change of position
_SCREAMING_SNAKE_CASE : Optional[int] = speed
highway.append(__SCREAMING_SNAKE_CASE )
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| 338
|
"""simple docstring"""
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 lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )-> List[Any]:
_SCREAMING_SNAKE_CASE : Optional[int] = {
"""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),
}
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = input_paths_and_base_extractors[compression_format]
if input_path is None:
_SCREAMING_SNAKE_CASE : List[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(__SCREAMING_SNAKE_CASE )
assert base_extractor.is_extractable(__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : Union[str, Any] = tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
base_extractor.extract(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
_SCREAMING_SNAKE_CASE : Optional[int] = file_path.read_text(encoding="""utf-8""" )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = output_path.read_text(encoding="""utf-8""" )
_SCREAMING_SNAKE_CASE : List[str] = 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 lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )-> List[str]:
_SCREAMING_SNAKE_CASE : Optional[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,
}
_SCREAMING_SNAKE_CASE : Tuple = input_paths[compression_format]
if input_path is None:
_SCREAMING_SNAKE_CASE : List[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(__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE : Optional[int] = Extractor.infer_extractor_format(__SCREAMING_SNAKE_CASE )
assert extractor_format is not None
_SCREAMING_SNAKE_CASE : Optional[Any] = tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
Extractor.extract(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
_SCREAMING_SNAKE_CASE : Tuple = file_path.read_text(encoding="""utf-8""" )
else:
_SCREAMING_SNAKE_CASE : str = output_path.read_text(encoding="""utf-8""" )
_SCREAMING_SNAKE_CASE : Optional[Any] = text_file.read_text(encoding="""utf-8""" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Any:
import tarfile
_SCREAMING_SNAKE_CASE : Any = tmp_path / """data_dot_dot"""
directory.mkdir()
_SCREAMING_SNAKE_CASE : Optional[int] = directory / """tar_file_with_dot_dot.tar"""
with tarfile.TarFile(__SCREAMING_SNAKE_CASE , """w""" ) as f:
f.add(__SCREAMING_SNAKE_CASE , arcname=os.path.join("""..""" , text_file.name ) )
return path
@pytest.fixture
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> Dict:
import tarfile
_SCREAMING_SNAKE_CASE : List[str] = tmp_path / """data_sym_link"""
directory.mkdir()
_SCREAMING_SNAKE_CASE : Optional[int] = directory / """tar_file_with_sym_link.tar"""
os.symlink("""..""" , directory / """subdir""" , target_is_directory=__SCREAMING_SNAKE_CASE )
with tarfile.TarFile(__SCREAMING_SNAKE_CASE , """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 lowerCamelCase_(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )-> Tuple:
_SCREAMING_SNAKE_CASE : List[Any] = {
"""tar_file_with_dot_dot""": tar_file_with_dot_dot,
"""tar_file_with_sym_link""": tar_file_with_sym_link,
}
_SCREAMING_SNAKE_CASE : int = insecure_tar_files[insecure_tar_file]
_SCREAMING_SNAKE_CASE : str = tmp_path / """extracted"""
TarExtractor.extract(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> str:
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
_SCREAMING_SNAKE_CASE : List[str] = tmpdir / """not_a_zip_file"""
# From: https://github.com/python/cpython/pull/5053
_SCREAMING_SNAKE_CASE : Any = (
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(__SCREAMING_SNAKE_CASE )
assert zipfile.is_zipfile(str(__SCREAMING_SNAKE_CASE ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__SCREAMING_SNAKE_CASE ) # but we're right
| 338
| 1
|
from itertools import product
def _UpperCamelCase ( UpperCamelCase_ : int , UpperCamelCase_ : int ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ = sides_number
lowerCAmelCase__ = max_face_number * dice_number
lowerCAmelCase__ = [0] * (max_total + 1)
lowerCAmelCase__ = 1
lowerCAmelCase__ = range(a_ , max_face_number + 1 )
for dice_numbers in product(a_ , repeat=a_ ):
lowerCAmelCase__ = sum(a_ )
totals_frequencies[total] += 1
return totals_frequencies
def _UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ = total_frequency_distribution(
sides_number=4 , dice_number=9 )
lowerCAmelCase__ = total_frequency_distribution(
sides_number=6 , dice_number=6 )
lowerCAmelCase__ = 0
lowerCAmelCase__ = 9
lowerCAmelCase__ = 4 * 9
lowerCAmelCase__ = 6
for peter_total in range(a_ , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
lowerCAmelCase__ = (4**9) * (6**6)
lowerCAmelCase__ = peter_wins_count / total_games_number
lowerCAmelCase__ = round(a_ , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(f'{solution() = }')
| 705
|
from __future__ import annotations
import pandas as pd
def _UpperCamelCase ( UpperCamelCase_ : list[int] , UpperCamelCase_ : list[int] , UpperCamelCase_ : int ) -> list[int]:
"""simple docstring"""
lowerCAmelCase__ = [0] * no_of_processes
lowerCAmelCase__ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(UpperCamelCase_ ):
lowerCAmelCase__ = burst_time[i]
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 9_9999_9999
lowerCAmelCase__ = 0
lowerCAmelCase__ = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(UpperCamelCase_ ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
lowerCAmelCase__ = remaining_time[j]
lowerCAmelCase__ = j
lowerCAmelCase__ = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
lowerCAmelCase__ = remaining_time[short]
if minm == 0:
lowerCAmelCase__ = 9_9999_9999
if remaining_time[short] == 0:
complete += 1
lowerCAmelCase__ = False
# Find finish time of current process
lowerCAmelCase__ = increment_time + 1
# Calculate waiting time
lowerCAmelCase__ = finish_time - arrival_time[short]
lowerCAmelCase__ = finar - burst_time[short]
if waiting_time[short] < 0:
lowerCAmelCase__ = 0
# Increment time
increment_time += 1
return waiting_time
def _UpperCamelCase ( UpperCamelCase_ : list[int] , UpperCamelCase_ : int , UpperCamelCase_ : list[int] ) -> list[int]:
"""simple docstring"""
lowerCAmelCase__ = [0] * no_of_processes
for i in range(UpperCamelCase_ ):
lowerCAmelCase__ = burst_time[i] + waiting_time[i]
return turn_around_time
def _UpperCamelCase ( UpperCamelCase_ : list[int] , UpperCamelCase_ : list[int] , UpperCamelCase_ : int ) -> None:
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
for i in range(UpperCamelCase_ ):
lowerCAmelCase__ = total_waiting_time + waiting_time[i]
lowerCAmelCase__ = total_turn_around_time + turn_around_time[i]
print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}" )
print('Average turn around time =' , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print("""Enter how many process you want to analyze""")
__snake_case : Dict = int(input())
__snake_case : List[Any] = [0] * no_of_processes
__snake_case : str = [0] * no_of_processes
__snake_case : Optional[int] = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
__snake_case , __snake_case : Any = map(int, input().split())
__snake_case : str = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
__snake_case : Dict = burst_time
__snake_case : Optional[int] = no_of_processes
__snake_case : str = waiting_time
__snake_case : int = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
__snake_case : Dict = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"""Process""",
"""BurstTime""",
"""ArrivalTime""",
"""WaitingTime""",
"""TurnAroundTime""",
],
)
# Printing the dataFrame
pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
print(fcfs)
| 365
| 0
|
from __future__ import annotations
def __UpperCAmelCase ( __a : list[int] ,__a : int ) -> list[list[int]]:
"""simple docstring"""
_a : list[list[int]] = []
_a : list[int] = []
_a : Optional[int] = 0
_a : Any = sum(__a )
create_state_space_tree(__a ,__a ,__a ,__a ,__a ,__a )
return result
def __UpperCAmelCase ( __a : list[int] ,__a : int ,__a : int ,__a : list[int] ,__a : list[list[int]] ,__a : int ,) -> None:
"""simple docstring"""
if sum(__a ) > max_sum or (remaining_nums_sum + sum(__a )) < max_sum:
return
if sum(__a ) == max_sum:
result.append(__a )
return
for index in range(__a ,len(__a ) ):
create_state_space_tree(
__a ,__a ,index + 1 ,[*path, nums[index]] ,__a ,remaining_nums_sum - nums[index] ,)
a__ = [3, 34, 4, 12, 5, 2]
a__ = 9
a__ = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 14
|
"""simple docstring"""
from __future__ import annotations
__A = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ) -> tuple[list[list[int]], list[list[int]]]:
__lowerCAmelCase: Dict = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__SCREAMING_SNAKE_CASE ) )
] # the reference grid
__lowerCAmelCase: Tuple = 1
__lowerCAmelCase: Dict = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__SCREAMING_SNAKE_CASE ) )
] # the action grid
__lowerCAmelCase: Tuple = init[0]
__lowerCAmelCase: Any = init[1]
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: int = g + heuristic[x][y] # cost from starting cell to destination cell
__lowerCAmelCase: Optional[Any] = [[f, g, x, y]]
__lowerCAmelCase: Union[str, Any] = False # flag that is set when search is complete
__lowerCAmelCase: List[Any] = False # flag set if we can't find expand
while not found and not resign:
if len(__SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("Algorithm is unable to find solution" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
__lowerCAmelCase: Union[str, Any] = cell.pop()
__lowerCAmelCase: Optional[int] = next_cell[2]
__lowerCAmelCase: int = next_cell[3]
__lowerCAmelCase: Optional[int] = next_cell[1]
if x == goal[0] and y == goal[1]:
__lowerCAmelCase: int = True
else:
for i in range(len(__SCREAMING_SNAKE_CASE ) ): # to try out different valid actions
__lowerCAmelCase: Dict = x + DIRECTIONS[i][0]
__lowerCAmelCase: str = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(__SCREAMING_SNAKE_CASE ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
__lowerCAmelCase: Tuple = g + cost
__lowerCAmelCase: Union[str, Any] = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
__lowerCAmelCase: int = 1
__lowerCAmelCase: List[Any] = i
__lowerCAmelCase: int = []
__lowerCAmelCase: Dict = goal[0]
__lowerCAmelCase: Any = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
__lowerCAmelCase: Tuple = x - DIRECTIONS[action[x][y]][0]
__lowerCAmelCase: Tuple = y - DIRECTIONS[action[x][y]][1]
__lowerCAmelCase: List[Any] = xa
__lowerCAmelCase: Dict = ya
invpath.append([x, y] )
__lowerCAmelCase: Tuple = []
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
path.append(invpath[len(__SCREAMING_SNAKE_CASE ) - 1 - i] )
return path, action
if __name__ == "__main__":
__A = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
__A = [0, 0]
# all coordinates are given in format [y,x]
__A = [len(grid) - 1, len(grid[0]) - 1]
__A = 1
# the cost map which pushes the path closer to the goal
__A = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
__A = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__A = 99
__A , __A = search(grid, init, goal, cost, heuristic)
print("ACTION MAP")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 346
| 0
|
'''simple docstring'''
import math
def lowerCAmelCase_ ( lowercase: Tuple , lowercase: List[str] ) -> int:
'''simple docstring'''
_UpperCamelCase: Optional[Any] = len(SCREAMING_SNAKE_CASE_ )
_UpperCamelCase: Union[str, Any] = int(math.floor(math.sqrt(SCREAMING_SNAKE_CASE_ ) ) )
_UpperCamelCase: Union[str, Any] = 0
while arr[min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) - 1] < x:
_UpperCamelCase: Any = step
step += int(math.floor(math.sqrt(SCREAMING_SNAKE_CASE_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCamelCase: str = prev + 1
if prev == min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
UpperCAmelCase_ = input('''Enter numbers separated by a comma:\n''').strip()
UpperCAmelCase_ = [int(item) for item in user_input.split(''',''')]
UpperCAmelCase_ = int(input('''Enter the number to be searched:\n'''))
UpperCAmelCase_ = jump_search(arr, x)
if res == -1:
print('''Number not found!''')
else:
print(f"""Number {x} is at index {res}""")
| 703
|
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
class __magic_name__ ( __a ):
"""simple docstring"""
def __init__( self : List[Any] , _lowercase : int=None , **_lowercase : Optional[Any] ):
"""simple docstring"""
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , _lowercase , )
super().__init__(args=_lowercase , **_lowercase )
| 264
| 0
|
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class lowercase ( unittest.TestCase ):
def UpperCAmelCase (self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = logging.get_logger()
# the current default level is logging.WARNING
lowerCAmelCase = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase (self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = logging.get_verbosity()
lowerCAmelCase = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
lowerCAmelCase = '''Testing 1, 2, 3'''
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(SCREAMING_SNAKE_CASE_ ) as cl:
logger.warning(SCREAMING_SNAKE_CASE_ )
self.assertEqual(cl.out ,msg + '''\n''' )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(SCREAMING_SNAKE_CASE_ ) as cl:
logger.warning(SCREAMING_SNAKE_CASE_ )
self.assertEqual(cl.out ,'''''' )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(SCREAMING_SNAKE_CASE_ ) as cl:
logger.warning(SCREAMING_SNAKE_CASE_ )
self.assertEqual(cl.out ,msg + '''\n''' )
# restore to the original level
logging.set_verbosity(SCREAMING_SNAKE_CASE_ )
@mockenv(TRANSFORMERS_VERBOSITY='''error''' )
def UpperCAmelCase (self : Any ) -> str:
"""simple docstring"""
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
lowerCAmelCase = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
lowerCAmelCase = os.getenv('''TRANSFORMERS_VERBOSITY''' ,SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = logging.log_levels[env_level_str]
lowerCAmelCase = logging.get_verbosity()
self.assertEqual(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}""" ,)
# restore to the original level
lowerCAmelCase = ''''''
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY='''super-error''' )
def UpperCAmelCase (self : List[Any] ) -> Tuple:
"""simple docstring"""
transformers.utils.logging._reset_library_root_logger()
lowerCAmelCase = logging.logging.getLogger()
with CaptureLogger(SCREAMING_SNAKE_CASE_ ) as cl:
# this action activates the env var
logging.get_logger('''transformers.models.bart.tokenization_bart''' )
self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error''' ,cl.out )
# no need to restore as nothing was changed
def UpperCAmelCase (self : Dict ) -> Any:
"""simple docstring"""
transformers.utils.logging._reset_library_root_logger()
lowerCAmelCase = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
lowerCAmelCase = '''Testing 1, 2, 3'''
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1''' ):
# nothing should be logged as env var disables this method
with CaptureLogger(SCREAMING_SNAKE_CASE_ ) as cl:
logger.warning_advice(SCREAMING_SNAKE_CASE_ )
self.assertEqual(cl.out ,'''''' )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''''' ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(SCREAMING_SNAKE_CASE_ ) as cl:
logger.warning_advice(SCREAMING_SNAKE_CASE_ )
self.assertEqual(cl.out ,msg + '''\n''' )
def __magic_name__ ( ) -> List[Any]:
'''simple docstring'''
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 535
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=lowercase__ ):
lowercase = ['''flax''', '''transformers''']
def __init__(self : List[Any] ,*SCREAMING_SNAKE_CASE_ : Union[str, Any] ,**SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> str:
"""simple docstring"""
requires_backends(self ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : List[Any] ,*SCREAMING_SNAKE_CASE_ : List[Any] ,**SCREAMING_SNAKE_CASE_ : int ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : List[Any] ,*SCREAMING_SNAKE_CASE_ : str ,**SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
class lowercase ( metaclass=lowercase__ ):
lowercase = ['''flax''', '''transformers''']
def __init__(self : int ,*SCREAMING_SNAKE_CASE_ : List[Any] ,**SCREAMING_SNAKE_CASE_ : Any ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : int ,*SCREAMING_SNAKE_CASE_ : str ,**SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : List[str] ,*SCREAMING_SNAKE_CASE_ : List[str] ,**SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Dict:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
class lowercase ( metaclass=lowercase__ ):
lowercase = ['''flax''', '''transformers''']
def __init__(self : Tuple ,*SCREAMING_SNAKE_CASE_ : Optional[int] ,**SCREAMING_SNAKE_CASE_ : Dict ) -> List[str]:
"""simple docstring"""
requires_backends(self ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : Tuple ,*SCREAMING_SNAKE_CASE_ : Dict ,**SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : Tuple ,*SCREAMING_SNAKE_CASE_ : Tuple ,**SCREAMING_SNAKE_CASE_ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
class lowercase ( metaclass=lowercase__ ):
lowercase = ['''flax''', '''transformers''']
def __init__(self : Optional[Any] ,*SCREAMING_SNAKE_CASE_ : Any ,**SCREAMING_SNAKE_CASE_ : str ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : List[Any] ,*SCREAMING_SNAKE_CASE_ : Optional[Any] ,**SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
@classmethod
def UpperCAmelCase (cls : List[Any] ,*SCREAMING_SNAKE_CASE_ : List[Any] ,**SCREAMING_SNAKE_CASE_ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls ,['''flax''', '''transformers'''] )
| 535
| 1
|
def A_ ( snake_case : int ) -> str:
'''simple docstring'''
if isinstance(snake_case , snake_case ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(snake_case , snake_case ):
raise TypeError('''\'str\' object cannot be interpreted as an integer''' )
if num == 0:
return "0b0"
__UpperCamelCase = False
if num < 0:
__UpperCamelCase = True
__UpperCamelCase = -num
__UpperCamelCase = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(snake_case ) for e in binary )
return "0b" + "".join(str(snake_case ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 451
|
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
def A_ ( snake_case : List[str] ) -> List[str]:
'''simple docstring'''
print('''Loading config file...''' )
def flatten_yaml_as_dict(snake_case : Optional[int] , snake_case : List[Any]="" , snake_case : str="." ):
__UpperCamelCase = []
for k, v in d.items():
__UpperCamelCase = parent_key + sep + k if parent_key else k
if isinstance(snake_case , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(snake_case , snake_case , sep=snake_case ).items() )
else:
items.append((new_key, v) )
return dict(snake_case )
__UpperCamelCase = argparse.Namespace()
with open(snake_case , '''r''' ) as yaml_file:
try:
__UpperCamelCase = yaml.load(snake_case , Loader=yaml.FullLoader )
__UpperCamelCase = flatten_yaml_as_dict(snake_case )
for k, v in flat_cfg.items():
setattr(snake_case , snake_case , snake_case )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(snake_case , str(snake_case ) ) )
return config
def A_ ( snake_case : List[Any] , snake_case : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase = MobileViTVaConfig()
__UpperCamelCase = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
__UpperCamelCase = 1000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
__UpperCamelCase = 384
else:
__UpperCamelCase = 256
__UpperCamelCase = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
__UpperCamelCase = 21000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
__UpperCamelCase = 384
else:
__UpperCamelCase = 256
__UpperCamelCase = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
__UpperCamelCase = 151
__UpperCamelCase = 512
__UpperCamelCase = '''ade20k-id2label.json'''
__UpperCamelCase = True
elif task_name.startswith('''voc_''' ):
__UpperCamelCase = 21
__UpperCamelCase = 512
__UpperCamelCase = '''pascal-voc-id2label.json'''
__UpperCamelCase = True
# orig_config
__UpperCamelCase = load_orig_config_file(snake_case )
assert getattr(snake_case , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
__UpperCamelCase = getattr(snake_case , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(snake_case , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
__UpperCamelCase = getattr(snake_case , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
__UpperCamelCase = getattr(snake_case , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
__UpperCamelCase = getattr(snake_case , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
__UpperCamelCase = getattr(snake_case , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
__UpperCamelCase = getattr(snake_case , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
__UpperCamelCase = '''huggingface/label-files'''
__UpperCamelCase = json.load(open(hf_hub_download(snake_case , snake_case , repo_type='''dataset''' ) , '''r''' ) )
__UpperCamelCase = {int(snake_case ): v for k, v in idalabel.items()}
__UpperCamelCase = idalabel
__UpperCamelCase = {v: k for k, v in idalabel.items()}
return config
def A_ ( snake_case : List[Any] , snake_case : int , snake_case : Any ) -> str:
'''simple docstring'''
__UpperCamelCase = dct.pop(snake_case )
__UpperCamelCase = val
def A_ ( snake_case : int , snake_case : List[Any]=False ) -> Optional[Any]:
'''simple docstring'''
if base_model:
__UpperCamelCase = ''''''
else:
__UpperCamelCase = '''mobilevitv2.'''
__UpperCamelCase = []
for k in state_dict.keys():
if k[:8] == "encoder.":
__UpperCamelCase = k[8:]
else:
__UpperCamelCase = k
if ".block." in k:
__UpperCamelCase = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
__UpperCamelCase = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
__UpperCamelCase = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
__UpperCamelCase = k_new.replace('''conv_1.''' , f"{model_prefix}conv_stem." )
for i in [1, 2]:
if f"layer_{i}." in k:
__UpperCamelCase = k_new.replace(f"layer_{i}." , f"{model_prefix}encoder.layer.{i-1}.layer." )
if ".exp_1x1." in k:
__UpperCamelCase = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
__UpperCamelCase = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f"layer_{i}.0." in k:
__UpperCamelCase = k_new.replace(f"layer_{i}.0." , f"{model_prefix}encoder.layer.{i-1}.downsampling_layer." )
if f"layer_{i}.1.local_rep.0." in k:
__UpperCamelCase = k_new.replace(f"layer_{i}.1.local_rep.0." , f"{model_prefix}encoder.layer.{i-1}.conv_kxk." )
if f"layer_{i}.1.local_rep.1." in k:
__UpperCamelCase = k_new.replace(f"layer_{i}.1.local_rep.1." , f"{model_prefix}encoder.layer.{i-1}.conv_1x1." )
for i in [3, 4, 5]:
if i == 3:
__UpperCamelCase = [0, 1]
elif i == 4:
__UpperCamelCase = [0, 1, 2, 3]
elif i == 5:
__UpperCamelCase = [0, 1, 2]
for j in j_in:
if f"layer_{i}.1.global_rep.{j}." in k:
__UpperCamelCase = k_new.replace(
f"layer_{i}.1.global_rep.{j}." , f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}." )
if f"layer_{i}.1.global_rep.{j+1}." in k:
__UpperCamelCase = k_new.replace(
f"layer_{i}.1.global_rep.{j+1}." , f"{model_prefix}encoder.layer.{i-1}.layernorm." )
if f"layer_{i}.1.conv_proj." in k:
__UpperCamelCase = k_new.replace(f"layer_{i}.1.conv_proj." , f"{model_prefix}encoder.layer.{i-1}.conv_projection." )
if "pre_norm_attn.0." in k:
__UpperCamelCase = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
__UpperCamelCase = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
__UpperCamelCase = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
__UpperCamelCase = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
__UpperCamelCase = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
__UpperCamelCase = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
__UpperCamelCase = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
__UpperCamelCase = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
__UpperCamelCase = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def A_ ( snake_case : List[str] ) -> str:
'''simple docstring'''
__UpperCamelCase = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(snake_case )
for k in keys_to_ignore:
state_dict.pop(snake_case , snake_case )
def A_ ( ) -> str:
'''simple docstring'''
__UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
__UpperCamelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw )
return im
@torch.no_grad()
def A_ ( snake_case : Dict , snake_case : List[str] , snake_case : Optional[Any] , snake_case : Optional[int] ) -> int:
'''simple docstring'''
__UpperCamelCase = get_mobilevitva_config(snake_case , snake_case )
# load original state_dict
__UpperCamelCase = torch.load(snake_case , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
__UpperCamelCase = MobileViTVaForSemanticSegmentation(snake_case ).eval()
__UpperCamelCase = False
else:
__UpperCamelCase = MobileViTVaForImageClassification(snake_case ).eval()
__UpperCamelCase = False
# remove and rename some keys of load the original model
__UpperCamelCase = checkpoint
remove_unused_keys(snake_case )
__UpperCamelCase = create_rename_keys(snake_case , base_model=snake_case )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(snake_case , snake_case , snake_case )
# load modified state_dict
model.load_state_dict(snake_case )
# Check outputs on an image, prepared by MobileViTImageProcessor
__UpperCamelCase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
__UpperCamelCase = image_processor(images=prepare_img() , return_tensors='''pt''' )
__UpperCamelCase = model(**snake_case )
# verify classification model
if task_name.startswith('''imagenet''' ):
__UpperCamelCase = outputs.logits
__UpperCamelCase = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
__UpperCamelCase = torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01] )
assert torch.allclose(logits[0, :3] , snake_case , atol=1e-4 )
Path(snake_case ).mkdir(exist_ok=snake_case )
print(f"Saving model {task_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(snake_case )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(snake_case )
if __name__ == "__main__":
lowercase__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
lowercase__ : Tuple = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 451
| 1
|
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase (__SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase = CanineTokenizer
_UpperCAmelCase = False
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCamelCase__ ( self ):
"""simple docstring"""
return CanineTokenizer.from_pretrained('google/canine-s' )
def UpperCamelCase__ ( self , **lowerCAmelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Tuple = 1_0_2_4
return tokenizer
@require_torch
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.canine_tokenizer
SCREAMING_SNAKE_CASE_ : List[str] = ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
# fmt: off
SCREAMING_SNAKE_CASE_ : Tuple = [5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
# fmt: on
SCREAMING_SNAKE_CASE_ : Dict = tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , return_tensors='pt' )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = list(batch.input_ids.numpy()[0] )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual((2, 3_9) , batch.input_ids.shape )
self.assertEqual((2, 3_9) , batch.attention_mask.shape )
@require_torch
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = self.canine_tokenizer
SCREAMING_SNAKE_CASE_ : Dict = ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
SCREAMING_SNAKE_CASE_ : Optional[Any] = tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , return_tensors='pt' )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn('input_ids' , lowerCAmelCase__ )
self.assertIn('attention_mask' , lowerCAmelCase__ )
self.assertIn('token_type_ids' , lowerCAmelCase__ )
@require_torch
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.canine_tokenizer
SCREAMING_SNAKE_CASE_ : List[Any] = [
'What\'s the weater?',
'It\'s about 25 degrees.',
]
SCREAMING_SNAKE_CASE_ : int = tokenizer(
text_target=lowerCAmelCase__ , max_length=3_2 , padding='max_length' , truncation=lowerCAmelCase__ , return_tensors='pt' )
self.assertEqual(3_2 , targets['input_ids'].shape[1] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
SCREAMING_SNAKE_CASE_ : List[str] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
SCREAMING_SNAKE_CASE_ : Union[str, Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ' He is very happy, UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE_ : List[str] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
tokenizer.save_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any = tokenizer.__class__.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : str = after_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
shutil.rmtree(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
SCREAMING_SNAKE_CASE_ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ : List[str] = ' He is very happy, UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE_ : Union[str, Any] = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
SCREAMING_SNAKE_CASE_ : Optional[Any] = chr(0XE_0_0_7 )
additional_special_tokens.append(lowerCAmelCase__ )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
SCREAMING_SNAKE_CASE_ : Any = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
tokenizer.save_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer.__class__.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = after_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertIn(lowerCAmelCase__ , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
SCREAMING_SNAKE_CASE_ : int = tokenizer.__class__.from_pretrained(lowerCAmelCase__ , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(lowerCAmelCase__ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.get_tokenizers(do_lower_case=lowerCAmelCase__ )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = self.get_clean_sequence(lowerCAmelCase__ )
# a special token for Canine can be defined as follows:
SCREAMING_SNAKE_CASE_ : Dict = 0XE_0_0_5
SCREAMING_SNAKE_CASE_ : Tuple = chr(lowerCAmelCase__ )
tokenizer.add_special_tokens({'cls_token': special_token} )
SCREAMING_SNAKE_CASE_ : Optional[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(len(lowerCAmelCase__ ) , 1 )
SCREAMING_SNAKE_CASE_ : Tuple = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , input_encoded + special_token_id )
SCREAMING_SNAKE_CASE_ : Any = tokenizer.decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
self.assertTrue(special_token not in decoded )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = self.get_tokenizers(do_lower_case=lowerCAmelCase__ )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE_ : List[Any] = chr(0XE_0_0_5 )
SCREAMING_SNAKE_CASE_ : Optional[int] = chr(0XE_0_0_6 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=lowerCAmelCase__ )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]} )
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer.tokenize(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[Any] = tokenizer.tokenize(lowerCAmelCase__ )
self.assertEqual(len(lowerCAmelCase__ ) , 1 )
self.assertEqual(len(lowerCAmelCase__ ) , 1 )
self.assertEqual(token_a[0] , lowerCAmelCase__ )
self.assertEqual(token_a[0] , lowerCAmelCase__ )
@require_tokenizers
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = self.get_tokenizers(do_lower_case=lowerCAmelCase__ )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
# a special token for Canine can be defined as follows:
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0XE_0_0_6
SCREAMING_SNAKE_CASE_ : Tuple = chr(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ )
tokenizer.add_special_tokens({'additional_special_tokens': [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(lowerCAmelCase__ )
tokenizer.from_pretrained(lowerCAmelCase__ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCAmelCase__ )
with open(os.path.join(lowerCAmelCase__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
SCREAMING_SNAKE_CASE_ : Any = json.load(lowerCAmelCase__ )
with open(os.path.join(lowerCAmelCase__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
SCREAMING_SNAKE_CASE_ : List[str] = json.load(lowerCAmelCase__ )
# a special token for Canine can be defined as follows:
SCREAMING_SNAKE_CASE_ : int = 0XE_0_0_6
SCREAMING_SNAKE_CASE_ : List[str] = chr(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any = [new_token_a]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [new_token_a]
with open(os.path.join(lowerCAmelCase__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(lowerCAmelCase__ , lowerCAmelCase__ )
with open(os.path.join(lowerCAmelCase__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(lowerCAmelCase__ , lowerCAmelCase__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer_class.from_pretrained(lowerCAmelCase__ , extra_ids=0 )
self.assertIn(lowerCAmelCase__ , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
SCREAMING_SNAKE_CASE_ : List[str] = 0XE_0_0_7
SCREAMING_SNAKE_CASE_ : Union[str, Any] = chr(lowerCAmelCase__ )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
SCREAMING_SNAKE_CASE_ : Dict = [AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ )]
SCREAMING_SNAKE_CASE_ : List[Any] = tokenizer_class.from_pretrained(
lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , extra_ids=0 )
self.assertIn(lowerCAmelCase__ , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = self.get_tokenizers(do_lower_case=lowerCAmelCase__ )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE_ : int = 'hello world'
if self.space_between_special_tokens:
SCREAMING_SNAKE_CASE_ : List[str] = '[CLS] hello world [SEP]'
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = input
SCREAMING_SNAKE_CASE_ : Optional[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = tokenizer.decode(lowerCAmelCase__ , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(lowerCAmelCase__ , [output, output.lower()] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 'a'
SCREAMING_SNAKE_CASE_ : Optional[Any] = ord(lowerCAmelCase__ )
for attr in attributes_list:
setattr(lowerCAmelCase__ , attr + '_id' , lowerCAmelCase__ )
self.assertEqual(getattr(lowerCAmelCase__ , lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(getattr(lowerCAmelCase__ , attr + '_id' ) , lowerCAmelCase__ )
setattr(lowerCAmelCase__ , attr + '_id' , lowerCAmelCase__ )
self.assertEqual(getattr(lowerCAmelCase__ , lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(getattr(lowerCAmelCase__ , attr + '_id' ) , lowerCAmelCase__ )
setattr(lowerCAmelCase__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(lowerCAmelCase__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(lowerCAmelCase__ , 'additional_special_tokens_ids' ) , [] )
SCREAMING_SNAKE_CASE_ : str = 0XE_0_0_6
SCREAMING_SNAKE_CASE_ : Union[str, Any] = chr(lowerCAmelCase__ )
setattr(lowerCAmelCase__ , 'additional_special_tokens_ids' , [additional_special_token_id] )
self.assertListEqual(getattr(lowerCAmelCase__ , 'additional_special_tokens' ) , [additional_special_token] )
self.assertListEqual(getattr(lowerCAmelCase__ , 'additional_special_tokens_ids' ) , [additional_special_token_id] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
| 101
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__snake_case = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
"""tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
"""GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXJapaneseForCausalLM""",
"""GPTNeoXJapaneseLayer""",
"""GPTNeoXJapaneseModel""",
"""GPTNeoXJapanesePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 451
| 0
|
__lowerCamelCase = [
'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
| 714
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 307
| 0
|
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerCamelCase ( __lowerCamelCase ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ : str = filter(lambda __lowerCamelCase : p.requires_grad , model.parameters() )
UpperCAmelCase__ : int = sum([np.prod(p.size() ) for p in model_parameters] )
return params
SCREAMING_SNAKE_CASE__ : int = logging.getLogger(__name__)
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
'''simple docstring'''
if metric == "rouge2":
UpperCAmelCase__ : Dict = """{val_avg_rouge2:.4f}-{step_count}"""
elif metric == "bleu":
UpperCAmelCase__ : Dict = """{val_avg_bleu:.4f}-{step_count}"""
elif metric == "em":
UpperCAmelCase__ : Tuple = """{val_avg_em:.4f}-{step_count}"""
else:
raise NotImplementedError(
F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
""" function.""" )
UpperCAmelCase__ : Tuple = ModelCheckpoint(
dirpath=__lowerCamelCase , filename=__lowerCamelCase , monitor=F"val_{metric}" , mode="""max""" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
'''simple docstring'''
return EarlyStopping(
monitor=F"val_{metric}" , mode="""min""" if """loss""" in metric else """max""" , patience=__lowerCamelCase , verbose=__lowerCamelCase , )
class UpperCAmelCase_ ( pl.Callback ):
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = {f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_lowerCAmelCase )
@rank_zero_only
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=True ):
logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****" )
UpperCAmelCase__ : List[str] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
# Log results
UpperCAmelCase__ : Union[str, Any] = Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCAmelCase__ : List[str] = od / """test_results.txt"""
UpperCAmelCase__ : Union[str, Any] = od / """test_generations.txt"""
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCAmelCase__ : List[Any] = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
UpperCAmelCase__ : Any = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=_lowerCAmelCase )
generations_file.parent.mkdir(exist_ok=_lowerCAmelCase )
with open(_lowerCAmelCase , """a+""" ) as writer:
for key in sorted(_lowerCAmelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCAmelCase__ : str = metrics[key]
if isinstance(_lowerCAmelCase , torch.Tensor ):
UpperCAmelCase__ : Tuple = val.item()
UpperCAmelCase__ : List[str] = f"{key}: {val:.6f}\n"
writer.write(_lowerCAmelCase )
if not save_generations:
return
if "preds" in metrics:
UpperCAmelCase__ : Any = """\n""".join(metrics["""preds"""] )
generations_file.open("""w+""" ).write(_lowerCAmelCase )
@rank_zero_only
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
try:
UpperCAmelCase__ : int = pl_module.model.model.num_parameters()
except AttributeError:
UpperCAmelCase__ : Optional[int] = pl_module.model.num_parameters()
UpperCAmelCase__ : Optional[int] = count_trainable_parameters(_lowerCAmelCase )
# mp stands for million parameters
trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6} )
@rank_zero_only
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_lowerCAmelCase , _lowerCAmelCase , """test""" )
@rank_zero_only
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 79
|
def __lowerCAmelCase ( __magic_name__ = 1_0_0 ):
_lowercase: Dict = set()
_lowercase: List[Any] = 0
_lowercase: List[Any] = n + 1 # maximum limit
for a in range(2 , __magic_name__ ):
for b in range(2 , __magic_name__ ):
_lowercase: int = a**b # calculates the current power
collect_powers.add(__magic_name__ ) # adds the result to the set
return len(__magic_name__ )
if __name__ == "__main__":
print('Number of terms ', solution(int(str(input()).strip())))
| 226
| 0
|
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
UpperCamelCase__ =True
except ImportError:
UpperCamelCase__ =False
try:
from torch.hub import _get_torch_home
UpperCamelCase__ =_get_torch_home()
except ImportError:
UpperCamelCase__ =os.path.expanduser(
os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
)
UpperCamelCase__ =os.path.join(torch_cache_home, 'transformers')
UpperCamelCase__ ='https://cdn.huggingface.co'
UpperCamelCase__ ='https://s3.amazonaws.com/models.huggingface.co/bert'
UpperCamelCase__ ='/'.join(str(Path(__file__).resolve()).split('/')[:-1])
UpperCamelCase__ =os.path.join(PATH, 'config.yaml')
UpperCamelCase__ =os.path.join(PATH, 'attributes.txt')
UpperCamelCase__ =os.path.join(PATH, 'objects.txt')
UpperCamelCase__ =os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
UpperCamelCase__ =os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
UpperCamelCase__ =os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
UpperCamelCase__ ='pytorch_model.bin'
UpperCamelCase__ ='config.yaml'
def lowerCamelCase__ (__lowerCamelCase=OBJECTS, __lowerCamelCase=ATTRIBUTES ):
_SCREAMING_SNAKE_CASE : List[Any] = []
with open(_lowerCamelCase ) as f:
for object in f.readlines():
vg_classes.append(object.split("," )[0].lower().strip() )
_SCREAMING_SNAKE_CASE : Tuple = []
with open(_lowerCamelCase ) as f:
for object in f.readlines():
vg_attrs.append(object.split("," )[0].lower().strip() )
return vg_classes, vg_attrs
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = OrderedDict()
with open(_lowerCamelCase, "rb" ) as f:
_SCREAMING_SNAKE_CASE : Union[str, Any] = pkl.load(_lowerCamelCase )["""model"""]
for k in copy.deepcopy(list(ckp.keys() ) ):
_SCREAMING_SNAKE_CASE : Dict = ckp.pop(_lowerCamelCase )
if isinstance(_lowerCamelCase, np.ndarray ):
_SCREAMING_SNAKE_CASE : List[str] = torch.tensor(_lowerCamelCase )
else:
assert isinstance(_lowerCamelCase, torch.tensor ), type(_lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = v
return r
class lowerCAmelCase__:
'''simple docstring'''
__snake_case = {}
def __init__( self , __lowerCamelCase , __lowerCamelCase = "root" , __lowerCamelCase=0 ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : Dict = name
_SCREAMING_SNAKE_CASE : Union[str, Any] = level
_SCREAMING_SNAKE_CASE : List[str] = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
_SCREAMING_SNAKE_CASE : Dict = copy.deepcopy(_lowercase )
_SCREAMING_SNAKE_CASE : Tuple = copy.deepcopy(_lowercase )
if isinstance(_lowercase , _lowercase ):
_SCREAMING_SNAKE_CASE : int = Config(_lowercase , name=_lowercase , level=level + 1 )
_SCREAMING_SNAKE_CASE : Dict = v
setattr(self , _lowercase , _lowercase )
_SCREAMING_SNAKE_CASE : str = d
def __repr__( self ) -> int:
return str(list((self._pointer.keys()) ) )
def __setattr__( self , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
_SCREAMING_SNAKE_CASE : Optional[Any] = val
_SCREAMING_SNAKE_CASE : Union[str, Any] = val
_SCREAMING_SNAKE_CASE : List[Any] = key.split("." )
_SCREAMING_SNAKE_CASE : List[str] = len(_lowercase ) - 1
_SCREAMING_SNAKE_CASE : int = self._pointer
if len(_lowercase ) > 1:
for i, l in enumerate(_lowercase ):
if hasattr(self , _lowercase ) and isinstance(getattr(self , _lowercase ) , _lowercase ):
setattr(getattr(self , _lowercase ) , ".".join(levels[i:] ) , _lowercase )
if l == last_level:
_SCREAMING_SNAKE_CASE : Optional[Any] = val
else:
_SCREAMING_SNAKE_CASE : List[str] = pointer[l]
def UpperCamelCase_ ( self ) -> List[str]:
return self._pointer
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> Dict:
with open(F"""{file_name}""" , "w" ) as stream:
dump(_lowercase , _lowercase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
with open(F"""{file_name}""" , "w" ) as stream:
json.dump(_lowercase , _lowercase )
@staticmethod
def UpperCamelCase_ ( __lowerCamelCase ) -> Any:
with open(_lowercase ) as stream:
_SCREAMING_SNAKE_CASE : Optional[Any] = load(_lowercase , Loader=_lowercase )
return data
def __str__( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : Tuple = """ """
if self._name != "root":
_SCREAMING_SNAKE_CASE : int = F"""{t * (self._level-1)}{self._name}:\n"""
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = """"""
_SCREAMING_SNAKE_CASE : str = self._level
for i, (k, v) in enumerate(self._pointer.items() ):
if isinstance(_lowercase , _lowercase ):
r += F"""{t * (self._level)}{v}\n"""
self._level += 1
else:
r += F"""{t * (self._level)}{k}: {v} ({type(_lowercase ).__name__})\n"""
_SCREAMING_SNAKE_CASE : Tuple = level
return r[:-1]
@classmethod
def UpperCamelCase_ ( cls , __lowerCamelCase , **__lowerCamelCase ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : Any = cls.get_config_dict(_lowercase , **_lowercase )
return cls(_lowercase )
@classmethod
def UpperCamelCase_ ( cls , __lowerCamelCase , **__lowerCamelCase ) -> List[str]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = kwargs.pop("cache_dir" , _lowercase )
_SCREAMING_SNAKE_CASE : Any = kwargs.pop("force_download" , _lowercase )
_SCREAMING_SNAKE_CASE : str = kwargs.pop("resume_download" , _lowercase )
_SCREAMING_SNAKE_CASE : int = kwargs.pop("proxies" , _lowercase )
_SCREAMING_SNAKE_CASE : str = kwargs.pop("local_files_only" , _lowercase )
if os.path.isdir(_lowercase ):
_SCREAMING_SNAKE_CASE : str = os.path.join(_lowercase , _lowercase )
elif os.path.isfile(_lowercase ) or is_remote_url(_lowercase ):
_SCREAMING_SNAKE_CASE : Dict = pretrained_model_name_or_path
else:
_SCREAMING_SNAKE_CASE : List[str] = hf_bucket_url(_lowercase , filename=_lowercase , use_cdn=_lowercase )
try:
# Load from URL or cache if already cached
_SCREAMING_SNAKE_CASE : Optional[Any] = cached_path(
_lowercase , cache_dir=_lowercase , force_download=_lowercase , proxies=_lowercase , resume_download=_lowercase , local_files_only=_lowercase , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
_SCREAMING_SNAKE_CASE : Optional[int] = Config.load_yaml(_lowercase )
except EnvironmentError:
_SCREAMING_SNAKE_CASE : Dict = """Can't load config for"""
raise EnvironmentError(_lowercase )
if resolved_config_file == config_file:
print("loading configuration file from path" )
else:
print("loading configuration file cache" )
return Config.load_yaml(_lowercase ), kwargs
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = torch.load("dump.pt", map_location=in_tensor.device )
_SCREAMING_SNAKE_CASE : Union[str, Any] = in_tensor.numpy()
_SCREAMING_SNAKE_CASE : Dict = out_tensor.numpy()[0]
print(na.shape, na[0, 0, :5] )
print(na.shape, na[0, 0, :5] )
assert np.allclose(_lowerCamelCase, _lowerCamelCase, rtol=0.01, atol=0.1 ), (
f"""{sum([1 for x in np.isclose(_lowerCamelCase, _lowerCamelCase, rtol=0.01, atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %"""
" element-wise mismatch"
)
raise Exception("tensors are all good" )
# Hugging face functions below
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = urlparse(_lowerCamelCase )
return parsed.scheme in ("http", "https")
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase=True ):
_SCREAMING_SNAKE_CASE : Tuple = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
_SCREAMING_SNAKE_CASE : List[str] = """/""" not in model_id
if legacy_format:
return f"""{endpoint}/{model_id}-{filename}"""
else:
return f"""{endpoint}/{model_id}/{filename}"""
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase=None, __lowerCamelCase=0, __lowerCamelCase=None, ):
_SCREAMING_SNAKE_CASE : str = """python/{}""".format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(_lowerCamelCase, _lowerCamelCase ):
ua += "; " + "; ".join("{}/{}".format(_lowerCamelCase, _lowerCamelCase ) for k, v in user_agent.items() )
elif isinstance(_lowerCamelCase, _lowerCamelCase ):
ua += "; " + user_agent
_SCREAMING_SNAKE_CASE : Tuple = {"""user-agent""": ua}
if resume_size > 0:
_SCREAMING_SNAKE_CASE : Dict = """bytes=%d-""" % (resume_size,)
_SCREAMING_SNAKE_CASE : Tuple = requests.get(_lowerCamelCase, stream=_lowerCamelCase, proxies=_lowerCamelCase, headers=_lowerCamelCase )
if response.status_code == 416: # Range not satisfiable
return
_SCREAMING_SNAKE_CASE : str = response.headers.get("Content-Length" )
_SCREAMING_SNAKE_CASE : List[str] = resume_size + int(_lowerCamelCase ) if content_length is not None else None
_SCREAMING_SNAKE_CASE : List[str] = tqdm(
unit="B", unit_scale=_lowerCamelCase, total=_lowerCamelCase, initial=_lowerCamelCase, desc="Downloading", )
for chunk in response.iter_content(chunk_size=1024 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(_lowerCamelCase ) )
temp_file.write(_lowerCamelCase )
progress.close()
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase=None, __lowerCamelCase=False, __lowerCamelCase=None, __lowerCamelCase=10, __lowerCamelCase=False, __lowerCamelCase=None, __lowerCamelCase=False, ):
if cache_dir is None:
_SCREAMING_SNAKE_CASE : List[Any] = TRANSFORMERS_CACHE
if isinstance(_lowerCamelCase, _lowerCamelCase ):
_SCREAMING_SNAKE_CASE : int = str(_lowerCamelCase )
os.makedirs(_lowerCamelCase, exist_ok=_lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = None
if not local_files_only:
try:
_SCREAMING_SNAKE_CASE : str = requests.head(_lowerCamelCase, allow_redirects=_lowerCamelCase, proxies=_lowerCamelCase, timeout=_lowerCamelCase )
if response.status_code == 200:
_SCREAMING_SNAKE_CASE : Optional[int] = response.headers.get("ETag" )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
_SCREAMING_SNAKE_CASE : Optional[Any] = url_to_filename(_lowerCamelCase, _lowerCamelCase )
# get cache path to put the file
_SCREAMING_SNAKE_CASE : int = os.path.join(_lowerCamelCase, _lowerCamelCase )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(_lowerCamelCase ):
return cache_path
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = [
file
for file in fnmatch.filter(os.listdir(_lowerCamelCase ), filename + ".*" )
if not file.endswith(".json" ) and not file.endswith(".lock" )
]
if len(_lowerCamelCase ) > 0:
return os.path.join(_lowerCamelCase, matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
"Cannot find the requested files in the cached path and outgoing traffic has been"
" disabled. To enable model look-ups and downloads online, set 'local_files_only'"
" to False." )
return None
# From now on, etag is not None.
if os.path.exists(_lowerCamelCase ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
_SCREAMING_SNAKE_CASE : str = cache_path + """.lock"""
with FileLock(_lowerCamelCase ):
# If the download just completed while the lock was activated.
if os.path.exists(_lowerCamelCase ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
_SCREAMING_SNAKE_CASE : List[str] = cache_path + """.incomplete"""
@contextmanager
def _resumable_file_manager():
with open(_lowerCamelCase, "a+b" ) as f:
yield f
_SCREAMING_SNAKE_CASE : Tuple = _resumable_file_manager
if os.path.exists(_lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = os.stat(_lowerCamelCase ).st_size
else:
_SCREAMING_SNAKE_CASE : int = 0
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = partial(tempfile.NamedTemporaryFile, dir=_lowerCamelCase, delete=_lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
"%s not found in cache or force_download set to True, downloading to %s", _lowerCamelCase, temp_file.name, )
http_get(
_lowerCamelCase, _lowerCamelCase, proxies=_lowerCamelCase, resume_size=_lowerCamelCase, user_agent=_lowerCamelCase, )
os.replace(temp_file.name, _lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = {"""url""": url, """etag""": etag}
_SCREAMING_SNAKE_CASE : Optional[Any] = cache_path + """.json"""
with open(_lowerCamelCase, "w" ) as meta_file:
json.dump(_lowerCamelCase, _lowerCamelCase )
return cache_path
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase=None ):
_SCREAMING_SNAKE_CASE : str = url.encode("utf-8" )
_SCREAMING_SNAKE_CASE : str = shaaaa(_lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = url_hash.hexdigest()
if etag:
_SCREAMING_SNAKE_CASE : Tuple = etag.encode("utf-8" )
_SCREAMING_SNAKE_CASE : Any = shaaaa(_lowerCamelCase )
filename += "." + etag_hash.hexdigest()
if url.endswith(".h5" ):
filename += ".h5"
return filename
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase=None, __lowerCamelCase=False, __lowerCamelCase=None, __lowerCamelCase=False, __lowerCamelCase=None, __lowerCamelCase=False, __lowerCamelCase=False, __lowerCamelCase=False, ):
if cache_dir is None:
_SCREAMING_SNAKE_CASE : int = TRANSFORMERS_CACHE
if isinstance(_lowerCamelCase, _lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Tuple = str(_lowerCamelCase )
if isinstance(_lowerCamelCase, _lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = str(_lowerCamelCase )
if is_remote_url(_lowerCamelCase ):
# URL, so get it from the cache (downloading if necessary)
_SCREAMING_SNAKE_CASE : Optional[Any] = get_from_cache(
_lowerCamelCase, cache_dir=_lowerCamelCase, force_download=_lowerCamelCase, proxies=_lowerCamelCase, resume_download=_lowerCamelCase, user_agent=_lowerCamelCase, local_files_only=_lowerCamelCase, )
elif os.path.exists(_lowerCamelCase ):
# File, and it exists.
_SCREAMING_SNAKE_CASE : Union[str, Any] = url_or_filename
elif urlparse(_lowerCamelCase ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(_lowerCamelCase ) )
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(_lowerCamelCase ) )
if extract_compressed_file:
if not is_zipfile(_lowerCamelCase ) and not tarfile.is_tarfile(_lowerCamelCase ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
_SCREAMING_SNAKE_CASE : Dict = os.path.split(_lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = output_file.replace(".", "-" ) + """-extracted"""
_SCREAMING_SNAKE_CASE : Any = os.path.join(_lowerCamelCase, _lowerCamelCase )
if os.path.isdir(_lowerCamelCase ) and os.listdir(_lowerCamelCase ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
_SCREAMING_SNAKE_CASE : List[str] = output_path + """.lock"""
with FileLock(_lowerCamelCase ):
shutil.rmtree(_lowerCamelCase, ignore_errors=_lowerCamelCase )
os.makedirs(_lowerCamelCase )
if is_zipfile(_lowerCamelCase ):
with ZipFile(_lowerCamelCase, "r" ) as zip_file:
zip_file.extractall(_lowerCamelCase )
zip_file.close()
elif tarfile.is_tarfile(_lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = tarfile.open(_lowerCamelCase )
tar_file.extractall(_lowerCamelCase )
tar_file.close()
else:
raise EnvironmentError("Archive format of {} could not be identified".format(_lowerCamelCase ) )
return output_path_extracted
return output_path
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase="," ):
assert isinstance(_lowerCamelCase, _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ):
with open(_lowerCamelCase ) as f:
_SCREAMING_SNAKE_CASE : Dict = eval(f.read() )
else:
_SCREAMING_SNAKE_CASE : List[Any] = requests.get(_lowerCamelCase )
try:
_SCREAMING_SNAKE_CASE : Tuple = requests.json()
except Exception:
_SCREAMING_SNAKE_CASE : Optional[Any] = req.content.decode()
assert data is not None, "could not connect"
try:
_SCREAMING_SNAKE_CASE : Optional[int] = eval(_lowerCamelCase )
except Exception:
_SCREAMING_SNAKE_CASE : Tuple = data.split("\n" )
req.close()
return data
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = requests.get(_lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = np.array(Image.open(BytesIO(response.content ) ) )
return img
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Tuple = url.split("/" )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(_lowerCamelCase )
with open(_lowerCamelCase, "rb" ) as stream:
_SCREAMING_SNAKE_CASE : List[Any] = pkl.load(_lowerCamelCase )
_SCREAMING_SNAKE_CASE : Tuple = weights.pop("model" )
_SCREAMING_SNAKE_CASE : str = {}
for k, v in model.items():
_SCREAMING_SNAKE_CASE : List[Any] = torch.from_numpy(_lowerCamelCase )
if "running_var" in k:
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([0] )
_SCREAMING_SNAKE_CASE : Dict = k.replace("running_var", "num_batches_tracked" )
_SCREAMING_SNAKE_CASE : Tuple = zero
return new
def lowerCamelCase__ ():
print(f"""{os.path.abspath(os.path.join(_lowerCamelCase, os.pardir ) )}/demo.ipynb""" )
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase="RGB" ):
assert isinstance(_lowerCamelCase, _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = cva.imread(_lowerCamelCase )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = get_image_from_url(_lowerCamelCase )
assert img is not None, f"""could not connect to: {im}"""
_SCREAMING_SNAKE_CASE : Tuple = cva.cvtColor(_lowerCamelCase, cva.COLOR_BGR2RGB )
if input_format == "RGB":
_SCREAMING_SNAKE_CASE : Dict = img[:, :, ::-1]
return img
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase=1 ):
return (images[i : i + batch] for i in range(0, len(_lowerCamelCase ), _lowerCamelCase ))
| 719
|
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# Construct model
if gpta_config_file == "":
_SCREAMING_SNAKE_CASE : str = GPTaConfig()
else:
_SCREAMING_SNAKE_CASE : int = GPTaConfig.from_json_file(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = GPTaModel(__lowerCamelCase )
# Load weights from numpy
load_tf_weights_in_gpta(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
# Save pytorch-model
_SCREAMING_SNAKE_CASE : Optional[Any] = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
_SCREAMING_SNAKE_CASE : Tuple = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(f"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict(), __lowerCamelCase )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(__lowerCamelCase, "w", encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCamelCase__ =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--gpt2_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained OpenAI model. \n'
'This specifies the model architecture.'
),
)
UpperCamelCase__ =parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 381
| 0
|
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __UpperCAmelCase ( __A , __A , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = AutoencoderKL
_lowerCamelCase = """sample"""
_lowerCamelCase = 1e-2
@property
def snake_case_ ( self ):
__a = 4
__a = 3
__a = (32, 32)
__a = floats_tensor((batch_size, num_channels) + sizes ).to(__A )
return {"sample": image}
@property
def snake_case_ ( self ):
return (3, 32, 32)
@property
def snake_case_ ( self ):
return (3, 32, 32)
def snake_case_ ( self ):
__a = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
__a = self.dummy_input
return init_dict, inputs_dict
def snake_case_ ( self ):
pass
def snake_case_ ( self ):
pass
@unittest.skipIf(torch_device == """mps""" , """Gradient checkpointing skipped on MPS""" )
def snake_case_ ( self ):
# enable deterministic behavior for gradient checkpointing
__a , __a = self.prepare_init_args_and_inputs_for_common()
__a = self.model_class(**__A )
model.to(__A )
assert not model.is_gradient_checkpointing and model.training
__a = model(**__A ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
__a = torch.randn_like(__A )
__a = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
__a = self.model_class(**__A )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(__A )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
__a = model_a(**__A ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
__a = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1E-5 )
__a = dict(model.named_parameters() )
__a = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5E-5 ) )
def snake_case_ ( self ):
__a , __a = AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy""" , output_loading_info=__A )
self.assertIsNotNone(__A )
self.assertEqual(len(loading_info["""missing_keys"""] ) , 0 )
model.to(__A )
__a = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def snake_case_ ( self ):
__a = AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy""" )
__a = model.to(__A )
model.eval()
if torch_device == "mps":
__a = torch.manual_seed(0 )
else:
__a = torch.Generator(device=__A ).manual_seed(0 )
__a = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
__a = image.to(__A )
with torch.no_grad():
__a = model(__A , sample_posterior=__A , generator=__A ).sample
__a = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
__a = torch.tensor(
[
-4.0078E-01,
-3.8323E-04,
-1.2681E-01,
-1.1462E-01,
2.0095E-01,
1.0893E-01,
-8.8247E-02,
-3.0361E-01,
-9.8644E-03,
] )
elif torch_device == "cpu":
__a = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
__a = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(__A , __A , rtol=1E-2 ) )
@slow
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self , __A , __A ):
return f'''gaussian_noise_s={seed}_shape={'_'.join([str(__A ) for s in shape] )}.npy'''
def snake_case_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self , __A=0 , __A=(4, 3, 512, 512) , __A=False ):
__a = torch.floataa if fpaa else torch.floataa
__a = torch.from_numpy(load_hf_numpy(self.get_file_format(__A , __A ) ) ).to(__A ).to(__A )
return image
def snake_case_ ( self , __A="CompVis/stable-diffusion-v1-4" , __A=False ):
__a = """fp16""" if fpaa else None
__a = torch.floataa if fpaa else torch.floataa
__a = AutoencoderKL.from_pretrained(
__A , subfolder="""vae""" , torch_dtype=__A , revision=__A , )
model.to(__A ).eval()
return model
def snake_case_ ( self , __A=0 ):
if torch_device == "mps":
return torch.manual_seed(__A )
return torch.Generator(device=__A ).manual_seed(__A )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def snake_case_ ( self , __A , __A , __A ):
__a = self.get_sd_vae_model()
__a = self.get_sd_image(__A )
__a = self.get_generator(__A )
with torch.no_grad():
__a = model(__A , generator=__A , sample_posterior=__A ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, -2:, :2].flatten().float().cpu()
__a = torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice )
assert torch_all_close(__A , __A , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def snake_case_ ( self , __A , __A ):
__a = self.get_sd_vae_model(fpaa=__A )
__a = self.get_sd_image(__A , fpaa=__A )
__a = self.get_generator(__A )
with torch.no_grad():
__a = model(__A , generator=__A , sample_posterior=__A ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, :2, -2:].flatten().float().cpu()
__a = torch.tensor(__A )
assert torch_all_close(__A , __A , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def snake_case_ ( self , __A , __A , __A ):
__a = self.get_sd_vae_model()
__a = self.get_sd_image(__A )
with torch.no_grad():
__a = model(__A ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, -2:, :2].flatten().float().cpu()
__a = torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice )
assert torch_all_close(__A , __A , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def snake_case_ ( self , __A , __A ):
__a = self.get_sd_vae_model()
__a = self.get_sd_image(__A , shape=(3, 4, 64, 64) )
with torch.no_grad():
__a = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
__a = sample[-1, -2:, :2, -2:].flatten().cpu()
__a = torch.tensor(__A )
assert torch_all_close(__A , __A , atol=1E-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def snake_case_ ( self , __A , __A ):
__a = self.get_sd_vae_model(fpaa=__A )
__a = self.get_sd_image(__A , shape=(3, 4, 64, 64) , fpaa=__A )
with torch.no_grad():
__a = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
__a = sample[-1, -2:, :2, -2:].flatten().float().cpu()
__a = torch.tensor(__A )
assert torch_all_close(__A , __A , atol=5E-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="""xformers is not required when using PyTorch 2.0.""" )
def snake_case_ ( self , __A ):
__a = self.get_sd_vae_model(fpaa=__A )
__a = self.get_sd_image(__A , shape=(3, 4, 64, 64) , fpaa=__A )
with torch.no_grad():
__a = model.decode(__A ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
__a = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(__A , __A , atol=1E-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="""xformers is not required when using PyTorch 2.0.""" )
def snake_case_ ( self , __A ):
__a = self.get_sd_vae_model()
__a = self.get_sd_image(__A , shape=(3, 4, 64, 64) )
with torch.no_grad():
__a = model.decode(__A ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
__a = model.decode(__A ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(__A , __A , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def snake_case_ ( self , __A , __A ):
__a = self.get_sd_vae_model()
__a = self.get_sd_image(__A )
__a = self.get_generator(__A )
with torch.no_grad():
__a = model.encode(__A ).latent_dist
__a = dist.sample(generator=__A )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
__a = sample[0, -1, -3:, -3:].flatten().cpu()
__a = torch.tensor(__A )
__a = 3E-3 if torch_device != """mps""" else 1E-2
assert torch_all_close(__A , __A , atol=__A )
| 99
|
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def a (lowerCAmelCase__ ):
if "://" in dataset_path:
__a = dataset_path.split("""://""" )[1]
return dataset_path
def a (lowerCAmelCase__ ):
if fs is not None and fs.protocol != "file":
return True
else:
return False
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__a = not is_remote_filesystem(lowerCAmelCase__ )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(lowerCAmelCase__ ) , fs._strip_protocol(lowerCAmelCase__ ) )
else:
fs.mv(lowerCAmelCase__ , lowerCAmelCase__ , recursive=lowerCAmelCase__ )
def a ():
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
__a = None
__a = None
__a = threading.Lock()
| 99
| 1
|
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
snake_case = """src/diffusers"""
# Matches is_xxx_available()
snake_case = re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
snake_case = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
snake_case = """
{0} = None
"""
snake_case = """
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
"""
snake_case = """
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
lowerCAmelCase__ : Any = _re_backend.findall(lowerCamelCase_ )
if len(lowerCamelCase_ ) == 0:
return None
return "_and_".join(lowerCamelCase_ )
def UpperCAmelCase_ ( ):
"""simple docstring"""
with open(os.path.join(lowerCamelCase_ , "__init__.py" ) , "r" , encoding="utf-8" , newline="\n" ) as f:
lowerCAmelCase__ : Dict = f.readlines()
# Get to the point we do the actual imports for type checking
lowerCAmelCase__ : Optional[int] = 0
lowerCAmelCase__ : Dict = {}
# Go through the end of the file
while line_index < len(lowerCamelCase_ ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
lowerCAmelCase__ : int = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith("else:" ):
line_index += 1
line_index += 1
lowerCAmelCase__ : str = []
# Until we unindent, add backend objects to the list
while line_index < len(lowerCamelCase_ ) and len(lines[line_index] ) > 1:
lowerCAmelCase__ : Tuple = lines[line_index]
lowerCAmelCase__ : Dict = _re_single_line_import.search(lowerCamelCase_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", " ) )
elif line.startswith(" " * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(lowerCamelCase_ ) > 0:
lowerCAmelCase__ : List[str] = objects
else:
line_index += 1
return backend_specific_objects
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if name.isupper():
return DUMMY_CONSTANT.format(lowerCamelCase_ )
elif name.islower():
return DUMMY_FUNCTION.format(lowerCamelCase_ , lowerCamelCase_ )
else:
return DUMMY_CLASS.format(lowerCamelCase_ , lowerCamelCase_ )
def UpperCAmelCase_ ( lowerCamelCase_=None ):
"""simple docstring"""
if backend_specific_objects is None:
lowerCAmelCase__ : Optional[Any] = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
lowerCAmelCase__ : int = {}
for backend, objects in backend_specific_objects.items():
lowerCAmelCase__ : Dict = "[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_" ) ) + "]"
lowerCAmelCase__ : Optional[int] = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(lowerCamelCase_ , lowerCamelCase_ ) for o in objects] )
lowerCAmelCase__ : Dict = dummy_file
return dummy_files
def UpperCAmelCase_ ( lowerCamelCase_=False ):
"""simple docstring"""
lowerCAmelCase__ : Any = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
lowerCAmelCase__ : Optional[Any] = {"torch": "pt"}
# Locate actual dummy modules and read their content.
lowerCAmelCase__ : str = os.path.join(lowerCamelCase_ , "utils" )
lowerCAmelCase__ : List[Any] = {
backend: os.path.join(lowerCamelCase_ , f'''dummy_{short_names.get(lowerCamelCase_ , lowerCamelCase_ )}_objects.py''' )
for backend in dummy_files.keys()
}
lowerCAmelCase__ : Union[str, Any] = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(lowerCamelCase_ ):
with open(lowerCamelCase_ , "r" , encoding="utf-8" , newline="\n" ) as f:
lowerCAmelCase__ : int = f.read()
else:
lowerCAmelCase__ : str = ""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f'''Updating diffusers.utils.dummy_{short_names.get(lowerCamelCase_ , lowerCamelCase_ )}_objects.py as the main '''
"__init__ has new objects." )
with open(dummy_file_paths[backend] , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
"The main __init__ has objects that are not present in "
f'''diffusers.utils.dummy_{short_names.get(lowerCamelCase_ , lowerCamelCase_ )}_objects.py. Run `make fix-copies` '''
"to fix this." )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
snake_case = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 568
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""ConvNextFeatureExtractor"""]
snake_case = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 568
| 1
|
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class a__( _a ):
@slow
@require_torch
def _lowercase ( self ) -> Optional[int]:
snake_case__ =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' , 'prajjwal1/bert-tiny' )
snake_case__ =BertTokenizer.from_pretrained('bert-base-uncased' )
snake_case__ =bertabert.config.encoder.vocab_size
snake_case__ =tokenizer.sep_token_id
snake_case__ =tokenizer.cls_token_id
snake_case__ =128
snake_case__ =datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='train[:1%]' )
snake_case__ =datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='validation[:1%]' )
snake_case__ =train_dataset.select(range(32 ) )
snake_case__ =val_dataset.select(range(16 ) )
snake_case__ =4
def _map_to_encoder_decoder_inputs(_UpperCAmelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
snake_case__ =tokenizer(batch['article'] , padding='max_length' , truncation=A_ , max_length=512 )
snake_case__ =tokenizer(batch['highlights'] , padding='max_length' , truncation=A_ , max_length=128 )
snake_case__ =inputs.input_ids
snake_case__ =inputs.attention_mask
snake_case__ =outputs.input_ids
snake_case__ =outputs.input_ids.copy()
snake_case__ =[
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
]
snake_case__ =outputs.attention_mask
assert all(len(A_ ) == 512 for x in inputs.input_ids )
assert all(len(A_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_UpperCAmelCase ):
snake_case__ =pred.label_ids
snake_case__ =pred.predictions
# all unnecessary tokens are removed
snake_case__ =tokenizer.batch_decode(A_ , skip_special_tokens=A_ )
snake_case__ =tokenizer.batch_decode(A_ , skip_special_tokens=A_ )
snake_case__ =sum([int(pred_str[i] == label_str[i] ) for i in range(len(A_ ) )] ) / len(A_ )
return {"accuracy": accuracy}
# map train dataset
snake_case__ =train_dataset.map(
_map_to_encoder_decoder_inputs , batched=A_ , batch_size=A_ , remove_columns=['article', 'highlights'] , )
train_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
# same for validation dataset
snake_case__ =val_dataset.map(
_map_to_encoder_decoder_inputs , batched=A_ , batch_size=A_ , remove_columns=['article', 'highlights'] , )
val_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
snake_case__ =self.get_auto_remove_tmp_dir()
snake_case__ =SeqaSeqTrainingArguments(
output_dir=A_ , per_device_train_batch_size=A_ , per_device_eval_batch_size=A_ , predict_with_generate=A_ , evaluation_strategy='steps' , do_train=A_ , do_eval=A_ , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
snake_case__ =SeqaSeqTrainer(
model=A_ , args=A_ , compute_metrics=_compute_metrics , train_dataset=A_ , eval_dataset=A_ , tokenizer=A_ , )
# start training
trainer.train()
| 538
|
__snake_case = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
'''l''': '''ABABA''',
'''m''': '''ABABB''',
'''n''': '''ABBAA''',
'''o''': '''ABBAB''',
'''p''': '''ABBBA''',
'''q''': '''ABBBB''',
'''r''': '''BAAAA''',
'''s''': '''BAAAB''',
'''t''': '''BAABA''',
'''u''': '''BAABB''',
'''v''': '''BBBAB''',
'''w''': '''BABAA''',
'''x''': '''BABAB''',
'''y''': '''BABBA''',
'''z''': '''BABBB''',
''' ''': ''' ''',
}
__snake_case = {value: key for key, value in encode_dict.items()}
def _A ( _lowercase ) -> str:
"""simple docstring"""
__UpperCamelCase = ''
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception('encode() accepts only letters of the alphabet and spaces' )
return encoded
def _A ( _lowercase ) -> str:
"""simple docstring"""
if set(_lowercase ) - {"A", "B", " "} != set():
raise Exception('decode() accepts only \'A\', \'B\' and spaces' )
__UpperCamelCase = ''
for word in coded.split():
while len(_lowercase ) != 0:
decoded += decode_dict[word[:5]]
__UpperCamelCase = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 1
| 0
|
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _snake_case ( _snake_case : List[Any] ) -> Any:
'''simple docstring'''
_A = {}
_A = tokenizer(example['content'] , truncation=_snake_case )['input_ids']
_A = len(example['content'] ) / len(output['input_ids'] )
return output
a = HfArgumentParser(PretokenizationArguments)
a = parser.parse_args()
if args.num_workers is None:
a = multiprocessing.cpu_count()
a = AutoTokenizer.from_pretrained(args.tokenizer_dir)
a = time.time()
a = load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')
a = time.time()
a = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')
a = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')
| 505
|
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a = logging.get_logger(__name__)
class lowercase_ :
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : str = None , _UpperCAmelCase : uuid.UUID = None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Tuple=None ):
if not conversation_id:
_A = uuid.uuida()
if past_user_inputs is None:
_A = []
if generated_responses is None:
_A = []
_A = conversation_id
_A = past_user_inputs
_A = generated_responses
_A = text
def __eq__( self : Union[str, Any] , _UpperCAmelCase : Optional[int] ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ):
if self.new_user_input:
if overwrite:
logger.warning(
F'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
F'''with: "{text}".''' )
_A = text
else:
logger.warning(
F'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
F'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
_A = text
def lowerCAmelCase_ ( self : List[str] ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
_A = None
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : str ):
self.generated_responses.append(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self : int ):
_A = F'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
_A = 'user' if is_user else 'bot'
output += F'''{name} >> {text} \n'''
return output
@add_end_docstrings(
__lowerCAmelCase , r'''
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
''' , )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , *_UpperCAmelCase : Optional[int] , **_UpperCAmelCase : Any ):
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
_A = self.tokenizer.eos_token
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Dict=None , **_UpperCAmelCase : List[Any] ):
_A = {}
_A = {}
_A = {}
if min_length_for_response is not None:
_A = min_length_for_response
if minimum_tokens is not None:
_A = minimum_tokens
if "max_length" in generate_kwargs:
_A = generate_kwargs['max_length']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
_A = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(_UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self : Any , _UpperCAmelCase : Union[Conversation, List[Conversation]] , _UpperCAmelCase : int=0 , **_UpperCAmelCase : str ):
_A = super().__call__(_UpperCAmelCase , num_workers=_UpperCAmelCase , **_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and len(_UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Conversation , _UpperCAmelCase : int=32 ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('ConversationalPipeline, expects Conversation as inputs' )
if conversation.new_user_input is None:
raise ValueError(
F'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
'Add user inputs with the conversation\'s `add_user_input` method' )
if hasattr(self.tokenizer , '_build_conversation_input_ids' ):
_A = self.tokenizer._build_conversation_input_ids(_UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
_A = self._legacy_parse_and_tokenize(_UpperCAmelCase )
if self.framework == "pt":
_A = torch.LongTensor([input_ids] )
elif self.framework == "tf":
_A = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict=10 , **_UpperCAmelCase : Any ):
_A = generate_kwargs.get('max_length' , self.model.config.max_length )
_A = model_inputs['input_ids'].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
_A = max_length - minimum_tokens
_A = model_inputs['input_ids'][:, -trim:]
if "attention_mask" in model_inputs:
_A = model_inputs['attention_mask'][:, -trim:]
_A = model_inputs.pop('conversation' )
_A = max_length
_A = self.model.generate(**_UpperCAmelCase , **_UpperCAmelCase )
if self.model.config.is_encoder_decoder:
_A = 1
else:
_A = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict=True ):
_A = model_outputs['output_ids']
_A = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase , )
_A = model_outputs['conversation']
conversation.mark_processed()
conversation.append_response(_UpperCAmelCase )
return conversation
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Conversation ):
_A = self.tokenizer.eos_token_id
_A = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) )
if len(_UpperCAmelCase ) > self.tokenizer.model_max_length:
_A = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 505
| 1
|
'''simple docstring'''
import math
import sys
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =''''''
try:
with open(__SCREAMING_SNAKE_CASE , '''rb''' ) as binary_file:
_UpperCamelCase =binary_file.read()
for dat in data:
_UpperCamelCase =f'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase ={'''0''': '''0''', '''1''': '''1'''}
_UpperCamelCase , _UpperCamelCase ='''''', ''''''
_UpperCamelCase =len(__SCREAMING_SNAKE_CASE )
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
_UpperCamelCase =lexicon[curr_string]
result += last_match_id
_UpperCamelCase =last_match_id + '''0'''
if math.loga(__SCREAMING_SNAKE_CASE ).is_integer():
_UpperCamelCase ={}
for curr_key in list(__SCREAMING_SNAKE_CASE ):
_UpperCamelCase =lexicon.pop(__SCREAMING_SNAKE_CASE )
_UpperCamelCase =new_lex
_UpperCamelCase =last_match_id + '''1'''
index += 1
_UpperCamelCase =''''''
return result
def _a (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =8
try:
with open(__SCREAMING_SNAKE_CASE , '''wb''' ) as opened_file:
_UpperCamelCase =[
to_write[i : i + byte_length]
for i in range(0 , len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(__SCREAMING_SNAKE_CASE , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =0
for letter in data_bits:
if letter == "1":
break
counter += 1
_UpperCamelCase =data_bits[counter:]
_UpperCamelCase =data_bits[counter + 1 :]
return data_bits
def _a (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =read_file_binary(__SCREAMING_SNAKE_CASE )
_UpperCamelCase =remove_prefix(__SCREAMING_SNAKE_CASE )
_UpperCamelCase =decompress_data(__SCREAMING_SNAKE_CASE )
write_file_binary(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 404
|
'''simple docstring'''
import os
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =len(grid[0] )
_UpperCamelCase =len(__SCREAMING_SNAKE_CASE )
_UpperCamelCase =0
_UpperCamelCase =0
_UpperCamelCase =0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(__SCREAMING_SNAKE_CASE ):
for j in range(n_rows - 3 ):
_UpperCamelCase =grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
_UpperCamelCase =grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
_UpperCamelCase =(
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
_UpperCamelCase =(
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
_UpperCamelCase =max(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if max_product > largest:
_UpperCamelCase =max_product
return largest
def _a ():
"""simple docstring"""
_UpperCamelCase =[]
with open(os.path.dirname(__SCREAMING_SNAKE_CASE ) + '''/grid.txt''' ) as file:
for line in file:
grid.append(line.strip('''\n''' ).split(''' ''' ) )
_UpperCamelCase =[[int(__SCREAMING_SNAKE_CASE ) for i in grid[j]] for j in range(len(__SCREAMING_SNAKE_CASE ) )]
return largest_product(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(solution())
| 404
| 1
|
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
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 (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Optional[int] , __A : Dict , __A : Dict=3 , __A : Dict=7 , __A : List[Any]=True , __A : Tuple=True , __A : Dict=False , __A : int=True , __A : Any=99 , __A : Optional[int]=32 , __A : str=5 , __A : List[str]=4 , __A : Dict=37 , __A : Dict="gelu" , __A : int=0.1 , __A : int=0.1 , __A : Optional[Any]=512 , __A : str=16 , __A : Union[str, Any]=2 , __A : Union[str, Any]=0.0_2 , __A : int=3 , __A : Optional[Any]=4 , __A : str=None , ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = seq_length
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_input_mask
lowerCAmelCase__ = use_token_type_ids
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = type_vocab_size
lowerCAmelCase__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = num_choices
lowerCAmelCase__ = scope
def lowercase__ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ = None
if self.use_input_mask:
lowerCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase__ ( self : Tuple ) -> str:
'''simple docstring'''
return FalconConfig(
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 , pad_token_id=1 , new_decoder_architecture=__A , )
def lowercase__ ( self : Optional[Any] , __A : Dict , __A : List[str] , __A : Tuple , __A : List[Any] , __A : Any , __A : Dict , __A : Optional[int] ) -> Dict:
'''simple docstring'''
lowerCAmelCase__ = FalconModel(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A , attention_mask=__A )
lowerCAmelCase__ = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Any , __A : List[Any] , __A : Dict , __A : Union[str, Any] , __A : int , __A : Optional[int] , __A : int , __A : Union[str, Any] , __A : str , __A : Dict , ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = True
lowerCAmelCase__ = FalconModel(__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(
__A , attention_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )
lowerCAmelCase__ = model(
__A , attention_mask=__A , encoder_hidden_states=__A , )
lowerCAmelCase__ = model(__A , attention_mask=__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self : Union[str, Any] , __A : Union[str, Any] , __A : Any , __A : List[str] , __A : Optional[int] , __A : Optional[int] , __A : str , __A : List[str] , __A : Any , __A : List[str] , ) -> str:
'''simple docstring'''
lowerCAmelCase__ = FalconForCausalLM(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A , attention_mask=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self : int , __A : Optional[Any] , __A : Tuple , __A : Optional[Any] , __A : str , __A : Dict , __A : str , __A : Optional[Any] , __A : Optional[int] , __A : Optional[Any] , ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = FalconForCausalLM(config=__A )
model.to(__A )
model.eval()
# first forward pass
lowerCAmelCase__ = model(
__A , attention_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , use_cache=__A , )
lowerCAmelCase__ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase__ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCAmelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase__ = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCAmelCase__ = model(
__A , attention_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , output_hidden_states=__A , )["""hidden_states"""][0]
lowerCAmelCase__ = 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
lowerCAmelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase__ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase__ = 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 lowercase__ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,
) = config_and_inputs
lowerCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( _A, _A, _A, unittest.TestCase ):
'''simple docstring'''
A__ = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
A__ = (FalconForCausalLM,) if is_torch_available() else ()
A__ = (
{
'''feature-extraction''': FalconModel,
'''text-classification''': FalconForSequenceClassification,
'''text-generation''': FalconForCausalLM,
'''question-answering''': FalconForQuestionAnswering,
'''token-classification''': FalconForTokenClassification,
'''zero-shot''': FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
A__ = False
A__ = False
def lowercase__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = FalconModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=__A , hidden_size=37 )
def lowercase__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowercase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ ,*lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
lowerCAmelCase__ = alibi
self.model_tester.create_and_check_model(__A , *__A )
def lowercase__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ ,lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = 3
lowerCAmelCase__ = input_dict["""input_ids"""]
lowerCAmelCase__ = input_ids.ne(1 ).to(__A )
lowerCAmelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase__ = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A , attention_mask=__A , labels=__A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowercase__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ ,lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = 3
lowerCAmelCase__ = """single_label_classification"""
lowerCAmelCase__ = input_dict["""input_ids"""]
lowerCAmelCase__ = input_ids.ne(1 ).to(__A )
lowerCAmelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase__ = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A , attention_mask=__A , labels=__A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowercase__ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ ,lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = input_dict["""input_ids"""]
lowerCAmelCase__ = FalconForCausalLM(__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A , use_cache=__A )
lowerCAmelCase__ = input_ids.shape[0]
lowerCAmelCase__ = model._convert_to_rw_cache(result.past_key_values )
lowerCAmelCase__ = model._convert_cache_to_standard_format(__A , __A )
for layer in range(len(__A ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def lowercase__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
lowerCAmelCase__ ,lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = 3
lowerCAmelCase__ = """multi_label_classification"""
lowerCAmelCase__ = input_dict["""input_ids"""]
lowerCAmelCase__ = input_ids.ne(1 ).to(__A )
lowerCAmelCase__ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase__ = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A , attention_mask=__A , labels=__A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowercase__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ ,lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(__A , """use_cache""" ):
return
lowerCAmelCase__ = model_class(__A ).to(__A )
if "use_cache" not in inputs:
lowerCAmelCase__ = True
lowerCAmelCase__ = model(**__A )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
lowerCAmelCase__ = (
getattr(__A , """decoder_layers""" , __A )
or getattr(__A , """num_decoder_layers""" , __A )
or config.num_hidden_layers
)
lowerCAmelCase__ = getattr(__A , """num_kv_heads""" , config.num_attention_heads )
lowerCAmelCase__ = getattr(__A , """d_model""" , config.hidden_size )
lowerCAmelCase__ = embed_dim // num_attention_heads
lowerCAmelCase__ = outputs["""past_key_values"""]
self.assertEqual(len(__A ) , __A )
lowerCAmelCase__ ,lowerCAmelCase__ = inputs["""input_ids"""].shape
for i in range(__A ):
if config.new_decoder_architecture:
lowerCAmelCase__ = config.num_attention_heads
elif config.multi_query:
lowerCAmelCase__ = 1
self.assertEqual(len(past_kv[0] ) , 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowercase__ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
lowerCAmelCase__ = FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b""" )
model.eval()
model.to(__A )
lowerCAmelCase__ = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(__A )
lowerCAmelCase__ = (
"""My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
)
lowerCAmelCase__ = model.generate(**__A , do_sample=__A , max_new_tokens=19 )
lowerCAmelCase__ = tokenizer.batch_decode(__A )[0]
self.assertEqual(__A , __A )
@slow
def lowercase__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
lowerCAmelCase__ = AutoTokenizer.from_pretrained(__A )
lowerCAmelCase__ = FalconForCausalLM.from_pretrained(__A )
model.eval()
model.to(__A )
lowerCAmelCase__ = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(__A )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**__A , do_sample=__A , max_new_tokens=4 )
model.generate(**__A , do_sample=__A , max_new_tokens=4 )
model.generate(**__A , num_beams=2 , max_new_tokens=4 )
@slow
def lowercase__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
lowerCAmelCase__ = AutoTokenizer.from_pretrained(__A )
lowerCAmelCase__ = FalconForCausalLM.from_pretrained(__A )
model.eval()
model.to(device=__A )
lowerCAmelCase__ = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(__A )
# Test results are the same with and without cache
lowerCAmelCase__ = model.generate(**__A , do_sample=__A , max_new_tokens=20 , use_cache=__A )
lowerCAmelCase__ = model.generate(**__A , do_sample=__A , max_new_tokens=20 , use_cache=__A )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 717
|
'''simple docstring'''
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 lowerCamelCase__ ( _A ):
'''simple docstring'''
def __init__( self : Optional[int] , __A : int=None , __A : Union[str, Any]=None , *__A : Optional[Any] , **__A : Optional[int] ) -> str:
'''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__}'''
)
lowerCAmelCase__ = self.model.config
else:
lowerCAmelCase__ = config
lowerCAmelCase__ = data_args
lowerCAmelCase__ = 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:
lowerCAmelCase__ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowerCAmelCase__ = label_smoothed_nll_loss
def lowercase__ ( self : Optional[Any] , __A : int ) -> Tuple:
'''simple docstring'''
if self.optimizer is None:
lowerCAmelCase__ = ["""bias""", """LayerNorm.weight"""]
lowerCAmelCase__ = [
{
"""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,
},
]
lowerCAmelCase__ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowerCAmelCase__ = Adafactor
lowerCAmelCase__ = {"""scale_parameter""": False, """relative_step""": False}
else:
lowerCAmelCase__ = AdamW
lowerCAmelCase__ = {
"""betas""": (self.args.adam_betaa, self.args.adam_betaa),
"""eps""": self.args.adam_epsilon,
}
lowerCAmelCase__ = self.args.learning_rate
if self.sharded_ddp:
lowerCAmelCase__ = OSS(
params=__A , optim=__A , **__A , )
else:
lowerCAmelCase__ = optimizer_cls(__A , **__A )
if self.lr_scheduler is None:
lowerCAmelCase__ = self._get_lr_scheduler(__A )
else: # ignoring --lr_scheduler
logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.""" )
def lowercase__ ( self : Optional[Any] , __A : Dict ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowerCAmelCase__ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowerCAmelCase__ = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowerCAmelCase__ = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=__A )
return scheduler
def lowercase__ ( self : List[Any] ) -> 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 lowercase__ ( self : int , __A : Optional[Any] , __A : List[str] , __A : List[str] ) -> 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
lowerCAmelCase__ = model(**__A , use_cache=__A )[0]
lowerCAmelCase__ = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowerCAmelCase__ ,lowerCAmelCase__ = model(**__A , labels=__A , use_cache=__A )[:2]
else:
# compute label smoothed loss
lowerCAmelCase__ = model(**__A , use_cache=__A )[0]
lowerCAmelCase__ = torch.nn.functional.log_softmax(__A , dim=-1 )
lowerCAmelCase__ ,lowerCAmelCase__ = self.loss_fn(__A , __A , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def lowercase__ ( self : Any , __A : Optional[int] , __A : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase__ = inputs.pop("""labels""" )
lowerCAmelCase__ ,lowerCAmelCase__ = self._compute_loss(__A , __A , __A )
return loss
def lowercase__ ( 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'''
lowerCAmelCase__ = self._prepare_inputs(__A )
lowerCAmelCase__ = {
"""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:
lowerCAmelCase__ = 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"]:
lowerCAmelCase__ = self._pad_tensors_to_max_len(__A , gen_kwargs["""max_length"""] )
lowerCAmelCase__ = inputs.pop("""labels""" )
with torch.no_grad():
# compute loss on predict data
lowerCAmelCase__ ,lowerCAmelCase__ = self._compute_loss(__A , __A , __A )
lowerCAmelCase__ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowerCAmelCase__ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowerCAmelCase__ = self._pad_tensors_to_max_len(__A , gen_kwargs["""max_length"""] )
return (loss, logits, labels)
def lowercase__ ( self : Optional[Any] , __A : Union[str, Any] , __A : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = 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}''' )
lowerCAmelCase__ = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowerCAmelCase__ = tensor
return padded_tensor
| 211
| 0
|
import collections
import importlib.util
import os
import re
from pathlib import Path
_snake_case : Optional[int] = "src/transformers"
# Matches is_xxx_available()
_snake_case : List[str] = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
_snake_case : Optional[Any] = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_snake_case : Tuple = re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
_snake_case : Any = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
_snake_case : Tuple = re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_snake_case : Optional[Any] = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
_snake_case : Union[str, Any] = re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
_snake_case : int = re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
_snake_case : Union[str, Any] = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
_snake_case : Optional[int] = re.compile(r"^\s*try:")
# Catches a line with else:
_snake_case : int = re.compile(r"^\s*else:")
def __snake_case ( __magic_name__ ):
'''simple docstring'''
if _re_test_backend.search(__magic_name__ ) is None:
return None
lowercase = [b[0] for b in _re_backend.findall(__magic_name__ )]
backends.sort()
return "_and_".join(__magic_name__ )
def __snake_case ( __magic_name__ ):
'''simple docstring'''
with open(__magic_name__ , "r" , encoding="utf-8" , newline="\n" ) as f:
lowercase = f.readlines()
lowercase = 0
while line_index < len(__magic_name__ ) and not lines[line_index].startswith("_import_structure = {" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__magic_name__ ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase = []
while not lines[line_index].startswith("if TYPE_CHECKING" ) and find_backend(lines[line_index] ) is None:
lowercase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__magic_name__ ):
lowercase = _re_one_line_import_struct.search(__magic_name__ ).groups()[0]
lowercase = re.findall("\[([^\]]+)\]" , __magic_name__ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(", " )] )
line_index += 1
continue
lowercase = _re_import_struct_key_value.search(__magic_name__ )
if single_line_import_search is not None:
lowercase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", " ) if len(__magic_name__ ) > 0]
objects.extend(__magic_name__ )
elif line.startswith(" " * 8 + "\"" ):
objects.append(line[9:-3] )
line_index += 1
lowercase = {"none": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("if TYPE_CHECKING" ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(" " * 4 ):
lowercase = lines[line_index]
if _re_import_struct_add_one.search(__magic_name__ ) is not None:
objects.append(_re_import_struct_add_one.search(__magic_name__ ).groups()[0] )
elif _re_import_struct_add_many.search(__magic_name__ ) is not None:
lowercase = _re_import_struct_add_many.search(__magic_name__ ).groups()[0].split(", " )
lowercase = [obj[1:-1] for obj in imports if len(__magic_name__ ) > 0]
objects.extend(__magic_name__ )
elif _re_between_brackets.search(__magic_name__ ) is not None:
lowercase = _re_between_brackets.search(__magic_name__ ).groups()[0].split(", " )
lowercase = [obj[1:-1] for obj in imports if len(__magic_name__ ) > 0]
objects.extend(__magic_name__ )
elif _re_quote_object.search(__magic_name__ ) is not None:
objects.append(_re_quote_object.search(__magic_name__ ).groups()[0] )
elif line.startswith(" " * 8 + "\"" ):
objects.append(line[9:-3] )
elif line.startswith(" " * 12 + "\"" ):
objects.append(line[13:-3] )
line_index += 1
lowercase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase = []
while (
line_index < len(__magic_name__ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("else" )
):
lowercase = lines[line_index]
lowercase = _re_import.search(__magic_name__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", " ) )
elif line.startswith(" " * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase = {"none": objects}
# Let's continue with backend-specific objects
while line_index < len(__magic_name__ ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(" " * 8 ):
lowercase = lines[line_index]
lowercase = _re_import.search(__magic_name__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", " ) )
elif line.startswith(" " * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def __snake_case ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
def find_duplicates(__magic_name__ ):
return [k for k, v in collections.Counter(__magic_name__ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase = []
for key in import_dict_objects.keys():
lowercase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowercase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase = "base imports" if key == "none" else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def __snake_case ( ):
'''simple docstring'''
lowercase = []
for root, _, files in os.walk(__magic_name__ ):
if "__init__.py" in files:
lowercase = os.path.join(__magic_name__ , "__init__.py" )
lowercase = parse_init(__magic_name__ )
if objects is not None:
lowercase = analyze_results(*__magic_name__ )
if len(__magic_name__ ) > 0:
lowercase = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append("\n".join(__magic_name__ ) )
if len(__magic_name__ ) > 0:
raise ValueError("\n\n".join(__magic_name__ ) )
def __snake_case ( ):
'''simple docstring'''
lowercase = []
for path, directories, files in os.walk(__magic_name__ ):
for folder in directories:
# Ignore private modules
if folder.startswith("_" ):
directories.remove(__magic_name__ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__magic_name__ ) / folder).glob("*.py" ) ) ) == 0:
continue
lowercase = str((Path(__magic_name__ ) / folder).relative_to(__magic_name__ ) )
lowercase = short_path.replace(os.path.sep , "." )
submodules.append(__magic_name__ )
for fname in files:
if fname == "__init__.py":
continue
lowercase = str((Path(__magic_name__ ) / fname).relative_to(__magic_name__ ) )
lowercase = short_path.replace(".py" , "" ).replace(os.path.sep , "." )
if len(submodule.split("." ) ) == 1:
submodules.append(__magic_name__ )
return submodules
_snake_case : Optional[int] = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
]
def __snake_case ( ):
'''simple docstring'''
lowercase = importlib.util.spec_from_file_location(
"transformers" , os.path.join(__magic_name__ , "__init__.py" ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
lowercase = spec.loader.load_module()
lowercase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(__magic_name__ ) > 0:
lowercase = "\n".join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
"The following submodules are not properly registered in the main init of Transformers:\n"
F'''{list_of_modules}\n'''
"Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value." )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 441
|
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class UpperCamelCase_ ( __a ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE( self :Tuple ) ->Optional[int]:
lowercase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCAmelCase__ , "tf_padding" ) )
self.parent.assertTrue(hasattr(lowerCAmelCase__ , "depth_multiplier" ) )
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self :Dict , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any]=13 , lowerCAmelCase__ :Tuple=3 , lowerCAmelCase__ :Optional[int]=32 , lowerCAmelCase__ :Any=0.25 , lowerCAmelCase__ :Dict=8 , lowerCAmelCase__ :Optional[int]=8 , lowerCAmelCase__ :List[str]=6 , lowerCAmelCase__ :List[Any]=32 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Dict="relu6" , lowerCAmelCase__ :Tuple=1280 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :List[str]=0.02 , lowerCAmelCase__ :Dict=True , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Tuple=10 , lowerCAmelCase__ :int=None , ) ->List[str]:
lowercase = parent
lowercase = batch_size
lowercase = num_channels
lowercase = image_size
lowercase = depth_multiplier
lowercase = depth_divisible_by
lowercase = min_depth
lowercase = expand_ratio
lowercase = tf_padding
lowercase = output_stride
lowercase = first_layer_is_expansion
lowercase = finegrained_output
lowercase = hidden_act
lowercase = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
lowercase = classifier_dropout_prob
lowercase = use_labels
lowercase = is_training
lowercase = num_labels
lowercase = initializer_range
lowercase = scope
def SCREAMING_SNAKE_CASE( self :str ) ->Dict:
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.num_labels )
lowercase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase = self.get_config()
return config, pixel_values, labels, pixel_labels
def SCREAMING_SNAKE_CASE( self :Optional[int] ) ->List[Any]:
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple ) ->Any:
lowercase = MobileNetVaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowercase = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def SCREAMING_SNAKE_CASE( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[Any] ) ->Union[str, Any]:
lowercase = self.num_labels
lowercase = MobileNetVaForImageClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowercase = model(lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[int] ) ->Union[str, Any]:
lowercase = self.num_labels
lowercase = MobileNetVaForSemanticSegmentation(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowercase = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowercase = model(lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def SCREAMING_SNAKE_CASE( self :Any ) ->int:
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase = config_and_inputs
lowercase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( __a , __a , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase : Optional[int] = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase : List[Any] = (
{
'''feature-extraction''': MobileNetVaModel,
'''image-classification''': MobileNetVaForImageClassification,
'''image-segmentation''': MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase : Union[str, Any] = False
UpperCamelCase : int = False
UpperCamelCase : str = False
UpperCamelCase : Dict = False
def SCREAMING_SNAKE_CASE( self :Optional[int] ) ->Dict:
lowercase = MobileNetVaModelTester(self )
lowercase = MobileNetVaConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE( self :Dict ) ->Any:
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV2 does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE( self :Tuple ) ->Optional[int]:
pass
@unittest.skip(reason="MobileNetV2 does not support input and output embeddings" )
def SCREAMING_SNAKE_CASE( self :Any ) ->str:
pass
@unittest.skip(reason="MobileNetV2 does not output attentions" )
def SCREAMING_SNAKE_CASE( self :List[Any] ) ->str:
pass
def SCREAMING_SNAKE_CASE( self :Union[str, Any] ) ->Dict:
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(lowerCAmelCase__ )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE( self :int ) ->List[str]:
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE( self :int ) ->Optional[int]:
def check_hidden_states_output(lowerCAmelCase__ :str , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :int ):
lowercase = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
lowercase = outputs.hidden_states
lowercase = 16
self.assertEqual(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE( self :Any ) ->Tuple:
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE( self :int ) ->str:
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__ )
@slow
def SCREAMING_SNAKE_CASE( self :Union[str, Any] ) ->str:
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = MobileNetVaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __snake_case ( ):
'''simple docstring'''
lowercase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE( self :str ) ->Any:
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224" ) if is_vision_available() else None
)
@slow
def SCREAMING_SNAKE_CASE( self :Tuple ) ->Union[str, Any]:
lowercase = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224" ).to(lowerCAmelCase__ )
lowercase = self.default_image_processor
lowercase = prepare_img()
lowercase = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase = model(**lowerCAmelCase__ )
# verify the logits
lowercase = torch.Size((1, 1001) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase__ )
lowercase = torch.tensor([0.24_45, -1.19_93, 0.19_05] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE( self :Tuple ) ->List[Any]:
lowercase = MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
lowercase = model.to(lowerCAmelCase__ )
lowercase = MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
lowercase = prepare_img()
lowercase = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase = model(**lowerCAmelCase__ )
lowercase = outputs.logits
# verify the logits
lowercase = torch.Size((1, 21, 65, 65) )
self.assertEqual(logits.shape , lowerCAmelCase__ )
lowercase = torch.tensor(
[
[[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
[[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
[[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
] , device=lowerCAmelCase__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 441
| 1
|
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
a = 'src/transformers'
# Matches is_xxx_available()
a = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
a = re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
a = re.compile(r'\s+\"\S*\":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
a = re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
a = re.compile(r'^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
a = re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
a = re.compile(r'^\s+\"([^\"]+)\",')
# Catches a line with objects between brackets only: ["foo", "bar"],
a = re.compile(r'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
a = re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
a = re.compile(r'^\s*try:')
# Catches a line with else:
a = re.compile(r'^\s*else:')
def a_ ( __UpperCAmelCase ) -> Any:
"""simple docstring"""
if _re_test_backend.search(snake_case_ ) is None:
return None
snake_case: Union[str, Any] =[b[0] for b in _re_backend.findall(snake_case_ )]
backends.sort()
return "_and_".join(snake_case_ )
def a_ ( __UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
with open(snake_case_ , 'r' , encoding='utf-8' , newline='\n' ) as f:
snake_case: str =f.readlines()
snake_case: Any =0
while line_index < len(snake_case_ ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(snake_case_ ):
return None
# First grab the objects without a specific backend in _import_structure
snake_case: Optional[int] =[]
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
snake_case: int =lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(snake_case_ ):
snake_case: Optional[int] =_re_one_line_import_struct.search(snake_case_ ).groups()[0]
snake_case: Optional[Any] =re.findall(R'\[([^\]]+)\]' , snake_case_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
snake_case: Union[str, Any] =_re_import_struct_key_value.search(snake_case_ )
if single_line_import_search is not None:
snake_case: int =[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(snake_case_ ) > 0]
objects.extend(snake_case_ )
elif line.startswith(' ' * 8 + '\"' ):
objects.append(line[9:-3] )
line_index += 1
snake_case: Union[str, Any] ={"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
snake_case: List[Any] =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
snake_case: List[Any] =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
snake_case: Optional[int] =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
snake_case: Dict =lines[line_index]
if _re_import_struct_add_one.search(snake_case_ ) is not None:
objects.append(_re_import_struct_add_one.search(snake_case_ ).groups()[0] )
elif _re_import_struct_add_many.search(snake_case_ ) is not None:
snake_case: int =_re_import_struct_add_many.search(snake_case_ ).groups()[0].split(', ' )
snake_case: Any =[obj[1:-1] for obj in imports if len(snake_case_ ) > 0]
objects.extend(snake_case_ )
elif _re_between_brackets.search(snake_case_ ) is not None:
snake_case: List[str] =_re_between_brackets.search(snake_case_ ).groups()[0].split(', ' )
snake_case: Union[str, Any] =[obj[1:-1] for obj in imports if len(snake_case_ ) > 0]
objects.extend(snake_case_ )
elif _re_quote_object.search(snake_case_ ) is not None:
objects.append(_re_quote_object.search(snake_case_ ).groups()[0] )
elif line.startswith(' ' * 8 + '\"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '\"' ):
objects.append(line[13:-3] )
line_index += 1
snake_case: List[Any] =objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
snake_case: Dict =[]
while (
line_index < len(snake_case_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
snake_case: Dict =lines[line_index]
snake_case: Tuple =_re_import.search(snake_case_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
snake_case: Union[str, Any] ={"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(snake_case_ ):
# If the line is an if is_backend_available, we grab all objects associated.
snake_case: Dict =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
snake_case: Dict =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
snake_case: str =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
snake_case: Dict =lines[line_index]
snake_case: Any =_re_import.search(snake_case_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
snake_case: Optional[int] =objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def a_ ( __UpperCAmelCase , __UpperCAmelCase ) -> Any:
"""simple docstring"""
def find_duplicates(__UpperCAmelCase ):
return [k for k, v in collections.Counter(snake_case_ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
snake_case: Any =[]
for key in import_dict_objects.keys():
snake_case: Union[str, Any] =find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
snake_case: int =find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
snake_case: str ="""base imports""" if key == """none""" else f'''{key} backend'''
errors.append(f'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def a_ ( ) -> Optional[int]:
"""simple docstring"""
snake_case: str =[]
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
snake_case: List[str] =os.path.join(snake_case_ , '__init__.py' )
snake_case: Optional[int] =parse_init(snake_case_ )
if objects is not None:
snake_case: List[Any] =analyze_results(*snake_case_ )
if len(snake_case_ ) > 0:
snake_case: str =f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(snake_case_ ) )
if len(snake_case_ ) > 0:
raise ValueError('\n\n'.join(snake_case_ ) )
def a_ ( ) -> Any:
"""simple docstring"""
snake_case: Any =[]
for path, directories, files in os.walk(snake_case_ ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(snake_case_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(snake_case_ ) / folder).glob('*.py' ) ) ) == 0:
continue
snake_case: Union[str, Any] =str((Path(snake_case_ ) / folder).relative_to(snake_case_ ) )
snake_case: List[Any] =short_path.replace(os.path.sep , '.' )
submodules.append(snake_case_ )
for fname in files:
if fname == "__init__.py":
continue
snake_case: Optional[int] =str((Path(snake_case_ ) / fname).relative_to(snake_case_ ) )
snake_case: str =short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(snake_case_ )
return submodules
a = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
'models.esm.openfold_utils',
]
def a_ ( ) -> str:
"""simple docstring"""
from transformers.utils import direct_transformers_import
snake_case: List[Any] =direct_transformers_import(snake_case_ )
snake_case: Tuple =set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(snake_case_ , '__init__.py' ) , 'r' ) as f:
snake_case: Optional[int] =f.read()
import_structure_keys.update(set(re.findall(R'import_structure\[\"([^\"]*)\"\]' , snake_case_ ) ) )
snake_case: Dict =[
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(snake_case_ ) > 0:
snake_case: Dict ="""\n""".join(f'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
f'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 710
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class a_ ( snake_case ):
UpperCAmelCase : Optional[torch.FloatTensor] = None
UpperCAmelCase : torch.FloatTensor = None
UpperCAmelCase : Optional[Tuple[torch.FloatTensor]] = None
UpperCAmelCase : Optional[Tuple[torch.FloatTensor]] = None
class a_ ( snake_case ):
def __init__( self : int , a_ : str=1 , a_ : Any=0 , a_ : List[str]=2 , a_ : List[Any]=5_1_2 , a_ : Union[str, Any]="cls" , a_ : Dict=False , a_ : Optional[int]=True , **a_ : int , ) -> Union[str, Any]:
super().__init__(pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ )
snake_case: Any =project_dim
snake_case: Optional[Any] =pooler_fn
snake_case: List[str] =learn_encoder
snake_case: Optional[Any] =use_attention_mask
class a_ ( snake_case ):
UpperCAmelCase : int = [r"""pooler""", r"""logit_scale"""]
UpperCAmelCase : List[Any] = [r"""position_ids""", r"""predictions.decoder.bias"""]
UpperCAmelCase : Union[str, Any] = """roberta"""
UpperCAmelCase : Tuple = RobertaSeriesConfig
def __init__( self : int , a_ : int ) -> Any:
super().__init__(a_ )
snake_case: List[str] =XLMRobertaModel(a_ )
snake_case: Optional[int] =nn.Linear(config.hidden_size , config.project_dim )
snake_case: Optional[Any] =getattr(a_ , 'has_pre_transformation' , a_ )
if self.has_pre_transformation:
snake_case: str =nn.Linear(config.hidden_size , config.project_dim )
snake_case: Optional[int] =nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def UpperCamelCase ( self : Dict , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , a_ : Optional[bool] = None , a_ : Optional[bool] = None , a_ : Optional[bool] = None , ) -> Tuple:
snake_case: Dict =return_dict if return_dict is not None else self.config.use_return_dict
snake_case: List[str] =self.base_model(
input_ids=a_ , attention_mask=a_ , token_type_ids=a_ , position_ids=a_ , head_mask=a_ , inputs_embeds=a_ , encoder_hidden_states=a_ , encoder_attention_mask=a_ , output_attentions=a_ , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=a_ , )
if self.has_pre_transformation:
snake_case: Union[str, Any] =outputs['hidden_states'][-2]
snake_case: List[str] =self.pre_LN(a_ )
snake_case: Optional[int] =self.transformation_pre(a_ )
return TransformationModelOutput(
projection_state=a_ , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
snake_case: str =self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=a_ , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 347
| 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,
)
| 85
|
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def UpperCamelCase__ ( ) -> List[str]:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_lowercase = """__test_patch_submodule_mock__"""
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE_ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def UpperCamelCase__ ( ) -> int:
assert _test_patching.open is open
_lowercase = """__test_patch_submodule_builtin_mock__"""
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE_ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def UpperCamelCase__ ( ) -> List[Any]:
# pandas.read_csv is not present in _test_patching
_lowercase = """__test_patch_submodule_missing_mock__"""
with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE_ ):
pass
def UpperCamelCase__ ( ) -> Tuple:
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
_lowercase = """__test_patch_submodule_missing_builtin_mock__"""
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE_ ) is None
with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE_ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def UpperCamelCase__ ( ) -> List[str]:
_lowercase = """__test_patch_submodule_start_and_stop_mock__"""
_lowercase = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE_ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def UpperCamelCase__ ( ) -> Union[str, Any]:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_lowercase = """__test_patch_submodule_successive_join__"""
_lowercase = """__test_patch_submodule_successive_dirname__"""
_lowercase = """__test_patch_submodule_successive_rename__"""
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE_ ):
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE_ ):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE_ ):
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE_ ):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def UpperCamelCase__ ( ) -> List[Any]:
_lowercase = """__test_patch_submodule_doesnt_exist_mock__"""
with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE_ ):
pass
with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE_ ):
pass
| 287
| 0
|
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
A = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'ctc_proj',
'mask_emb': 'masked_spec_embed',
}
A = [
'ctc_proj',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
for attribute in key.split('.' ):
snake_case_ = getattr(lowercase__ , lowercase__ )
if weight_type is not None:
snake_case_ = getattr(lowercase__ , lowercase__ ).shape
else:
snake_case_ = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case_ = value
elif weight_type == "weight_g":
snake_case_ = value
elif weight_type == "weight_v":
snake_case_ = value
elif weight_type == "bias":
snake_case_ = value
else:
snake_case_ = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = fairseq_model.state_dict()
snake_case_ = hf_model.feature_extractor
for name, value in fairseq_dict.items():
snake_case_ = False
if "conv_layers" in name:
load_conv_layer(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , hf_model.config.feat_extract_norm == 'group' , )
snake_case_ = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case_ = True
if "*" in mapped_key:
snake_case_ = name.split(lowercase__ )[0].split('.' )[-2]
snake_case_ = mapped_key.replace('*' , lowercase__ )
if "weight_g" in name:
snake_case_ = 'weight_g'
elif "weight_v" in name:
snake_case_ = 'weight_v'
elif "bias" in name and "relative_attention_bias" not in name:
snake_case_ = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case_ = 'weight'
else:
snake_case_ = None
set_recursively(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
continue
if not is_used:
unused_weights.append(lowercase__ )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = full_name.split('conv_layers.' )[-1]
snake_case_ = name.split('.' )
snake_case_ = int(items[0] )
snake_case_ = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowercase__ )
@torch.no_grad()
def a(lowercase__ , lowercase__ , lowercase__=None ):
'''simple docstring'''
# load the pre-trained checkpoints
snake_case_ = torch.load(lowercase__ )
snake_case_ = WavLMConfigOrig(checkpoint['cfg'] )
snake_case_ = WavLMOrig(lowercase__ )
model.load_state_dict(checkpoint['model'] )
model.eval()
if config_path is not None:
snake_case_ = WavLMConfig.from_pretrained(lowercase__ )
else:
snake_case_ = WavLMConfig()
snake_case_ = WavLMModel(lowercase__ )
recursively_load_weights(lowercase__ , lowercase__ )
hf_wavlm.save_pretrained(lowercase__ )
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
A = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 46
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = LEDConfig
__A = {}
__A = """gelu"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=37 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=20 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=4 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
snake_case_ = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
snake_case_ = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
snake_case_ = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
snake_case_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
snake_case_ = tf.concat([input_ids, eos_tensor] , axis=1 )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
snake_case_ = prepare_led_inputs_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ = tf.concat(
[tf.zeros_like(__UpperCamelCase )[:, :-1], tf.ones_like(__UpperCamelCase )[:, -1:]] , axis=-1 , )
snake_case_ = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = TFLEDModel(config=__UpperCamelCase ).get_decoder()
snake_case_ = inputs_dict['input_ids']
snake_case_ = input_ids[:1, :]
snake_case_ = inputs_dict['attention_mask'][:1, :]
snake_case_ = 1
# first forward pass
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , use_cache=__UpperCamelCase )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
snake_case_ = tf.concat([input_ids, next_tokens] , axis=-1 )
snake_case_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase )[0]
snake_case_ = model(__UpperCamelCase , attention_mask=__UpperCamelCase , past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
snake_case_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
snake_case_ = output_from_no_past[:, -3:, random_slice_idx]
snake_case_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase , __UpperCamelCase , rtol=1E-3 )
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
__A = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
__A = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A = True
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = tf.zeros_like(inputs_dict['attention_mask'] )
snake_case_ = 2
snake_case_ = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['global_attention_mask'] , )
snake_case_ = True
snake_case_ = self.model_tester.seq_length
snake_case_ = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__UpperCamelCase ):
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__UpperCamelCase ):
snake_case_ = [t.numpy() for t in outputs.encoder_attentions]
snake_case_ = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = len(__UpperCamelCase )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_decoder_attentions_output(__UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = model(self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , __UpperCamelCase )
check_encoder_attentions_output(__UpperCamelCase )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def a(lowercase__ ):
'''simple docstring'''
return tf.constant(lowercase__ , dtype=tf.intaa )
A = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, 7_68)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
snake_case_ = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
snake_case_ = prepare_led_inputs_dict(model.config , __UpperCamelCase , __UpperCamelCase )
snake_case_ = model(**__UpperCamelCase )[0]
snake_case_ = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape , __UpperCamelCase )
# change to expected output here
snake_case_ = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCamelCase , atol=1E-3 , rtol=1E-3 )
| 46
| 1
|
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
UpperCamelCase = logging.get_logger(__name__)
def A ( lowercase__ : Optional[Any] , lowercase__ : List[Any] , lowercase__ : Optional[int] ) -> Optional[int]:
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def A ( lowercase__ : np.ndarray , lowercase__ : Optional[str] , lowercase__ : Optional[str] ) -> int:
UpperCamelCase__ :Dict = to_pil_image(lowercase__ )
UpperCamelCase__ , UpperCamelCase__ :Tuple = pil_image.size
UpperCamelCase__ :Union[str, Any] = pytesseract.image_to_data(lowercase__ , lang=lowercase__ , output_type="""dict""" , config=lowercase__ )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :Dict = data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]
# filter empty words and corresponding coordinates
UpperCamelCase__ :Optional[int] = [idx for idx, word in enumerate(lowercase__ ) if not word.strip()]
UpperCamelCase__ :List[Any] = [word for idx, word in enumerate(lowercase__ ) if idx not in irrelevant_indices]
UpperCamelCase__ :List[str] = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
UpperCamelCase__ :str = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
UpperCamelCase__ :List[str] = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
UpperCamelCase__ :Tuple = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
UpperCamelCase__ :Optional[int] = []
for x, y, w, h in zip(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
UpperCamelCase__ :Tuple = [x, y, x + w, y + h]
actual_boxes.append(lowercase__ )
# finally, normalize the bounding boxes
UpperCamelCase__ :int = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(lowercase__ , lowercase__ , lowercase__ ) )
assert len(lowercase__ ) == len(lowercase__ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
_snake_case : List[str] = ["""pixel_values"""]
def __init__( self :Optional[Any] , lowerCamelCase__ :bool = True , lowerCamelCase__ :Dict[str, int] = None , lowerCamelCase__ :PILImageResampling = PILImageResampling.BILINEAR , lowerCamelCase__ :bool = True , lowerCamelCase__ :float = 1 / 2_55 , lowerCamelCase__ :bool = True , lowerCamelCase__ :Union[float, Iterable[float]] = None , lowerCamelCase__ :Union[float, Iterable[float]] = None , lowerCamelCase__ :bool = True , lowerCamelCase__ :Optional[str] = None , lowerCamelCase__ :Optional[str] = "" , **lowerCamelCase__ :int , ):
super().__init__(**lowerCamelCase__ )
UpperCamelCase__ :str = size if size is not None else {"""height""": 2_24, """width""": 2_24}
UpperCamelCase__ :Union[str, Any] = get_size_dict(lowerCamelCase__ )
UpperCamelCase__ :Optional[Any] = do_resize
UpperCamelCase__ :Optional[Any] = size
UpperCamelCase__ :Any = resample
UpperCamelCase__ :int = do_rescale
UpperCamelCase__ :Optional[int] = rescale_value
UpperCamelCase__ :Union[str, Any] = do_normalize
UpperCamelCase__ :int = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase__ :Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD
UpperCamelCase__ :Optional[int] = apply_ocr
UpperCamelCase__ :Tuple = ocr_lang
UpperCamelCase__ :List[Any] = tesseract_config
def __a ( self :Optional[int] , lowerCamelCase__ :np.ndarray , lowerCamelCase__ :Dict[str, int] , lowerCamelCase__ :PILImageResampling = PILImageResampling.BILINEAR , lowerCamelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase__ :Tuple , ):
UpperCamelCase__ :int = get_size_dict(lowerCamelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
UpperCamelCase__ :Any = (size["""height"""], size["""width"""])
return resize(lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def __a ( self :int , lowerCamelCase__ :np.ndarray , lowerCamelCase__ :Union[int, float] , lowerCamelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase__ :int , ):
return rescale(lowerCamelCase__ , scale=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def __a ( self :Union[str, Any] , lowerCamelCase__ :np.ndarray , lowerCamelCase__ :Union[float, Iterable[float]] , lowerCamelCase__ :Union[float, Iterable[float]] , lowerCamelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase__ :Optional[int] , ):
return normalize(lowerCamelCase__ , mean=lowerCamelCase__ , std=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def __a ( self :Optional[Any] , lowerCamelCase__ :ImageInput , lowerCamelCase__ :bool = None , lowerCamelCase__ :Dict[str, int] = None , lowerCamelCase__ :Tuple=None , lowerCamelCase__ :bool = None , lowerCamelCase__ :float = None , lowerCamelCase__ :bool = None , lowerCamelCase__ :Union[float, Iterable[float]] = None , lowerCamelCase__ :Union[float, Iterable[float]] = None , lowerCamelCase__ :bool = None , lowerCamelCase__ :Optional[str] = None , lowerCamelCase__ :Optional[str] = None , lowerCamelCase__ :Optional[Union[str, TensorType]] = None , lowerCamelCase__ :ChannelDimension = ChannelDimension.FIRST , **lowerCamelCase__ :List[Any] , ):
UpperCamelCase__ :str = do_resize if do_resize is not None else self.do_resize
UpperCamelCase__ :str = size if size is not None else self.size
UpperCamelCase__ :Any = get_size_dict(lowerCamelCase__ )
UpperCamelCase__ :List[Any] = resample if resample is not None else self.resample
UpperCamelCase__ :Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase__ :List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase__ :Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase__ :str = image_mean if image_mean is not None else self.image_mean
UpperCamelCase__ :str = image_std if image_std is not None else self.image_std
UpperCamelCase__ :List[Any] = apply_ocr if apply_ocr is not None else self.apply_ocr
UpperCamelCase__ :Union[str, Any] = ocr_lang if ocr_lang is not None else self.ocr_lang
UpperCamelCase__ :Any = tesseract_config if tesseract_config is not None else self.tesseract_config
UpperCamelCase__ :Dict = make_list_of_images(lowerCamelCase__ )
if not valid_images(lowerCamelCase__ ):
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_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("""If do_normalize is True, image_mean and image_std must be specified.""" )
# All transformations expect numpy arrays.
UpperCamelCase__ :List[str] = [to_numpy_array(lowerCamelCase__ ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , """pytesseract""" )
UpperCamelCase__ :Tuple = []
UpperCamelCase__ :List[Any] = []
for image in images:
UpperCamelCase__ , UpperCamelCase__ :List[Any] = apply_tesseract(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
words_batch.append(lowerCamelCase__ )
boxes_batch.append(lowerCamelCase__ )
if do_resize:
UpperCamelCase__ :List[Any] = [self.resize(image=lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ ) for image in images]
if do_rescale:
UpperCamelCase__ :Any = [self.rescale(image=lowerCamelCase__ , scale=lowerCamelCase__ ) for image in images]
if do_normalize:
UpperCamelCase__ :str = [self.normalize(image=lowerCamelCase__ , mean=lowerCamelCase__ , std=lowerCamelCase__ ) for image in images]
UpperCamelCase__ :int = [to_channel_dimension_format(lowerCamelCase__ , lowerCamelCase__ ) for image in images]
UpperCamelCase__ :int = BatchFeature(data={"""pixel_values""": images} , tensor_type=lowerCamelCase__ )
if apply_ocr:
UpperCamelCase__ :Optional[Any] = words_batch
UpperCamelCase__ :Dict = boxes_batch
return data
| 45
|
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self :Union[str, Any] , *lowerCamelCase__ :Optional[int] , **lowerCamelCase__ :Dict ):
warnings.warn(
"""The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use MobileViTImageProcessor instead.""" , lowerCamelCase__ , )
super().__init__(*lowerCamelCase__ , **lowerCamelCase__ )
| 45
| 1
|
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> list:
_UpperCAmelCase = word.split()
def justify(snake_case , snake_case , snake_case ) -> str:
_UpperCAmelCase = max_width - width
_UpperCAmelCase = len(_lowercase )
if len(_lowercase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
_UpperCAmelCase = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
_UpperCAmelCase = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
_UpperCAmelCase = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowercase ):
num_spaces_between_words_list[i] += 1
_UpperCAmelCase = []
for i in range(_lowercase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * """ """ )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowercase )
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = 0
for word in words:
if width + len(_lowercase ) + len(_lowercase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowercase )
width += len(_lowercase )
else:
# justify the line and add it to result
answer.append(justify(_lowercase , _lowercase , _lowercase ) )
# reset new line and new width
_UpperCAmelCase = [word], len(_lowercase )
_UpperCAmelCase = max_width - width - len(_lowercase )
answer.append(""" """.join(_lowercase ) + (remaining_spaces + 1) * """ """ )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 709
|
import csv
import tweepy
# Twitter API credentials
a = ""
a = ""
a = ""
a = ""
def _SCREAMING_SNAKE_CASE ( snake_case ) -> None:
# authorize twitter, initialize tweepy
_UpperCAmelCase = tweepy.OAuthHandler(snake_case , snake_case )
auth.set_access_token(snake_case , snake_case )
_UpperCAmelCase = tweepy.API(snake_case )
# initialize a list to hold all the tweepy Tweets
_UpperCAmelCase = []
# make initial request for most recent tweets (200 is the maximum allowed count)
_UpperCAmelCase = api.user_timeline(screen_name=snake_case , count=2_0_0 )
# save most recent tweets
alltweets.extend(snake_case )
# save the id of the oldest tweet less one
_UpperCAmelCase = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(snake_case ) > 0:
print(f"getting tweets before {oldest}" )
# all subsequent requests use the max_id param to prevent duplicates
_UpperCAmelCase = api.user_timeline(
screen_name=snake_case , count=2_0_0 , max_id=snake_case )
# save most recent tweets
alltweets.extend(snake_case )
# update the id of the oldest tweet less one
_UpperCAmelCase = alltweets[-1].id - 1
print(f"...{len(snake_case )} tweets downloaded so far" )
# transform the tweepy tweets into a 2D array that will populate the csv
_UpperCAmelCase = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f"new_{screen_name}_tweets.csv" , """w""" ) as f:
_UpperCAmelCase = csv.writer(snake_case )
writer.writerow(["""id""", """created_at""", """text"""] )
writer.writerows(snake_case )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets("FirePing32")
| 175
| 0
|
def snake_case (UpperCAmelCase__ ) -> str:
UpperCamelCase_: Dict = 1
UpperCamelCase_: List[Any] = 2
while i * i <= n:
UpperCamelCase_: Dict = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def snake_case () -> List[Any]:
UpperCamelCase_: Any = 1
UpperCamelCase_: str = 1
while True:
i += 1
t_num += i
if count_divisors(UpperCAmelCase__ ) > 5_0_0:
break
return t_num
if __name__ == "__main__":
print(solution())
| 57
|
from collections import namedtuple
A_ : Tuple = namedtuple('from_to', 'from_ to')
A_ : int = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.00454, 264.172),
'cubicyard': from_to(0.76455, 1.30795),
'cubicfoot': from_to(0.028, 35.3147),
'cup': from_to(0.000236588, 4226.75),
}
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> float:
if from_type not in METRIC_CONVERSION:
raise ValueError(
F'''Invalid \'from_type\' value: {from_type!r} Supported values are:\n'''
+ ', '.join(UpperCAmelCase__ ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
F'''Invalid \'to_type\' value: {to_type!r}. Supported values are:\n'''
+ ', '.join(UpperCAmelCase__ ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
| 1
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _snake_case ( __snake_case , __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
a = AltDiffusionPipeline
a = TEXT_TO_IMAGE_PARAMS
a = TEXT_TO_IMAGE_BATCH_PARAMS
a = TEXT_TO_IMAGE_IMAGE_PARAMS
a = TEXT_TO_IMAGE_IMAGE_PARAMS
def _lowerCAmelCase ( self : int):
"""simple docstring"""
torch.manual_seed(0)
_SCREAMING_SNAKE_CASE : int = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=3_2 , )
_SCREAMING_SNAKE_CASE : Optional[Any] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_A , set_alpha_to_one=_A , )
torch.manual_seed(0)
_SCREAMING_SNAKE_CASE : Union[str, Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0)
_SCREAMING_SNAKE_CASE : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_0_0_2 , )
_SCREAMING_SNAKE_CASE : Dict = CLIPTextModel(_A)
_SCREAMING_SNAKE_CASE : Any = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""")
_SCREAMING_SNAKE_CASE : List[str] = 7_7
_SCREAMING_SNAKE_CASE : List[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _lowerCAmelCase ( self : Tuple , _A : List[str] , _A : str=0):
"""simple docstring"""
if str(_A).startswith("""mps"""):
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(_A)
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Generator(device=_A).manual_seed(_A)
_SCREAMING_SNAKE_CASE : List[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _lowerCAmelCase ( self : List[str]):
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def _lowerCAmelCase ( self : Optional[int]):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Tuple = """cpu""" # ensure determinism for the device-dependent torch.Generator
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_components()
torch.manual_seed(0)
_SCREAMING_SNAKE_CASE : Union[str, Any] = RobertaSeriesConfig(
hidden_size=3_2 , project_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_0_0_2 , )
# TODO: remove after fixing the non-deterministic text encoder
_SCREAMING_SNAKE_CASE : Tuple = RobertaSeriesModelWithTransformation(_A)
_SCREAMING_SNAKE_CASE : str = text_encoder
_SCREAMING_SNAKE_CASE : str = AltDiffusionPipeline(**_A)
_SCREAMING_SNAKE_CASE : str = alt_pipe.to(_A)
alt_pipe.set_progress_bar_config(disable=_A)
_SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_inputs(_A)
_SCREAMING_SNAKE_CASE : Union[str, Any] = """A photo of an astronaut"""
_SCREAMING_SNAKE_CASE : List[str] = alt_pipe(**_A)
_SCREAMING_SNAKE_CASE : str = output.images
_SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_SCREAMING_SNAKE_CASE : Tuple = np.array(
[0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
_SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_components()
_SCREAMING_SNAKE_CASE : Union[str, Any] = PNDMScheduler(skip_prk_steps=_A)
torch.manual_seed(0)
_SCREAMING_SNAKE_CASE : Union[str, Any] = RobertaSeriesConfig(
hidden_size=3_2 , project_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_0_0_2 , )
# TODO: remove after fixing the non-deterministic text encoder
_SCREAMING_SNAKE_CASE : Union[str, Any] = RobertaSeriesModelWithTransformation(_A)
_SCREAMING_SNAKE_CASE : Tuple = text_encoder
_SCREAMING_SNAKE_CASE : Dict = AltDiffusionPipeline(**_A)
_SCREAMING_SNAKE_CASE : str = alt_pipe.to(_A)
alt_pipe.set_progress_bar_config(disable=_A)
_SCREAMING_SNAKE_CASE : Dict = self.get_dummy_inputs(_A)
_SCREAMING_SNAKE_CASE : List[str] = alt_pipe(**_A)
_SCREAMING_SNAKE_CASE : List[Any] = output.images
_SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_SCREAMING_SNAKE_CASE : List[Any] = np.array(
[0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self : str):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" , safety_checker=_A)
_SCREAMING_SNAKE_CASE : List[str] = alt_pipe.to(_A)
alt_pipe.set_progress_bar_config(disable=_A)
_SCREAMING_SNAKE_CASE : Union[str, Any] = """A painting of a squirrel eating a burger"""
_SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0)
_SCREAMING_SNAKE_CASE : Any = alt_pipe([prompt] , generator=_A , guidance_scale=6.0 , num_inference_steps=2_0 , output_type="""np""")
_SCREAMING_SNAKE_CASE : Dict = output.images
_SCREAMING_SNAKE_CASE : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_SCREAMING_SNAKE_CASE : Optional[int] = np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def _lowerCAmelCase ( self : Optional[Any]):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Dict = DDIMScheduler.from_pretrained("""BAAI/AltDiffusion""" , subfolder="""scheduler""")
_SCREAMING_SNAKE_CASE : Union[str, Any] = AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" , scheduler=_A , safety_checker=_A)
_SCREAMING_SNAKE_CASE : Optional[Any] = alt_pipe.to(_A)
alt_pipe.set_progress_bar_config(disable=_A)
_SCREAMING_SNAKE_CASE : Dict = """A painting of a squirrel eating a burger"""
_SCREAMING_SNAKE_CASE : List[Any] = torch.manual_seed(0)
_SCREAMING_SNAKE_CASE : str = alt_pipe([prompt] , generator=_A , num_inference_steps=2 , output_type="""numpy""")
_SCREAMING_SNAKE_CASE : List[Any] = output.images
_SCREAMING_SNAKE_CASE : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_SCREAMING_SNAKE_CASE : int = np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 635
|
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 6_50, "eval_accuracy": 0.6, "eval_loss": 0.9},
},
{
"framework": "tensorflow",
"script": "run_tf.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
},
] )
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int]):
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=_A , )
assert hasattr(self , """env""")
def _lowerCAmelCase ( self : Union[str, Any] , _A : str=1):
"""simple docstring"""
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-single""" , instance_count=_A , instance_type=self.instance_type , debugger_hook_config=_A , hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version="""py36""" , )
def _lowerCAmelCase ( self : Union[str, Any] , _A : Union[str, Any]):
"""simple docstring"""
TrainingJobAnalytics(_A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = self.create_estimator()
# run training
estimator.fit()
# result dataframe
_SCREAMING_SNAKE_CASE : Any = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()
# extract kpis
_SCREAMING_SNAKE_CASE : Any = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
_SCREAMING_SNAKE_CASE : Tuple = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
# get train time from SageMaker job, this includes starting, preprocessing, stopping
_SCREAMING_SNAKE_CASE : int = (
Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds""" , 9_9_9_9_9_9)
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy)
assert all(t <= self.results["""eval_loss"""] for t in eval_loss)
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""") as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , _A)
| 635
| 1
|
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCamelCase : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__lowerCamelCase : Any = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class UpperCAmelCase ( unittest.TestCase ):
def UpperCAmelCase__ (self : Optional[int] ) -> Union[str, Any]:
lowercase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) )
lowercase = self.diffusers_dir
shutil.copy(
os.path.join(A__ , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , )
def UpperCAmelCase__ (self : Dict ) -> Union[str, Any]:
lowercase = "src/diffusers"
shutil.rmtree(self.diffusers_dir )
def UpperCAmelCase__ (self : Optional[int] , A__ : str , A__ : Any , A__ : Optional[Any] , A__ : Union[str, Any]=None ) -> str:
lowercase = comment + f'\nclass {class_name}(nn.Module):\n' + class_code
if overwrite_result is not None:
lowercase = comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
lowercase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 )
lowercase = black.format_str(A__ , mode=A__ )
lowercase = os.path.join(self.diffusers_dir , "new_code.py" )
with open(A__ , "w" , newline="\n" ) as f:
f.write(A__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(A__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=A__ )
with open(A__ , "r" ) as f:
self.assertTrue(f.read() , A__ )
def UpperCAmelCase__ (self : Tuple ) -> Union[str, Any]:
lowercase = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
self.assertEqual(A__ , A__ )
def UpperCAmelCase__ (self : Union[str, Any] ) -> Tuple:
# Base copy consistency
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , )
# With no empty line at the end
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , A__ , )
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , A__ ) , )
# Copy consistency with a really long name
lowercase = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
f'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}' , f'{long_class_name}SchedulerOutput' , re.sub("Bert" , A__ , A__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , A__ , overwrite_result=re.sub("DDPM" , "Test" , A__ ) , )
| 310
|
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
__lowerCamelCase : List[str] = get_logger(__name__)
__lowerCamelCase : List[Any] = Path(__file__).parent / "model_card_template.md"
__lowerCamelCase : Any = uuida().hex
__lowerCamelCase : Optional[Any] = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
__lowerCamelCase : Union[str, Any] = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
__lowerCamelCase : Optional[Any] = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def UpperCAmelCase_ ( lowerCAmelCase_ = None ):
"""simple docstring"""
lowercase = f'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f'; torch/{_torch_version}'
if is_flax_available():
ua += f'; jax/{_jax_version}'
ua += f'; flax/{_flax_version}'
if is_onnx_available():
ua += f'; onnxruntime/{_onnxruntime_version}'
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI" , "" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items() )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
ua += "; " + user_agent
return ua
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None ):
"""simple docstring"""
if token is None:
lowercase = HfFolder.get_token()
if organization is None:
lowercase = whoami(lowerCAmelCase_ )["name"]
return f'{username}/{model_id}'
else:
return f'{organization}/{model_id}'
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `create_model_card`."
" To install it, please run `pip install Jinja2`." )
if hasattr(lowerCAmelCase_ , "local_rank" ) and args.local_rank not in [-1, 0]:
return
lowercase = args.hub_token if hasattr(lowerCAmelCase_ , "hub_token" ) else None
lowercase = get_full_repo_name(lowerCAmelCase_ , token=lowerCAmelCase_ )
lowercase = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="en" , license="apache-2.0" , library_name="diffusers" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=lowerCAmelCase_ , model_name=lowerCAmelCase_ , repo_name=lowerCAmelCase_ , dataset_name=args.dataset_name if hasattr(lowerCAmelCase_ , "dataset_name" ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(lowerCAmelCase_ , "gradient_accumulation_steps" ) else None
) , adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase_ , "adam_beta1" ) else None , adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase_ , "adam_beta2" ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase_ , "adam_weight_decay" ) else None , adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase_ , "adam_epsilon" ) else None , lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase_ , "lr_scheduler" ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase_ , "lr_warmup_steps" ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase_ , "ema_inv_gamma" ) else None , ema_power=args.ema_power if hasattr(lowerCAmelCase_ , "ema_power" ) else None , ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase_ , "ema_max_decay" ) else None , mixed_precision=args.mixed_precision , )
lowercase = os.path.join(args.output_dir , "README.md" )
model_card.save(lowerCAmelCase_ )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ = None ):
"""simple docstring"""
if resolved_file is None or commit_hash is not None:
return commit_hash
lowercase = str(Path(lowerCAmelCase_ ).as_posix() )
lowercase = re.search(R"snapshots/([^/]+)/" , lowerCAmelCase_ )
if search is None:
return None
lowercase = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase_ ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__lowerCamelCase : List[str] = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__lowerCamelCase : Union[str, Any] = os.path.join(hf_cache_home, "diffusers")
def UpperCAmelCase_ ( lowerCAmelCase_ = None , lowerCAmelCase_ = None ):
"""simple docstring"""
if new_cache_dir is None:
lowercase = DIFFUSERS_CACHE
if old_cache_dir is None:
lowercase = old_diffusers_cache
lowercase = Path(lowerCAmelCase_ ).expanduser()
lowercase = Path(lowerCAmelCase_ ).expanduser()
for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
lowercase = new_cache_dir / old_blob_path.relative_to(lowerCAmelCase_ )
new_blob_path.parent.mkdir(parents=lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
os.replace(lowerCAmelCase_ , lowerCAmelCase_ )
try:
os.symlink(lowerCAmelCase_ , lowerCAmelCase_ )
except OSError:
logger.warning(
"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
__lowerCamelCase : Union[str, Any] = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
__lowerCamelCase : Optional[int] = 0
else:
with open(cache_version_file) as f:
try:
__lowerCamelCase : Tuple = int(f.read())
except ValueError:
__lowerCamelCase : Tuple = 0
if cache_version < 1:
__lowerCamelCase : Tuple = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
__lowerCamelCase : str = "\n".join(traceback.format_tb(e.__traceback__))
logger.error(
f"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
"the directory exists and can be written to."
)
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ = None ):
"""simple docstring"""
if variant is not None:
lowercase = weights_name.split("." )
lowercase = splits[:-1] + [variant] + splits[-1:]
lowercase = ".".join(lowerCAmelCase_ )
return weights_name
def UpperCAmelCase_ ( lowerCAmelCase_ , *,
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None , ):
"""simple docstring"""
lowercase = str(lowerCAmelCase_ )
if os.path.isfile(lowerCAmelCase_ ):
return pretrained_model_name_or_path
elif os.path.isdir(lowerCAmelCase_ ):
if os.path.isfile(os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) ):
# Load from a PyTorch checkpoint
lowercase = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) ):
lowercase = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return model_file
else:
raise EnvironmentError(
f'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(lowerCAmelCase_ ).base_version ) >= version.parse("0.20.0" )
):
try:
lowercase = hf_hub_download(
lowerCAmelCase_ , filename=_add_variant(lowerCAmelCase_ , lowerCAmelCase_ ) , cache_dir=lowerCAmelCase_ , force_download=lowerCAmelCase_ , proxies=lowerCAmelCase_ , resume_download=lowerCAmelCase_ , local_files_only=lowerCAmelCase_ , use_auth_token=lowerCAmelCase_ , user_agent=lowerCAmelCase_ , subfolder=lowerCAmelCase_ , revision=revision or commit_hash , )
warnings.warn(
f'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.' , lowerCAmelCase_ , )
return model_file
except: # noqa: E722
warnings.warn(
f'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCAmelCase_ , lowerCAmelCase_ )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(lowerCAmelCase_ , lowerCAmelCase_ )}\' so that the correct variant file can be added.' , lowerCAmelCase_ , )
try:
# 2. Load model file as usual
lowercase = hf_hub_download(
lowerCAmelCase_ , filename=lowerCAmelCase_ , cache_dir=lowerCAmelCase_ , force_download=lowerCAmelCase_ , proxies=lowerCAmelCase_ , resume_download=lowerCAmelCase_ , local_files_only=lowerCAmelCase_ , use_auth_token=lowerCAmelCase_ , user_agent=lowerCAmelCase_ , subfolder=lowerCAmelCase_ , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`." )
except RevisionNotFoundError:
raise EnvironmentError(
f'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
"this model name. Check the model page at "
f'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.' )
except EntryNotFoundError:
raise EnvironmentError(
f'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.' )
except HTTPError as err:
raise EnvironmentError(
f'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}' )
except ValueError:
raise EnvironmentError(
f'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
f' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
f' directory containing a file named {weights_name} or'
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
except EnvironmentError:
raise EnvironmentError(
f'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
f'containing a file named {weights_name}' )
| 310
| 1
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Tuple = 'deta'
UpperCAmelCase__ : Union[str, Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self: Optional[Any] , UpperCamelCase_: Dict=None , UpperCamelCase_: List[str]=9_00 , UpperCamelCase_: Union[str, Any]=20_48 , UpperCamelCase_: int=6 , UpperCamelCase_: Union[str, Any]=20_48 , UpperCamelCase_: List[str]=8 , UpperCamelCase_: Optional[int]=6 , UpperCamelCase_: Optional[Any]=10_24 , UpperCamelCase_: int=8 , UpperCamelCase_: List[str]=0.0 , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: Dict="relu" , UpperCamelCase_: Optional[int]=2_56 , UpperCamelCase_: List[Any]=0.1 , UpperCamelCase_: Union[str, Any]=0.0 , UpperCamelCase_: Tuple=0.0 , UpperCamelCase_: Union[str, Any]=0.02 , UpperCamelCase_: List[str]=1.0 , UpperCamelCase_: Union[str, Any]=True , UpperCamelCase_: Tuple=False , UpperCamelCase_: Union[str, Any]="sine" , UpperCamelCase_: Optional[int]=5 , UpperCamelCase_: List[str]=4 , UpperCamelCase_: List[Any]=4 , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: str=3_00 , UpperCamelCase_: str=True , UpperCamelCase_: Tuple=True , UpperCamelCase_: List[Any]=1 , UpperCamelCase_: List[Any]=5 , UpperCamelCase_: Dict=2 , UpperCamelCase_: Any=1 , UpperCamelCase_: Dict=1 , UpperCamelCase_: List[Any]=5 , UpperCamelCase_: Tuple=2 , UpperCamelCase_: Optional[int]=0.1 , UpperCamelCase_: str=0.25 , **UpperCamelCase_: Tuple , ):
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
__lowerCamelCase = CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""] )
else:
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = backbone_config.pop("""model_type""" )
__lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
__lowerCamelCase = config_class.from_dict(UpperCamelCase_ )
__lowerCamelCase = backbone_config
__lowerCamelCase = num_queries
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = d_model
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = init_xavier_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = auxiliary_loss
__lowerCamelCase = position_embedding_type
# deformable attributes
__lowerCamelCase = num_feature_levels
__lowerCamelCase = encoder_n_points
__lowerCamelCase = decoder_n_points
__lowerCamelCase = two_stage
__lowerCamelCase = two_stage_num_proposals
__lowerCamelCase = with_box_refine
__lowerCamelCase = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
# Hungarian matcher
__lowerCamelCase = class_cost
__lowerCamelCase = bbox_cost
__lowerCamelCase = giou_cost
# Loss coefficients
__lowerCamelCase = mask_loss_coefficient
__lowerCamelCase = dice_loss_coefficient
__lowerCamelCase = bbox_loss_coefficient
__lowerCamelCase = giou_loss_coefficient
__lowerCamelCase = eos_coefficient
__lowerCamelCase = focal_alpha
super().__init__(is_encoder_decoder=UpperCamelCase_ , **UpperCamelCase_ )
@property
def lowerCAmelCase__ ( self: List[str] ):
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self: Union[str, Any] ):
return self.d_model
def lowerCAmelCase__ ( self: int ):
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.backbone_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 708
|
class lowerCamelCase__: # Public class to implement a graph
def __init__( self: Dict , UpperCamelCase_: int , UpperCamelCase_: int , UpperCamelCase_: list[list[bool]] ):
__lowerCamelCase = row
__lowerCamelCase = col
__lowerCamelCase = graph
def lowerCAmelCase__ ( self: Union[str, Any] , UpperCamelCase_: int , UpperCamelCase_: int , UpperCamelCase_: list[list[bool]] ):
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: int , UpperCamelCase_: int , UpperCamelCase_: list[list[bool]] ):
# Checking all 8 elements surrounding nth element
__lowerCamelCase = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
__lowerCamelCase = [-1, 0, 1, -1, 1, -1, 0, 1]
__lowerCamelCase = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , UpperCamelCase_ ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[Any] ): # And finally, count all islands.
__lowerCamelCase = [[False for j in range(self.COL )] for i in range(self.ROW )]
__lowerCamelCase = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
count += 1
return count
| 80
| 0
|
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase__ : Dict = logging.getLogger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,snake_case__=-1 ):
# in NER datasets, the last column is usually reserved for NER label
SCREAMING_SNAKE_CASE_ : List[Any] = label_idx
def snake_case ( self ,snake_case__ ,snake_case__ ):
if isinstance(snake_case__ ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = mode.value
SCREAMING_SNAKE_CASE_ : int = os.path.join(snake_case__ ,F'{mode}.txt' )
SCREAMING_SNAKE_CASE_ : Dict = 1
SCREAMING_SNAKE_CASE_ : Any = []
with open(snake_case__ ,encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE_ : Any = []
SCREAMING_SNAKE_CASE_ : Tuple = []
for line in f:
if line.startswith('-DOCSTART-' ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' ,words=snake_case__ ,labels=snake_case__ ) )
guid_index += 1
SCREAMING_SNAKE_CASE_ : int = []
SCREAMING_SNAKE_CASE_ : Union[str, Any] = []
else:
SCREAMING_SNAKE_CASE_ : Dict = line.split(' ' )
words.append(splits[0] )
if len(snake_case__ ) > 1:
labels.append(splits[self.label_idx].replace('\n' ,'' ) )
else:
# Examples could have no label for mode = "test"
labels.append('O' )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' ,words=snake_case__ ,labels=snake_case__ ) )
return examples
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
for line in test_input_reader:
if line.startswith('-DOCSTART-' ) or line == "" or line == "\n":
writer.write(snake_case__ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
SCREAMING_SNAKE_CASE_ : int = line.split()[0] + ' ' + preds_list[example_id].pop(0 ) + '\n'
writer.write(snake_case__ )
else:
logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.' ,line.split()[0] )
def snake_case ( self ,snake_case__ ):
if path:
with open(snake_case__ ,'r' ) as f:
SCREAMING_SNAKE_CASE_ : Dict = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE_ : int = ['O'] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ):
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2 )
def snake_case ( self ,snake_case__ ):
if path:
with open(snake_case__ ,'r' ) as f:
SCREAMING_SNAKE_CASE_ : str = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE_ : int = ['O'] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class lowerCAmelCase_ ( lowerCamelCase_ ):
def snake_case ( self ,snake_case__ ,snake_case__ ):
if isinstance(snake_case__ ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : List[str] = mode.value
SCREAMING_SNAKE_CASE_ : str = os.path.join(snake_case__ ,F'{mode}.txt' )
SCREAMING_SNAKE_CASE_ : Optional[int] = 1
SCREAMING_SNAKE_CASE_ : List[str] = []
with open(snake_case__ ,encoding='utf-8' ) as f:
for sentence in parse_incr(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Tuple = []
SCREAMING_SNAKE_CASE_ : List[str] = []
for token in sentence:
words.append(token['form'] )
labels.append(token['upos'] )
assert len(snake_case__ ) == len(snake_case__ )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' ,words=snake_case__ ,labels=snake_case__ ) )
guid_index += 1
return examples
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : int = 0
for sentence in parse_incr(snake_case__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = preds_list[example_id]
SCREAMING_SNAKE_CASE_ : Tuple = ''
for token in sentence:
out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '
out += "\n"
writer.write(snake_case__ )
example_id += 1
def snake_case ( self ,snake_case__ ):
if path:
with open(snake_case__ ,'r' ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 105
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "open-llama"
def __init__( self: int, a_: List[str]=100_000, a_: List[str]=4_096, a_: int=11_008, a_: Tuple=32, a_: Any=32, a_: Optional[Any]="silu", a_: Any=2_048, a_: List[Any]=0.02, a_: int=1E-6, a_: Optional[int]=True, a_: List[str]=0, a_: Any=1, a_: Optional[int]=2, a_: Tuple=False, a_: List[Any]=True, a_: Optional[int]=0.1, a_: Tuple=0.1, a_: List[Any]=True, a_: Optional[int]=True, a_: Dict=None, **a_: int, ):
'''simple docstring'''
_snake_case : Any = vocab_size
_snake_case : Tuple = max_position_embeddings
_snake_case : str = hidden_size
_snake_case : Dict = intermediate_size
_snake_case : str = num_hidden_layers
_snake_case : int = num_attention_heads
_snake_case : Union[str, Any] = hidden_act
_snake_case : Dict = initializer_range
_snake_case : Tuple = rms_norm_eps
_snake_case : Dict = use_cache
_snake_case : Optional[int] = kwargs.pop(
"""use_memorry_efficient_attention""", a_ )
_snake_case : List[Any] = hidden_dropout_prob
_snake_case : List[str] = attention_dropout_prob
_snake_case : Optional[int] = use_stable_embedding
_snake_case : int = shared_input_output_embedding
_snake_case : List[Any] = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=a_, bos_token_id=a_, eos_token_id=a_, tie_word_embeddings=a_, **a_, )
def UpperCamelCase_ ( self: List[Any] ):
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, a_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f"got {self.rope_scaling}" )
_snake_case : Optional[int] = self.rope_scaling.get("""type""", a_ )
_snake_case : Optional[int] = self.rope_scaling.get("""factor""", a_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(a_, a_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 609
| 0
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
snake_case__ = get_tests_dir('''fixtures''')
class lowerCAmelCase_ ( unittest.TestCase):
def _snake_case ( self : Tuple ) ->Tuple:
"""simple docstring"""
a__ :str = mock.Mock()
a__ :Optional[Any] = 500
a__ :Any = {}
a__ :int = HTTPError
a__ :str = {}
# Download this model to make sure it's in the cache.
a__ :List[Any] = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=__A ) as mock_head:
a__ :Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# This check we did call the fake head request
mock_head.assert_called()
def _snake_case ( self : Tuple ) ->Optional[Any]:
"""simple docstring"""
a__ :Tuple = WavaVecaFeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json" )
@is_staging_test
class lowerCAmelCase_ ( unittest.TestCase):
@classmethod
def _snake_case ( cls : Optional[int] ) ->List[str]:
"""simple docstring"""
a__ :str = TOKEN
HfFolder.save_token(__A )
@classmethod
def _snake_case ( cls : int ) ->Optional[Any]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-feature-extractor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-feature-extractor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-feature-extractor" )
except HTTPError:
pass
def _snake_case ( self : int ) ->Optional[Any]:
"""simple docstring"""
a__ :Tuple = WavaVecaFeatureExtractor.from_pretrained(__A )
feature_extractor.push_to_hub("test-feature-extractor" , use_auth_token=self._token )
a__ :Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
__A , repo_id="test-feature-extractor" , push_to_hub=__A , use_auth_token=self._token )
a__ :Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__A , getattr(__A , __A ) )
def _snake_case ( self : Dict ) ->str:
"""simple docstring"""
a__ :List[Any] = WavaVecaFeatureExtractor.from_pretrained(__A )
feature_extractor.push_to_hub("valid_org/test-feature-extractor" , use_auth_token=self._token )
a__ :str = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
__A , repo_id="valid_org/test-feature-extractor-org" , push_to_hub=__A , use_auth_token=self._token )
a__ :int = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__A , getattr(__A , __A ) )
def _snake_case ( self : Union[str, Any] ) ->str:
"""simple docstring"""
CustomFeatureExtractor.register_for_auto_class()
a__ :Dict = CustomFeatureExtractor.from_pretrained(__A )
feature_extractor.push_to_hub("test-dynamic-feature-extractor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"} , )
a__ :str = AutoFeatureExtractor.from_pretrained(
F'''{USER}/test-dynamic-feature-extractor''' , trust_remote_code=__A )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , "CustomFeatureExtractor" )
| 373
|
import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ) ->Any:
"""simple docstring"""
a__ :Optional[Any] = []
def _snake_case ( self : Optional[Any] , __A : List[Any] ) ->List[str]:
"""simple docstring"""
return self.node_position[vertex]
def _snake_case ( self : Optional[Any] , __A : str , __A : Any ) ->Dict:
"""simple docstring"""
a__ :Dict = pos
def _snake_case ( self : str , __A : Union[str, Any] , __A : Union[str, Any] , __A : Optional[Any] , __A : Optional[int] ) ->List[Any]:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
a__ :str = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
a__ :Optional[int] = 2 * start + 1
else:
a__ :List[Any] = 2 * start + 2
if heap[smallest_child] < heap[start]:
a__ , a__ :Optional[Any] = heap[smallest_child], positions[smallest_child]
a__ , a__ :int = (
heap[start],
positions[start],
)
a__ , a__ :List[Any] = temp, tempa
a__ :Any = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , __A )
self.top_to_bottom(__A , __A , __A , __A )
def _snake_case ( self : List[str] , __A : Any , __A : List[str] , __A : Any , __A : str ) ->Optional[Any]:
"""simple docstring"""
a__ :Optional[Any] = position[index]
while index != 0:
a__ :str = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
a__ :int = heap[parent]
a__ :Optional[Any] = position[parent]
self.set_position(position[parent] , __A )
else:
a__ :List[Any] = val
a__ :List[Any] = temp
self.set_position(__A , __A )
break
a__ :Union[str, Any] = parent
else:
a__ :int = val
a__ :Dict = temp
self.set_position(__A , 0 )
def _snake_case ( self : Tuple , __A : int , __A : int ) ->Union[str, Any]:
"""simple docstring"""
a__ :Tuple = len(__A ) // 2 - 1
for i in range(__A , -1 , -1 ):
self.top_to_bottom(__A , __A , len(__A ) , __A )
def _snake_case ( self : List[Any] , __A : List[Any] , __A : int ) ->Optional[Any]:
"""simple docstring"""
a__ :Any = positions[0]
a__ :str = sys.maxsize
self.top_to_bottom(__A , 0 , len(__A ) , __A )
return temp
def lowerCamelCase__ ( a : Any ) -> Union[str, Any]:
"""simple docstring"""
a__ :Tuple = Heap()
a__ :List[Any] = [0] * len(a )
a__ :str = [-1] * len(a ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
a__ :Any = [] # Heap of Distance of vertices from their neighboring vertex
a__ :int = []
for vertex in range(len(a ) ):
distance_tv.append(sys.maxsize )
positions.append(a )
heap.node_position.append(a )
a__ :Tuple = []
a__ :Any = 1
a__ :int = sys.maxsize
for neighbor, distance in adjacency_list[0]:
a__ :int = 0
a__ :List[str] = distance
heap.heapify(a , a )
for _ in range(1 , len(a ) ):
a__ :Dict = heap.delete_minimum(a , a )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
a__ :Optional[int] = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(a )]
):
a__ :List[str] = distance
heap.bottom_to_top(
a , heap.get_position(a ) , a , a )
a__ :str = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
snake_case__ = int(input('''Enter number of edges: ''').strip())
snake_case__ = defaultdict(list)
for _ in range(edges_number):
snake_case__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 373
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__snake_case : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase ( a ):
"""simple docstring"""
def __init__( self : Optional[int] , *_lowerCamelCase : int , **_lowerCamelCase : Optional[Any] ):
warnings.warn(
'''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use OwlViTImageProcessor instead.''' , _lowerCamelCase , )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
| 571
|
"""simple docstring"""
def a_ ( __a ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
A__ = sorted(string.lower() )
return len(__a ) == len(set(__a ) )
if __name__ == "__main__":
__snake_case : Any = input('Enter a string ').strip()
__snake_case : Dict = is_isogram(input_str)
print(f'{input_str} is {"an" if isogram else "not an"} isogram.')
| 571
| 1
|
import operator as op
def snake_case_ ( __lowercase ):
UpperCAmelCase_ : Optional[Any] = []
UpperCAmelCase_ : Optional[Any] = lambda __lowercase , __lowercase : int(x / y ) # noqa: E731 integer division operation
UpperCAmelCase_ : List[Any] = {
'''^''': op.pow,
'''*''': op.mul,
'''/''': div,
'''+''': op.add,
'''-''': op.sub,
} # operators & their respective operation
# print table header
print('''Symbol'''.center(8 ) , '''Action'''.center(1_2 ) , '''Stack''' , sep=''' | ''' )
print('''-''' * (3_0 + len(UpperCAmelCase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(UpperCAmelCase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('''push(''' + x + ''')''').ljust(1_2 ) , ''','''.join(UpperCAmelCase__ ) , sep=''' | ''' )
else:
UpperCAmelCase_ : int = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + b + ''')''').ljust(1_2 ) , ''','''.join(UpperCAmelCase__ ) , sep=''' | ''' )
UpperCAmelCase_ : Union[str, Any] = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + a + ''')''').ljust(1_2 ) , ''','''.join(UpperCAmelCase__ ) , sep=''' | ''' )
stack.append(
str(opr[x](int(UpperCAmelCase__ ) , int(UpperCAmelCase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('''push(''' + a + x + b + ''')''').ljust(1_2 ) , ''','''.join(UpperCAmelCase__ ) , sep=''' | ''' , )
return int(stack[0] )
if __name__ == "__main__":
__UpperCamelCase : List[str] = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
print('\n\tResult = ', solve(Postfix))
| 715
|
# 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_torch_available, is_vision_available
__UpperCamelCase : List[Any] = {
'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = ['VivitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'VivitModel',
'VivitPreTrainedModel',
'VivitForVideoClassification',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
__UpperCamelCase : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 641
| 0
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase: List[str] = logging.get_logger(__name__)
_lowerCAmelCase: Any = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
),
}
class lowercase_ (lowercase__ ):
snake_case ='pix2struct_text_model'
snake_case =['past_key_values']
snake_case ={
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , lowercase_=50244 , lowercase_=768 , lowercase_=64 , lowercase_=2048 , lowercase_=12 , lowercase_=12 , lowercase_=32 , lowercase_=128 , lowercase_=0.1 , lowercase_=1e-6 , lowercase_=1.0 , lowercase_="gelu_new" , lowercase_=0 , lowercase_=False , lowercase_=0 , lowercase_=1 , lowercase_=False , lowercase_=True , **lowercase_ , ) -> str:
a__ =vocab_size
a__ =hidden_size
a__ =d_kv
a__ =d_ff
a__ =num_layers
a__ =num_heads
a__ =relative_attention_num_buckets
a__ =relative_attention_max_distance
a__ =dropout_rate
a__ =layer_norm_epsilon
a__ =initializer_factor
a__ =use_cache
a__ =eos_token_id
a__ =decoder_start_token_id
# for backwards compatibility
a__ =dense_act_fn
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , decoder_start_token_id=lowercase_ , tie_word_embeddings=lowercase_ , is_decoder=lowercase_ , **lowercase_ , )
@classmethod
def __UpperCamelCase ( cls , lowercase_ , **lowercase_) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowercase_)
a__ , a__ =cls.get_config_dict(lowercase_ , **lowercase_)
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type') == "pix2struct":
a__ =config_dict['text_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(lowercase_ , **lowercase_)
class lowercase_ (lowercase__ ):
snake_case ='pix2struct_vision_model'
def __init__( self , lowercase_=768 , lowercase_=768 , lowercase_=2048 , lowercase_=64 , lowercase_=12 , lowercase_=12 , lowercase_="gelu_new" , lowercase_=1e-6 , lowercase_=0.0 , lowercase_=0.0 , lowercase_=1e-10 , lowercase_=1.0 , lowercase_=4096 , lowercase_=32 , lowercase_=128 , **lowercase_ , ) -> Optional[Any]:
super().__init__(**lowercase_)
a__ =hidden_size
a__ =patch_embed_hidden_size
a__ =d_ff
a__ =dropout_rate
a__ =num_hidden_layers
a__ =num_attention_heads
a__ =initializer_range
a__ =initializer_factor
a__ =attention_dropout
a__ =layer_norm_eps
a__ =dense_act_fn
a__ =seq_len
a__ =relative_attention_num_buckets
a__ =relative_attention_max_distance
a__ =d_kv
@classmethod
def __UpperCamelCase ( cls , lowercase_ , **lowercase_) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowercase_)
a__ , a__ =cls.get_config_dict(lowercase_ , **lowercase_)
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type') == "pix2struct":
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(lowercase_ , **lowercase_)
class lowercase_ (lowercase__ ):
snake_case ='pix2struct'
snake_case =True
def __init__( self , lowercase_=None , lowercase_=None , lowercase_=1.0 , lowercase_=0.02 , lowercase_=False , lowercase_=False , lowercase_=True , **lowercase_ , ) -> str:
super().__init__(tie_word_embeddings=lowercase_ , is_encoder_decoder=lowercase_ , **lowercase_)
if text_config is None:
a__ ={}
logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.')
if vision_config is None:
a__ ={}
logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.')
a__ =PixaStructTextConfig(**lowercase_)
a__ =PixaStructVisionConfig(**lowercase_)
a__ =self.text_config.decoder_start_token_id
a__ =self.text_config.pad_token_id
a__ =self.text_config.eos_token_id
a__ =initializer_factor
a__ =initializer_range
a__ =self.initializer_range
a__ =self.initializer_range
a__ =is_vqa
@classmethod
def __UpperCamelCase ( cls , lowercase_ , lowercase_ , **lowercase_) -> Any:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **lowercase_)
def __UpperCamelCase ( self) -> Union[str, Any]:
a__ =copy.deepcopy(self.__dict__)
a__ =self.text_config.to_dict()
a__ =self.vision_config.to_dict()
a__ =self.__class__.model_type
return output
| 20
|
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __snake_case ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self :int ):
_a = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(UpperCamelCase__ , "tf_padding" ) )
self.parent.assertTrue(hasattr(UpperCamelCase__ , "depth_multiplier" ) )
class __snake_case :
"""simple docstring"""
def __init__( self :Dict , UpperCamelCase__ :Union[str, Any] , UpperCamelCase__ :Tuple=13 , UpperCamelCase__ :Optional[int]=3 , UpperCamelCase__ :int=32 , UpperCamelCase__ :List[Any]=0.25 , UpperCamelCase__ :Optional[int]=8 , UpperCamelCase__ :List[Any]=True , UpperCamelCase__ :Any=1_024 , UpperCamelCase__ :List[Any]=32 , UpperCamelCase__ :Union[str, Any]="relu6" , UpperCamelCase__ :List[Any]=0.1 , UpperCamelCase__ :Any=0.02 , UpperCamelCase__ :int=True , UpperCamelCase__ :str=True , UpperCamelCase__ :List[Any]=10 , UpperCamelCase__ :Tuple=None , ):
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = depth_multiplier
_a = min_depth
_a = tf_padding
_a = int(last_hidden_size * depth_multiplier )
_a = output_stride
_a = hidden_act
_a = classifier_dropout_prob
_a = use_labels
_a = is_training
_a = num_labels
_a = initializer_range
_a = scope
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
_a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.num_labels )
_a = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_a = self.get_config()
return config, pixel_values, labels, pixel_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ ( self :int , UpperCamelCase__ :Optional[Any] , UpperCamelCase__ :Dict , UpperCamelCase__ :Union[str, Any] , UpperCamelCase__ :Union[str, Any] ):
_a = MobileNetVaModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
_a = model(UpperCamelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def SCREAMING_SNAKE_CASE_ ( self :str , UpperCamelCase__ :Optional[Any] , UpperCamelCase__ :List[Any] , UpperCamelCase__ :Any , UpperCamelCase__ :Union[str, Any] ):
_a = self.num_labels
_a = MobileNetVaForImageClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
_a = model(UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self :str ):
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase_ : str = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
lowerCAmelCase_ : int = (
{'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Dict = False
lowerCAmelCase_ : Optional[Any] = False
lowerCAmelCase_ : Union[str, Any] = False
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
_a = MobileNetVaModelTester(self )
_a = MobileNetVaConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( self :str ):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE_ ( self :str ):
pass
@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason="MobileNetV1 does not output attentions" )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(UpperCamelCase__ )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
def check_hidden_states_output(UpperCamelCase__ :Optional[int] , UpperCamelCase__ :List[str] , UpperCamelCase__ :str ):
_a = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
_a = outputs.hidden_states
_a = 26
self.assertEqual(len(UpperCamelCase__ ) , UpperCamelCase__ )
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = MobileNetVaModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def __a ( ):
"""simple docstring"""
_a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
)
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
_a = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(UpperCamelCase__ )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=UpperCamelCase__ , return_tensors="pt" ).to(UpperCamelCase__ )
# forward pass
with torch.no_grad():
_a = model(**UpperCamelCase__ )
# verify the logits
_a = torch.Size((1, 1_001) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
_a = torch.tensor([-4.1739, -1.1233, 3.1205] ).to(UpperCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1E-4 ) )
| 388
| 0
|
'''simple docstring'''
import sys
snake_case_ :Dict = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def _a ( _lowercase : str ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = 1
for digit in s:
product *= int(_lowercase )
return product
def _a ( _lowercase : str = N ):
'''simple docstring'''
__UpperCAmelCase : List[str] = -sys.maxsize - 1
__UpperCAmelCase : Dict = n[:13]
__UpperCAmelCase : Tuple = 13
while cur_index < len(_lowercase ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
__UpperCAmelCase : Tuple = substr[1:] + n[cur_index]
cur_index += 1
else:
__UpperCAmelCase : int = max(_lowercase , str_eval(_lowercase ) )
__UpperCAmelCase : Tuple = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 700
|
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class a :
"""simple docstring"""
def __init__( self : Any , snake_case : int | None = None ) -> int:
__UpperCAmelCase : str = value
__UpperCAmelCase : Node | None = None # Added in order to delete a node easier
__UpperCAmelCase : Node | None = None
__UpperCAmelCase : Node | None = None
def __repr__( self : str ) -> str:
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f'{self.value}': (self.left, self.right)} , indent=1 )
class a :
"""simple docstring"""
def __init__( self : Optional[int] , snake_case : Node | None = None ) -> str:
__UpperCAmelCase : Optional[Any] = root
def __str__( self : str ) -> str:
return str(self.root )
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : Node , snake_case : Node | None ) -> None:
if new_children is not None: # reset its kids
__UpperCAmelCase : List[str] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(snake_case ): # If it is the right children
__UpperCAmelCase : int = new_children
else:
__UpperCAmelCase : Tuple = new_children
else:
__UpperCAmelCase : List[Any] = new_children
def lowerCamelCase__ ( self : Optional[int] , snake_case : Node ) -> bool:
if node.parent and node.parent.right:
return node == node.parent.right
return False
def lowerCamelCase__ ( self : int ) -> bool:
return self.root is None
def lowerCamelCase__ ( self : Optional[int] , snake_case : Optional[Any] ) -> None:
__UpperCAmelCase : int = Node(snake_case ) # create a new Node
if self.empty(): # if Tree is empty
__UpperCAmelCase : List[Any] = new_node # set its root
else: # Tree is not empty
__UpperCAmelCase : Tuple = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
__UpperCAmelCase : Optional[int] = new_node # We insert the new node in a leaf
break
else:
__UpperCAmelCase : List[Any] = parent_node.left
else:
if parent_node.right is None:
__UpperCAmelCase : Optional[int] = new_node
break
else:
__UpperCAmelCase : List[str] = parent_node.right
__UpperCAmelCase : int = parent_node
def lowerCamelCase__ ( self : Optional[int] , *snake_case : List[Any] ) -> None:
for value in values:
self.__insert(snake_case )
def lowerCamelCase__ ( self : Optional[int] , snake_case : Dict ) -> Node | None:
if self.empty():
raise IndexError('''Warning: Tree is empty! please use another.''' )
else:
__UpperCAmelCase : List[Any] = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
__UpperCAmelCase : Union[str, Any] = node.left if value < node.value else node.right
return node
def lowerCamelCase__ ( self : str , snake_case : Node | None = None ) -> Node | None:
if node is None:
if self.root is None:
return None
__UpperCAmelCase : Optional[Any] = self.root
if not self.empty():
while node.right is not None:
__UpperCAmelCase : str = node.right
return node
def lowerCamelCase__ ( self : int , snake_case : Node | None = None ) -> Node | None:
if node is None:
__UpperCAmelCase : str = self.root
if self.root is None:
return None
if not self.empty():
__UpperCAmelCase : List[str] = self.root
while node.left is not None:
__UpperCAmelCase : str = node.left
return node
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : int ) -> None:
__UpperCAmelCase : List[str] = self.search(snake_case ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(snake_case , snake_case )
elif node.left is None: # Has only right children
self.__reassign_nodes(snake_case , node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(snake_case , node.left )
else:
__UpperCAmelCase : Optional[int] = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
__UpperCAmelCase : List[str] = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def lowerCamelCase__ ( self : List[str] , snake_case : Node | None ) -> Iterable:
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : int=None ) -> Any:
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def lowerCamelCase__ ( self : str , snake_case : list , snake_case : Node | None ) -> None:
if node:
self.inorder(snake_case , node.left )
arr.append(node.value )
self.inorder(snake_case , node.right )
def lowerCamelCase__ ( self : Optional[int] , snake_case : int , snake_case : Node ) -> int:
__UpperCAmelCase : list[int] = []
self.inorder(snake_case , snake_case ) # append all values to list using inorder traversal
return arr[k - 1]
def _a ( _lowercase : Node | None ):
'''simple docstring'''
__UpperCAmelCase : List[str] = []
if curr_node is not None:
__UpperCAmelCase : Union[str, Any] = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def _a ( ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = (8, 3, 6, 1, 10, 14, 13, 4, 7)
__UpperCAmelCase : Union[str, Any] = BinarySearchTree()
for i in testlist:
t.insert(_lowercase )
# Prints all the elements of the list in order traversal
print(_lowercase )
if t.search(6 ) is not None:
print('''The value 6 exists''' )
else:
print('''The value 6 doesn\'t exist''' )
if t.search(-1 ) is not None:
print('''The value -1 exists''' )
else:
print('''The value -1 doesn\'t exist''' )
if not t.empty():
print('''Max Value: ''' , t.get_max().value ) # type: ignore
print('''Min Value: ''' , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(_lowercase )
print(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 266
| 0
|
import re
from filelock import FileLock
try:
import nltk
__snake_case : Optional[int] = True
except (ImportError, ModuleNotFoundError):
__snake_case : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def _UpperCAmelCase ( a__):
'''simple docstring'''
re.sub("""<n>""" , """""" , __lowerCamelCase) # 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(__lowerCamelCase))
| 540
|
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"""simple docstring"""
a : Dict = 1
@register_to_config
def __init__( self : int , __lowercase : int = 1000 , __lowercase : Optional[Union[np.ndarray, List[float]]] = None ) -> Union[str, Any]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(__lowercase )
# standard deviation of the initial noise distribution
__UpperCAmelCase : List[Any] = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
__UpperCAmelCase : List[Any] = 4
# running values
__UpperCAmelCase : str = []
def UpperCAmelCase ( self : Union[str, Any] , __lowercase : int , __lowercase : Union[str, torch.device] = None ) -> int:
__UpperCAmelCase : int = num_inference_steps
__UpperCAmelCase : Union[str, Any] = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
__UpperCAmelCase : Union[str, Any] = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
__UpperCAmelCase : Dict = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
__UpperCAmelCase : Dict = torch.sin(steps * math.pi / 2 ) ** 2
__UpperCAmelCase : List[Any] = (1.0 - self.betas**2) ** 0.5
__UpperCAmelCase : Tuple = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
__UpperCAmelCase : Dict = timesteps.to(__lowercase )
__UpperCAmelCase : Optional[Any] = []
def UpperCAmelCase ( self : Optional[int] , __lowercase : torch.FloatTensor , __lowercase : int , __lowercase : torch.FloatTensor , __lowercase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
__UpperCAmelCase : List[str] = (self.timesteps == timestep).nonzero().item()
__UpperCAmelCase : Optional[Any] = timestep_index + 1
__UpperCAmelCase : List[str] = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__lowercase )
if len(self.ets ) == 1:
__UpperCAmelCase : Tuple = self.ets[-1]
elif len(self.ets ) == 2:
__UpperCAmelCase : Union[str, Any] = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
__UpperCAmelCase : Union[str, Any] = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
__UpperCAmelCase : List[Any] = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
__UpperCAmelCase : Union[str, Any] = self._get_prev_sample(__lowercase , __lowercase , __lowercase , __lowercase )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowercase )
def UpperCAmelCase ( self : Optional[Any] , __lowercase : torch.FloatTensor , *__lowercase : Optional[Any] , **__lowercase : Any ) -> torch.FloatTensor:
return sample
def UpperCAmelCase ( self : Tuple , __lowercase : Tuple , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Dict ) -> str:
__UpperCAmelCase : int = self.alphas[timestep_index]
__UpperCAmelCase : Tuple = self.betas[timestep_index]
__UpperCAmelCase : Any = self.alphas[prev_timestep_index]
__UpperCAmelCase : List[str] = self.betas[prev_timestep_index]
__UpperCAmelCase : List[str] = (sample - sigma * ets) / max(__lowercase , 1e-8 )
__UpperCAmelCase : List[Any] = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Tuple ) -> str:
return self.config.num_train_timesteps
| 63
| 0
|
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowercase ( lowerCAmelCase__ : Optional[int] ) -> int:
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.fixture
def lowercase ( lowerCAmelCase__ : Any ) -> Any:
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self , _a ):
__a = metric_id
class __lowerCAmelCase :
'''simple docstring'''
__UpperCAmelCase : Any = [MetricMock(__SCREAMING_SNAKE_CASE ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]
def __UpperCAmelCase ( self ):
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 lowercase ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Tuple ) -> Optional[int]:
if "tmp_path" in args:
__a = 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__ )
| 720
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowercase_ = abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def lowercase ( lowerCAmelCase__ : List[Any] ) -> str:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : int ) -> Union[str, Any]:
from transformers.testing_utils import pytest_terminal_summary_main
__a = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(lowerCAmelCase__ , id=lowerCAmelCase__ )
| 65
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : str = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
"feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
"processing_wav2vec2": ["Wav2Vec2Processor"],
"tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : int = [
"WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Wav2Vec2ForAudioFrameClassification",
"Wav2Vec2ForCTC",
"Wav2Vec2ForMaskedLM",
"Wav2Vec2ForPreTraining",
"Wav2Vec2ForSequenceClassification",
"Wav2Vec2ForXVector",
"Wav2Vec2Model",
"Wav2Vec2PreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : int = [
"TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFWav2Vec2ForCTC",
"TFWav2Vec2Model",
"TFWav2Vec2PreTrainedModel",
"TFWav2Vec2ForSequenceClassification",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
"FlaxWav2Vec2ForCTC",
"FlaxWav2Vec2ForPreTraining",
"FlaxWav2Vec2Model",
"FlaxWav2Vec2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
"""google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
"""google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
"""google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class lowerCAmelCase ( UpperCamelCase_ ):
A_ : List[str] = """mobilenet_v2"""
def __init__( self : Optional[int] , a__ : Dict=3 , a__ : str=224 , a__ : int=1.0 , a__ : Dict=8 , a__ : Dict=8 , a__ : Dict=6 , a__ : Optional[Any]=32 , a__ : List[str]=True , a__ : int=True , a__ : List[str]="relu6" , a__ : List[str]=True , a__ : List[Any]=0.8 , a__ : Tuple=0.02 , a__ : Tuple=0.001 , a__ : Optional[Any]=255 , **a__ : str , ):
'''simple docstring'''
super().__init__(**a__ )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
lowerCAmelCase__ : Any = num_channels
lowerCAmelCase__ : Union[str, Any] = image_size
lowerCAmelCase__ : Tuple = depth_multiplier
lowerCAmelCase__ : Optional[int] = depth_divisible_by
lowerCAmelCase__ : Union[str, Any] = min_depth
lowerCAmelCase__ : List[str] = expand_ratio
lowerCAmelCase__ : Union[str, Any] = output_stride
lowerCAmelCase__ : Optional[int] = first_layer_is_expansion
lowerCAmelCase__ : Tuple = finegrained_output
lowerCAmelCase__ : Optional[int] = hidden_act
lowerCAmelCase__ : Optional[Any] = tf_padding
lowerCAmelCase__ : Union[str, Any] = classifier_dropout_prob
lowerCAmelCase__ : Optional[int] = initializer_range
lowerCAmelCase__ : str = layer_norm_eps
lowerCAmelCase__ : int = semantic_loss_ignore_index
class lowerCAmelCase ( UpperCamelCase_ ):
A_ : Optional[int] = version.parse("""1.11""" )
@property
def _A ( self : List[Any] ):
'''simple docstring'''
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def _A ( self : Optional[int] ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})] )
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )
@property
def _A ( self : List[str] ):
'''simple docstring'''
return 1e-4
| 378
| 0
|
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE_ : str = 'scheduler_config.json'
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = 1
UpperCAmelCase = 2
UpperCAmelCase = 3
UpperCAmelCase = 4
UpperCAmelCase = 5
UpperCAmelCase = 6
UpperCAmelCase = 7
UpperCAmelCase = 8
UpperCAmelCase = 9
UpperCAmelCase = 1_0
UpperCAmelCase = 1_1
UpperCAmelCase = 1_2
UpperCAmelCase = 1_3
UpperCAmelCase = 1_4
@dataclass
class a ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = 42
class a :
"""simple docstring"""
UpperCAmelCase = SCHEDULER_CONFIG_NAME
UpperCAmelCase = []
UpperCAmelCase = True
@classmethod
def UpperCamelCase ( cls: List[Any] , UpperCamelCase: Dict[str, Any] = None , UpperCamelCase: Optional[str] = None , UpperCamelCase: Optional[int]=False , **UpperCamelCase: str , ):
"""simple docstring"""
A__ , A__ , A__ = cls.load_config(
pretrained_model_name_or_path=UpperCamelCase , subfolder=UpperCamelCase , return_unused_kwargs=UpperCamelCase , return_commit_hash=UpperCamelCase , **UpperCamelCase , )
return cls.from_config(UpperCamelCase , return_unused_kwargs=UpperCamelCase , **UpperCamelCase )
def UpperCamelCase ( self: Optional[int] , UpperCamelCase: Union[str, os.PathLike] , UpperCamelCase: bool = False , **UpperCamelCase: int ):
"""simple docstring"""
self.save_config(save_directory=UpperCamelCase , push_to_hub=UpperCamelCase , **UpperCamelCase )
@property
def UpperCamelCase ( self: int ):
"""simple docstring"""
return self._get_compatibles()
@classmethod
def UpperCamelCase ( cls: Union[str, Any] ):
"""simple docstring"""
A__ = list(set([cls.__name__] + cls._compatibles ) )
A__ = importlib.import_module(__name__.split(""".""" )[0] )
A__ = [
getattr(UpperCamelCase , UpperCamelCase ) for c in compatible_classes_str if hasattr(UpperCamelCase , UpperCamelCase )
]
return compatible_classes
| 500
|
"""simple docstring"""
from itertools import count
def _snake_case ( UpperCAmelCase_ : int = 50 ):
A__ = [1] * min_block_length
for n in count(UpperCAmelCase_ ):
fill_count_functions.append(1 )
for block_length in range(UpperCAmelCase_ , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 100_0000:
break
return n
if __name__ == "__main__":
print(f"""{solution() = }""")
| 500
| 1
|
'''simple docstring'''
import math
def __A ( a_ : Optional[int] ,a_ : Dict ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(a_ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("This should never happen" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
lowerCAmelCase = 'Enter the base and the power separated by a comma: '
lowerCAmelCase = map(int, input(prompt).split(""","""))
lowerCAmelCase = map(int, input(prompt).split(""","""))
# We find the log of each number, using the function res(), which takes two
# arguments.
lowerCAmelCase = res(xa, ya)
lowerCAmelCase = res(xa, ya)
# We check for the largest number
if resa > resa:
print("""Largest number is""", xa, """^""", ya)
elif resa > resa:
print("""Largest number is""", xa, """^""", ya)
else:
print("""Both are equal""")
| 525
|
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCAmelCase__ ( __magic_name__ : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def UpperCAmelCase__ ( __magic_name__ : np.ndarray , __magic_name__ : np.ndarray , __magic_name__ : np.ndarray ):
'''simple docstring'''
lowerCAmelCase : Tuple = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__magic_name__ , __magic_name__ )
# Predict target for test data
lowerCAmelCase : str = xgb.predict(__magic_name__ )
lowerCAmelCase : Any = predictions.reshape(len(__magic_name__ ) , 1 )
return predictions
def UpperCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = fetch_california_housing()
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = data_handling(__magic_name__ )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : List[str] = train_test_split(
__magic_name__ , __magic_name__ , test_size=0.25 , random_state=1 )
lowerCAmelCase : Dict = xgboost(__magic_name__ , __magic_name__ , __magic_name__ )
# Error printing
print(f'''Mean Absolute Error : {mean_absolute_error(__magic_name__ , __magic_name__ )}''' )
print(f'''Mean Square Error : {mean_squared_error(__magic_name__ , __magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 348
| 0
|
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class snake_case_ ( unittest.TestCase ):
def __A ( self , __lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = jnp.ones((batch_size, length) ) / length
return scores
def __A ( self ):
SCREAMING_SNAKE_CASE_ : Optional[int] = None
SCREAMING_SNAKE_CASE_ : Tuple = 20
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self._get_uniform_logits(batch_size=2 , length=__lowerCAmelCase )
# tweak scores to not be uniform anymore
SCREAMING_SNAKE_CASE_ : List[Any] = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
SCREAMING_SNAKE_CASE_ : List[Any] = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
SCREAMING_SNAKE_CASE_ : int = jax.nn.softmax(__lowerCAmelCase , axis=-1 )
SCREAMING_SNAKE_CASE_ : Dict = FlaxTemperatureLogitsWarper(temperature=0.5 )
SCREAMING_SNAKE_CASE_ : Any = FlaxTemperatureLogitsWarper(temperature=1.3 )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = jax.nn.softmax(temp_dist_warper_sharper(__lowerCAmelCase , scores.copy() , cur_len=__lowerCAmelCase ) , axis=-1 )
SCREAMING_SNAKE_CASE_ : Optional[Any] = jax.nn.softmax(temp_dist_warper_smoother(__lowerCAmelCase , scores.copy() , cur_len=__lowerCAmelCase ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1e-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1e-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def __A ( self ):
SCREAMING_SNAKE_CASE_ : List[str] = None
SCREAMING_SNAKE_CASE_ : Optional[int] = 10
SCREAMING_SNAKE_CASE_ : int = 2
# create ramp distribution
SCREAMING_SNAKE_CASE_ : Optional[Any] = np.broadcast_to(np.arange(__lowerCAmelCase )[None, :] , (batch_size, vocab_size) ).copy()
SCREAMING_SNAKE_CASE_ : Dict = ramp_logits[1:, : vocab_size // 2] + vocab_size
SCREAMING_SNAKE_CASE_ : List[Any] = FlaxTopKLogitsWarper(3 )
SCREAMING_SNAKE_CASE_ : int = top_k_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
SCREAMING_SNAKE_CASE_ : Optional[int] = 5
SCREAMING_SNAKE_CASE_ : Dict = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
SCREAMING_SNAKE_CASE_ : str = np.broadcast_to(np.arange(__lowerCAmelCase )[None, :] , (batch_size, length) ).copy()
SCREAMING_SNAKE_CASE_ : Any = top_k_warp_safety_check(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def __A ( self ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = None
SCREAMING_SNAKE_CASE_ : Dict = 10
SCREAMING_SNAKE_CASE_ : List[Any] = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
SCREAMING_SNAKE_CASE_ : Dict = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
SCREAMING_SNAKE_CASE_ : List[str] = FlaxTopPLogitsWarper(0.8 )
SCREAMING_SNAKE_CASE_ : List[str] = np.exp(top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
SCREAMING_SNAKE_CASE_ : Any = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1e-3 ) )
# check edge cases with negative and extreme logits
SCREAMING_SNAKE_CASE_ : List[Any] = np.broadcast_to(np.arange(__lowerCAmelCase )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
SCREAMING_SNAKE_CASE_ : Optional[int] = ramp_logits[1] * 1_00.0
# make sure at least 2 tokens are kept
SCREAMING_SNAKE_CASE_ : Dict = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
SCREAMING_SNAKE_CASE_ : List[Any] = top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def __A ( self ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = 20
SCREAMING_SNAKE_CASE_ : Optional[int] = 4
SCREAMING_SNAKE_CASE_ : Dict = 0
SCREAMING_SNAKE_CASE_ : List[Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=__lowerCAmelCase )
# check that min length is applied at length 5
SCREAMING_SNAKE_CASE_ : List[Any] = ids_tensor((batch_size, 20) , vocab_size=20 )
SCREAMING_SNAKE_CASE_ : str = 5
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = min_dist_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('inf' )] )
# check that min length is not applied anymore at length 15
SCREAMING_SNAKE_CASE_ : Dict = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Any = 15
SCREAMING_SNAKE_CASE_ : List[Any] = min_dist_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertFalse(jnp.isinf(__lowerCAmelCase ).any() )
def __A ( self ):
SCREAMING_SNAKE_CASE_ : Tuple = 20
SCREAMING_SNAKE_CASE_ : str = 4
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : Tuple = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
SCREAMING_SNAKE_CASE_ : Any = ids_tensor((batch_size, 1) , vocab_size=20 )
SCREAMING_SNAKE_CASE_ : Dict = 1
SCREAMING_SNAKE_CASE_ : List[str] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : int = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
SCREAMING_SNAKE_CASE_ : int = 3
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[Any] = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertFalse(jnp.isinf(__lowerCAmelCase ).any() )
def __A ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] = 20
SCREAMING_SNAKE_CASE_ : Optional[Any] = 4
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ : List[Any] = 5
SCREAMING_SNAKE_CASE_ : Optional[int] = FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCAmelCase , eos_token_id=__lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
SCREAMING_SNAKE_CASE_ : Dict = ids_tensor((batch_size, 4) , vocab_size=20 )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 4
SCREAMING_SNAKE_CASE_ : List[Any] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
SCREAMING_SNAKE_CASE_ : str = 3
SCREAMING_SNAKE_CASE_ : int = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = logits_processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
self.assertFalse(jnp.isinf(__lowerCAmelCase ).any() )
def __A ( self ):
SCREAMING_SNAKE_CASE_ : int = 4
SCREAMING_SNAKE_CASE_ : List[str] = 10
SCREAMING_SNAKE_CASE_ : str = 15
SCREAMING_SNAKE_CASE_ : Tuple = 2
SCREAMING_SNAKE_CASE_ : List[str] = 1
SCREAMING_SNAKE_CASE_ : Optional[Any] = 15
# dummy input_ids and scores
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ids_tensor((batch_size, sequence_length) , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = input_ids.copy()
SCREAMING_SNAKE_CASE_ : Tuple = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = scores.copy()
# instantiate all dist processors
SCREAMING_SNAKE_CASE_ : int = FlaxTemperatureLogitsWarper(temperature=0.5 )
SCREAMING_SNAKE_CASE_ : Tuple = FlaxTopKLogitsWarper(3 )
SCREAMING_SNAKE_CASE_ : Optional[int] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
SCREAMING_SNAKE_CASE_ : Tuple = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCAmelCase , eos_token_id=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = 10
# no processor list
SCREAMING_SNAKE_CASE_ : str = temp_dist_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Any = top_k_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : str = top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = min_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = bos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = eos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# with processor list
SCREAMING_SNAKE_CASE_ : int = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
SCREAMING_SNAKE_CASE_ : Dict = processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def __A ( self ):
SCREAMING_SNAKE_CASE_ : Dict = 4
SCREAMING_SNAKE_CASE_ : Dict = 10
SCREAMING_SNAKE_CASE_ : Optional[Any] = 15
SCREAMING_SNAKE_CASE_ : Optional[Any] = 2
SCREAMING_SNAKE_CASE_ : Dict = 1
SCREAMING_SNAKE_CASE_ : int = 15
# dummy input_ids and scores
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ids_tensor((batch_size, sequence_length) , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : str = input_ids.copy()
SCREAMING_SNAKE_CASE_ : List[Any] = self._get_uniform_logits(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = scores.copy()
# instantiate all dist processors
SCREAMING_SNAKE_CASE_ : Tuple = FlaxTemperatureLogitsWarper(temperature=0.5 )
SCREAMING_SNAKE_CASE_ : Tuple = FlaxTopKLogitsWarper(3 )
SCREAMING_SNAKE_CASE_ : List[str] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
SCREAMING_SNAKE_CASE_ : Union[str, Any] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[str] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCAmelCase , eos_token_id=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 10
# no processor list
def run_no_processor_list(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : List[Any] = temp_dist_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : int = top_k_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = top_p_warp(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[str] = min_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = bos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : str = eos_dist_proc(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
return scores
# with processor list
def run_processor_list(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : Dict = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
SCREAMING_SNAKE_CASE_ : Tuple = processor(__lowerCAmelCase , __lowerCAmelCase , cur_len=__lowerCAmelCase )
return scores
SCREAMING_SNAKE_CASE_ : Union[str, Any] = jax.jit(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Any = jax.jit(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = jitted_run_no_processor_list(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = jitted_run_processor_list(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
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|
class snake_case_ :
def __init__( self ):
SCREAMING_SNAKE_CASE_ : str = ''
SCREAMING_SNAKE_CASE_ : Tuple = ''
SCREAMING_SNAKE_CASE_ : str = []
def __A ( self , __lowerCAmelCase , __lowerCAmelCase ):
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
SCREAMING_SNAKE_CASE_ : int = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
SCREAMING_SNAKE_CASE_ : Tuple = self.__min_dist_top_down_dp(__lowerCAmelCase , n - 1 )
SCREAMING_SNAKE_CASE_ : List[str] = self.__min_dist_top_down_dp(m - 1 , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Tuple = self.__min_dist_top_down_dp(m - 1 , n - 1 )
SCREAMING_SNAKE_CASE_ : Any = 1 + min(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return self.dp[m][n]
def __A ( self , __lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : Any = worda
SCREAMING_SNAKE_CASE_ : List[str] = worda
SCREAMING_SNAKE_CASE_ : int = [[-1 for _ in range(len(__lowerCAmelCase ) )] for _ in range(len(__lowerCAmelCase ) )]
return self.__min_dist_top_down_dp(len(__lowerCAmelCase ) - 1 , len(__lowerCAmelCase ) - 1 )
def __A ( self , __lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : List[Any] = worda
SCREAMING_SNAKE_CASE_ : List[str] = worda
SCREAMING_SNAKE_CASE_ : Tuple = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[str] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Any = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
SCREAMING_SNAKE_CASE_ : int = j
elif j == 0: # second string is empty
SCREAMING_SNAKE_CASE_ : Union[str, Any] = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
SCREAMING_SNAKE_CASE_ : int = self.dp[i - 1][j - 1]
else:
SCREAMING_SNAKE_CASE_ : int = self.dp[i][j - 1]
SCREAMING_SNAKE_CASE_ : str = self.dp[i - 1][j]
SCREAMING_SNAKE_CASE_ : int = self.dp[i - 1][j - 1]
SCREAMING_SNAKE_CASE_ : int = 1 + min(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return self.dp[m][n]
if __name__ == "__main__":
lowerCAmelCase__: str = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
lowerCAmelCase__: Optional[Any] = input("Enter the first string: ").strip()
lowerCAmelCase__: Any = input("Enter the second string: ").strip()
print()
print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| 311
| 1
|
'''simple docstring'''
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __A ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any]=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = OmegaConf.load(_SCREAMING_SNAKE_CASE )
if display:
print(yaml.dump(OmegaConf.to_container(_SCREAMING_SNAKE_CASE ) ) )
return config
def __A ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict=None , _SCREAMING_SNAKE_CASE : List[str]=None ):
"""simple docstring"""
if conf_path is None:
__SCREAMING_SNAKE_CASE : int = "./model_checkpoints/vqgan_only.yaml"
__SCREAMING_SNAKE_CASE : Any = load_config(_SCREAMING_SNAKE_CASE , display=_SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : List[str] = VQModel(**config.model.params )
if ckpt_path is None:
__SCREAMING_SNAKE_CASE : Dict = "./model_checkpoints/vqgan_only.pt"
__SCREAMING_SNAKE_CASE : List[str] = torch.load(_SCREAMING_SNAKE_CASE , map_location=_SCREAMING_SNAKE_CASE )
if ".ckpt" in ckpt_path:
__SCREAMING_SNAKE_CASE : List[str] = sd["state_dict"]
model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
del sd
return model
def __A ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = model.encode(_SCREAMING_SNAKE_CASE )
print(f'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}' )
__SCREAMING_SNAKE_CASE : Any = model.decode(_SCREAMING_SNAKE_CASE )
return xrec
def __A ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[Any]=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = string.rsplit("." , 1 )
if reload:
__SCREAMING_SNAKE_CASE : Union[str, Any] = importlib.import_module(_SCREAMING_SNAKE_CASE )
importlib.reload(_SCREAMING_SNAKE_CASE )
return getattr(importlib.import_module(_SCREAMING_SNAKE_CASE , package=_SCREAMING_SNAKE_CASE ) , cls )
def __A ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if "target" not in config:
raise KeyError("Expected key `target` to instantiate." )
return get_obj_from_str(config["target"] )(**config.get("params" , {} ) )
def __A ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str]=True , _SCREAMING_SNAKE_CASE : Tuple=True ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = instantiate_from_config(_SCREAMING_SNAKE_CASE )
if sd is not None:
model.load_state_dict(_SCREAMING_SNAKE_CASE )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def __A ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
if ckpt:
__SCREAMING_SNAKE_CASE : List[Any] = torch.load(_SCREAMING_SNAKE_CASE , map_location="cpu" )
__SCREAMING_SNAKE_CASE : Dict = pl_sd["global_step"]
print(f'loaded model from global step {global_step}.' )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = {"state_dict": None}
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : int = load_model_from_config(config.model , pl_sd["state_dict"] , gpu=_SCREAMING_SNAKE_CASE , eval_mode=_SCREAMING_SNAKE_CASE )["model"]
return model, global_step
| 211
|
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ , ):
super().__init__()
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(
speech_model=a__ , speech_processor=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , unet=a__ , scheduler=a__ , feature_extractor=a__ , )
def a_ ( self , a__ = "auto" ):
if slice_size == "auto":
__SCREAMING_SNAKE_CASE : List[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(a__ )
def a_ ( self ):
self.enable_attention_slicing(a__ )
@torch.no_grad()
def __call__( self , a__ , a__=16000 , a__ = 512 , a__ = 512 , a__ = 50 , a__ = 7.5 , a__ = None , a__ = 1 , a__ = 0.0 , a__ = None , a__ = None , a__ = "pil" , a__ = True , a__ = None , a__ = 1 , **a__ , ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.speech_processor.feature_extractor(
a__ , return_tensors="pt" , sampling_rate=a__ ).input_features.to(self.device )
__SCREAMING_SNAKE_CASE : Optional[int] = self.speech_model.generate(a__ , max_length=480000 )
__SCREAMING_SNAKE_CASE : List[Any] = self.speech_processor.tokenizer.batch_decode(a__ , skip_special_tokens=a__ , normalize=a__ )[
0
]
if isinstance(a__ , a__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
elif isinstance(a__ , a__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = len(a__ )
else:
raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(a__ )}' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(a__ , a__ ) or callback_steps <= 0)
):
raise ValueError(
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
f' {type(a__ )}.' )
# get prompt text embeddings
__SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer(
a__ , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
__SCREAMING_SNAKE_CASE : Tuple = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__SCREAMING_SNAKE_CASE : Tuple = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
__SCREAMING_SNAKE_CASE : Tuple = text_input_ids[:, : self.tokenizer.model_max_length]
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = text_embeddings.shape
__SCREAMING_SNAKE_CASE : int = text_embeddings.repeat(1 , a__ , 1 )
__SCREAMING_SNAKE_CASE : Optional[int] = text_embeddings.view(bs_embed * num_images_per_prompt , a__ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
__SCREAMING_SNAKE_CASE : str = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE : List[str]
if negative_prompt is None:
__SCREAMING_SNAKE_CASE : Any = [""] * batch_size
elif type(a__ ) is not type(a__ ):
raise TypeError(
f'`negative_prompt` should be the same type to `prompt`, but got {type(a__ )} !='
f' {type(a__ )}.' )
elif isinstance(a__ , a__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = [negative_prompt]
elif batch_size != len(a__ ):
raise ValueError(
f'`negative_prompt`: {negative_prompt} has batch size {len(a__ )}, but `prompt`:'
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
" the batch size of `prompt`." )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = negative_prompt
__SCREAMING_SNAKE_CASE : Optional[int] = text_input_ids.shape[-1]
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer(
a__ , padding="max_length" , max_length=a__ , truncation=a__ , return_tensors="pt" , )
__SCREAMING_SNAKE_CASE : Dict = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__SCREAMING_SNAKE_CASE : Dict = uncond_embeddings.shape[1]
__SCREAMING_SNAKE_CASE : int = uncond_embeddings.repeat(1 , a__ , 1 )
__SCREAMING_SNAKE_CASE : Any = uncond_embeddings.view(batch_size * num_images_per_prompt , a__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__SCREAMING_SNAKE_CASE : Dict = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
__SCREAMING_SNAKE_CASE : Dict = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
__SCREAMING_SNAKE_CASE : int = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
__SCREAMING_SNAKE_CASE : Optional[int] = torch.randn(a__ , generator=a__ , device="cpu" , dtype=a__ ).to(
self.device )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.randn(a__ , generator=a__ , device=self.device , dtype=a__ )
else:
if latents.shape != latents_shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
__SCREAMING_SNAKE_CASE : Dict = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(a__ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
__SCREAMING_SNAKE_CASE : List[str] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__SCREAMING_SNAKE_CASE : Union[str, Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__SCREAMING_SNAKE_CASE : str = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if accepts_eta:
__SCREAMING_SNAKE_CASE : Dict = eta
for i, t in enumerate(self.progress_bar(a__ ) ):
# expand the latents if we are doing classifier free guidance
__SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler.scale_model_input(a__ , a__ )
# predict the noise residual
__SCREAMING_SNAKE_CASE : List[Any] = self.unet(a__ , a__ , encoder_hidden_states=a__ ).sample
# perform guidance
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = noise_pred.chunk(2 )
__SCREAMING_SNAKE_CASE : Tuple = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
__SCREAMING_SNAKE_CASE : Any = self.scheduler.step(a__ , a__ , a__ , **a__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(a__ , a__ , a__ )
__SCREAMING_SNAKE_CASE : Any = 1 / 0.18215 * latents
__SCREAMING_SNAKE_CASE : Optional[Any] = self.vae.decode(a__ ).sample
__SCREAMING_SNAKE_CASE : List[str] = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__SCREAMING_SNAKE_CASE : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__SCREAMING_SNAKE_CASE : int = self.numpy_to_pil(a__ )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=a__ , nsfw_content_detected=a__ )
| 211
| 1
|
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 712
|
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowercase = {
"""tiny.en""": """https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt""",
"""tiny""": """https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt""",
"""base.en""": """https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt""",
"""base""": """https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt""",
"""small.en""": """https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt""",
"""small""": """https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt""",
"""medium.en""": """https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt""",
"""medium""": """https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt""",
"""large""": """https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt""",
"""large-v2""": """https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt""",
}
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ : Dict =['''layers''', '''blocks''']
for k in ignore_keys:
state_dict.pop(UpperCAmelCase_ , UpperCAmelCase_ )
_lowercase = {
"""blocks""": """layers""",
"""mlp.0""": """fc1""",
"""mlp.2""": """fc2""",
"""mlp_ln""": """final_layer_norm""",
""".attn.query""": """.self_attn.q_proj""",
""".attn.key""": """.self_attn.k_proj""",
""".attn.value""": """.self_attn.v_proj""",
""".attn_ln""": """.self_attn_layer_norm""",
""".attn.out""": """.self_attn.out_proj""",
""".cross_attn.query""": """.encoder_attn.q_proj""",
""".cross_attn.key""": """.encoder_attn.k_proj""",
""".cross_attn.value""": """.encoder_attn.v_proj""",
""".cross_attn_ln""": """.encoder_attn_layer_norm""",
""".cross_attn.out""": """.encoder_attn.out_proj""",
"""decoder.ln.""": """decoder.layer_norm.""",
"""encoder.ln.""": """encoder.layer_norm.""",
"""token_embedding""": """embed_tokens""",
"""encoder.positional_embedding""": """encoder.embed_positions.weight""",
"""decoder.positional_embedding""": """decoder.embed_positions.weight""",
"""ln_post""": """layer_norm""",
}
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ : Optional[int] =list(s_dict.keys() )
for key in keys:
SCREAMING_SNAKE_CASE_ : Tuple =key
for k, v in WHISPER_MAPPING.items():
if k in key:
SCREAMING_SNAKE_CASE_ : int =new_key.replace(UpperCAmelCase_ , UpperCAmelCase_ )
print(f'{key} -> {new_key}' )
SCREAMING_SNAKE_CASE_ : Any =s_dict.pop(UpperCAmelCase_ )
return s_dict
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any =emb.weight.shape
SCREAMING_SNAKE_CASE_ : List[Any] =nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ , bias=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ : int =emb.weight.data
return lin_layer
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ) -> bytes:
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ : List[Any] =os.path.basename(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Any =url.split('''/''' )[-2]
SCREAMING_SNAKE_CASE_ : Union[str, Any] =os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
if os.path.exists(UpperCAmelCase_ ) and not os.path.isfile(UpperCAmelCase_ ):
raise RuntimeError(f'{download_target} exists and is not a regular file' )
if os.path.isfile(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE_ : Tuple =open(UpperCAmelCase_ , '''rb''' ).read()
if hashlib.shaaaa(UpperCAmelCase_ ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(f'{download_target} exists, but the SHA256 checksum does not match; re-downloading the file' )
with urllib.request.urlopen(UpperCAmelCase_ ) as source, open(UpperCAmelCase_ , '''wb''' ) as output:
with tqdm(
total=int(source.info().get('''Content-Length''' ) ) , ncols=8_0 , unit='''iB''' , unit_scale=UpperCAmelCase_ , unit_divisor=1_0_2_4 ) as loop:
while True:
SCREAMING_SNAKE_CASE_ : Dict =source.read(8_1_9_2 )
if not buffer:
break
output.write(UpperCAmelCase_ )
loop.update(len(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE_ : str =open(UpperCAmelCase_ , '''rb''' ).read()
if hashlib.shaaaa(UpperCAmelCase_ ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.''' )
return model_bytes
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Any , UpperCAmelCase_ : str ) -> Tuple:
if ".pt" not in checkpoint_path:
SCREAMING_SNAKE_CASE_ : int =_download(_MODELS[checkpoint_path] )
else:
SCREAMING_SNAKE_CASE_ : List[str] =torch.load(UpperCAmelCase_ , map_location='''cpu''' )
SCREAMING_SNAKE_CASE_ : int =original_checkpoint['''dims''']
SCREAMING_SNAKE_CASE_ : Any =original_checkpoint['''model_state_dict''']
SCREAMING_SNAKE_CASE_ : Any =state_dict['''decoder.token_embedding.weight''']
remove_ignore_keys_(UpperCAmelCase_ )
rename_keys(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =True
SCREAMING_SNAKE_CASE_ : int =state_dict['''decoder.layers.0.fc1.weight'''].shape[0]
SCREAMING_SNAKE_CASE_ : Union[str, Any] =WhisperConfig(
vocab_size=dimensions['''n_vocab'''] , encoder_ffn_dim=UpperCAmelCase_ , decoder_ffn_dim=UpperCAmelCase_ , num_mel_bins=dimensions['''n_mels'''] , d_model=dimensions['''n_audio_state'''] , max_target_positions=dimensions['''n_text_ctx'''] , encoder_layers=dimensions['''n_audio_layer'''] , encoder_attention_heads=dimensions['''n_audio_head'''] , decoder_layers=dimensions['''n_text_layer'''] , decoder_attention_heads=dimensions['''n_text_state'''] , max_source_positions=dimensions['''n_audio_ctx'''] , )
SCREAMING_SNAKE_CASE_ : List[str] =WhisperForConditionalGeneration(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] =model.model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0 and not set(UpperCAmelCase_ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f' but all the following weights are missing {missing}' )
if tie_embeds:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =make_linear_from_emb(model.model.decoder.embed_tokens )
else:
SCREAMING_SNAKE_CASE_ : Optional[int] =proj_out_weights
model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("""--checkpoint_path""", type=str, help="""Patht to the downloaded checkpoints""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
_lowercase = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 431
| 0
|
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__A ):
'''simple docstring'''
lowerCamelCase_ = ['''flax''', '''transformers''']
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
class UpperCAmelCase ( metaclass=__A ):
'''simple docstring'''
lowerCamelCase_ = ['''flax''', '''transformers''']
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
class UpperCAmelCase ( metaclass=__A ):
'''simple docstring'''
lowerCamelCase_ = ['''flax''', '''transformers''']
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
class UpperCAmelCase ( metaclass=__A ):
'''simple docstring'''
lowerCamelCase_ = ['''flax''', '''transformers''']
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['flax', 'transformers'] )
| 558
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""post_extract_proj""": """feature_projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def UpperCamelCase ( __lowercase : int ,__lowercase : List[str] ,__lowercase : str ,__lowercase : Optional[Any] ,__lowercase : Any ):
'''simple docstring'''
for attribute in key.split('.' ):
A_ : Dict = getattr(__lowercase ,__lowercase )
if weight_type is not None:
A_ : Any = getattr(__lowercase ,__lowercase ).shape
else:
A_ : Optional[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
A_ : int = value
elif weight_type == "weight_g":
A_ : Tuple = value
elif weight_type == "weight_v":
A_ : Union[str, Any] = value
elif weight_type == "bias":
A_ : Any = value
else:
A_ : str = value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def UpperCamelCase ( __lowercase : str ,__lowercase : Dict ,__lowercase : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = []
A_ : Tuple = fairseq_model.state_dict()
A_ : Any = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
A_ : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,hf_model.config.feat_extract_norm == 'group' ,)
A_ : List[str] = True
else:
for key, mapped_key in MAPPING.items():
A_ : str = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
A_ : int = True
if "*" in mapped_key:
A_ : str = name.split(__lowercase )[0].split('.' )[-2]
A_ : Optional[Any] = mapped_key.replace('*' ,__lowercase )
if "weight_g" in name:
A_ : Dict = 'weight_g'
elif "weight_v" in name:
A_ : Tuple = 'weight_v'
elif "weight" in name:
A_ : Union[str, Any] = 'weight'
elif "bias" in name:
A_ : Optional[Any] = 'bias'
else:
A_ : Union[str, Any] = None
set_recursively(__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase )
continue
if not is_used:
unused_weights.append(__lowercase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : Union[str, Any] ,__lowercase : Any ,__lowercase : List[Any] ,__lowercase : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[int] = full_name.split('conv_layers.' )[-1]
A_ : Any = name.split('.' )
A_ : Dict = int(items[0] )
A_ : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
A_ : Optional[int] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
A_ : Union[str, Any] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
A_ : Any = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
A_ : Tuple = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowercase )
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : str ):
'''simple docstring'''
A_ : Union[str, Any] = SEWConfig()
if is_finetuned:
A_ : Any = model.wav_encoder.wav_model.cfg
else:
A_ : int = model.cfg
A_ : Any = fs_config.conv_bias
A_ : Dict = eval(fs_config.conv_feature_layers )
A_ : List[Any] = [x[0] for x in conv_layers]
A_ : Optional[Any] = [x[1] for x in conv_layers]
A_ : List[Any] = [x[2] for x in conv_layers]
A_ : Optional[int] = 'gelu'
A_ : Union[str, Any] = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
A_ : Tuple = 0.0
A_ : Dict = fs_config.activation_fn.name
A_ : List[Any] = fs_config.encoder_embed_dim
A_ : int = 0.02
A_ : List[str] = fs_config.encoder_ffn_embed_dim
A_ : Any = 1e-5
A_ : Optional[Any] = fs_config.encoder_layerdrop
A_ : Optional[int] = fs_config.encoder_attention_heads
A_ : Any = fs_config.conv_pos_groups
A_ : int = fs_config.conv_pos
A_ : Tuple = len(__lowercase )
A_ : List[Any] = fs_config.encoder_layers
A_ : Any = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
A_ : Union[str, Any] = model.cfg
A_ : str = fs_config.final_dropout
A_ : Any = fs_config.layerdrop
A_ : str = fs_config.activation_dropout
A_ : Any = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
A_ : str = fs_config.attention_dropout
A_ : Any = fs_config.dropout_input
A_ : Dict = fs_config.dropout
A_ : Optional[Any] = fs_config.mask_channel_length
A_ : List[str] = fs_config.mask_channel_prob
A_ : Tuple = fs_config.mask_length
A_ : Dict = fs_config.mask_prob
A_ : Any = 'Wav2Vec2FeatureExtractor'
A_ : Union[str, Any] = 'Wav2Vec2CTCTokenizer'
return config
@torch.no_grad()
def UpperCamelCase ( __lowercase : List[Any] ,__lowercase : int ,__lowercase : Optional[int]=None ,__lowercase : Optional[Any]=None ,__lowercase : str=True ):
'''simple docstring'''
if is_finetuned:
A_ , A_ , A_ : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
A_ , A_ , A_ : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
A_ : Union[str, Any] = SEWConfig.from_pretrained(__lowercase )
else:
A_ : Dict = convert_config(model[0] ,__lowercase )
A_ : Union[str, Any] = model[0].eval()
A_ : Optional[int] = True if config.feat_extract_norm == 'layer' else False
A_ : List[Any] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=__lowercase ,return_attention_mask=__lowercase ,)
if is_finetuned:
if dict_path:
A_ : Optional[int] = Dictionary.load(__lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A_ : int = target_dict.pad_index
A_ : List[Any] = target_dict.bos_index
A_ : Optional[Any] = target_dict.pad_index
A_ : str = target_dict.bos_index
A_ : str = target_dict.eos_index
A_ : str = len(target_dict.symbols )
A_ : Union[str, Any] = os.path.join(__lowercase ,'vocab.json' )
if not os.path.isdir(__lowercase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowercase ) )
return
os.makedirs(__lowercase ,exist_ok=__lowercase )
with open(__lowercase ,'w' ,encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices ,__lowercase )
A_ : Any = WavaVecaCTCTokenizer(
__lowercase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token='|' ,do_lower_case=__lowercase ,)
A_ : Tuple = WavaVecaProcessor(feature_extractor=__lowercase ,tokenizer=__lowercase )
processor.save_pretrained(__lowercase )
A_ : Dict = SEWForCTC(__lowercase )
else:
A_ : Tuple = SEWModel(__lowercase )
feature_extractor.save_pretrained(__lowercase )
recursively_load_weights(__lowercase ,__lowercase ,__lowercase )
hf_model.save_pretrained(__lowercase )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_UpperCAmelCase = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 558
| 1
|
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
UpperCamelCase = '''sshleifer/mar_enro_6_3_student'''
class lowerCamelCase__ ( UpperCAmelCase ):
def UpperCAmelCase_ (self : Any ) -> int:
"""simple docstring"""
super().setUp()
lowerCamelCase_ : Union[str, Any] = cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' , extract_compressed_file=_snake_case , )
lowerCamelCase_ : Dict = f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
@slow
@require_torch_gpu
def UpperCAmelCase_ (self : Tuple ) -> Any:
"""simple docstring"""
MarianMTModel.from_pretrained(_snake_case )
@slow
@require_torch_gpu
def UpperCAmelCase_ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ : int = {
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
lowerCamelCase_ : Union[str, Any] = (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
lowerCamelCase_ : Union[str, Any] = bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
for k, v in env_vars_to_replace.items():
lowerCamelCase_ : Any = bash_script.replace(_snake_case , str(_snake_case ) )
lowerCamelCase_ : Optional[int] = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
lowerCamelCase_ : Optional[int] = f'\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n '.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
lowerCamelCase_ : List[str] = ['finetune.py'] + bash_script.split() + args
with patch.object(_snake_case , 'argv' , _snake_case ):
lowerCamelCase_ : Dict = argparse.ArgumentParser()
lowerCamelCase_ : str = pl.Trainer.add_argparse_args(_snake_case )
lowerCamelCase_ : Optional[Any] = SummarizationModule.add_model_specific_args(_snake_case , os.getcwd() )
lowerCamelCase_ : Any = parser.parse_args()
lowerCamelCase_ : Any = main(_snake_case )
# Check metrics
lowerCamelCase_ : Union[str, Any] = load_json(model.metrics_save_path )
lowerCamelCase_ : Optional[int] = metrics['val'][0]
lowerCamelCase_ : str = metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] , _snake_case )
self.assertGreater(last_step_stats['val_avg_gen_time'] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
lowerCamelCase_ : int = os.listdir(_snake_case )
lowerCamelCase_ : Optional[int] = [x for x in contents if x.endswith('.ckpt' )][0]
lowerCamelCase_ : str = os.path.join(args.output_dir , _snake_case )
lowerCamelCase_ : Dict = torch.load(_snake_case , map_location='cpu' )
lowerCamelCase_ : Optional[int] = 'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowerCamelCase_ : Any = {os.path.basename(_snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class lowerCamelCase__ ( UpperCAmelCase ):
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCAmelCase_ (self : int ) -> str:
"""simple docstring"""
lowerCamelCase_ : Dict = f'{self.test_file_dir_str}/test_data/wmt_en_ro'
lowerCamelCase_ : Dict = {
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
lowerCamelCase_ : Dict = (
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
lowerCamelCase_ : Union[str, Any] = bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
lowerCamelCase_ : Union[str, Any] = bash_script.replace('--fp16 ' , ' ' )
for k, v in env_vars_to_replace.items():
lowerCamelCase_ : Optional[int] = bash_script.replace(_snake_case , str(_snake_case ) )
lowerCamelCase_ : List[Any] = self.get_auto_remove_tmp_dir()
lowerCamelCase_ : Optional[int] = bash_script.replace('--fp16' , '' )
lowerCamelCase_ : int = 6
lowerCamelCase_ : List[Any] = (
['distillation.py']
+ bash_script.split()
+ [
f'--output_dir={output_dir}',
'--gpus=1',
'--learning_rate=1e-3',
f'--num_train_epochs={epochs}',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(_snake_case , 'argv' , _snake_case ):
lowerCamelCase_ : int = argparse.ArgumentParser()
lowerCamelCase_ : Tuple = pl.Trainer.add_argparse_args(_snake_case )
lowerCamelCase_ : Any = SummarizationDistiller.add_model_specific_args(_snake_case , os.getcwd() )
lowerCamelCase_ : Union[str, Any] = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
lowerCamelCase_ : Dict = distill_main(_snake_case )
# Check metrics
lowerCamelCase_ : List[str] = load_json(model.metrics_save_path )
lowerCamelCase_ : Any = metrics['val'][0]
lowerCamelCase_ : Any = metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] , _snake_case )
# check lightning ckpt can be loaded and has a reasonable statedict
lowerCamelCase_ : List[Any] = os.listdir(_snake_case )
lowerCamelCase_ : int = [x for x in contents if x.endswith('.ckpt' )][0]
lowerCamelCase_ : List[Any] = os.path.join(args.output_dir , _snake_case )
lowerCamelCase_ : Optional[Any] = torch.load(_snake_case , map_location='cpu' )
lowerCamelCase_ : Optional[int] = 'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowerCamelCase_ : List[str] = {os.path.basename(_snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 144
|
def _a ( lowerCamelCase__ ) -> int:
lowerCamelCase_ : List[Any] = []
lowerCamelCase_ : int = set({'(', '[', '{'} )
lowerCamelCase_ : Optional[Any] = set({')', ']', '}'} )
lowerCamelCase_ : Dict = {'{': '}', '[': ']', '(': ')'}
for i in range(len(lowerCamelCase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowerCamelCase__ ) == 0 or (len(lowerCamelCase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowerCamelCase__ ) == 0
def _a ( ) -> str:
lowerCamelCase_ : Dict = input('Enter sequence of brackets: ' )
if is_balanced(lowerCamelCase__ ):
print(lowerCamelCase__ , 'is balanced' )
else:
print(lowerCamelCase__ , 'is not balanced' )
if __name__ == "__main__":
main()
| 144
| 1
|
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : List[str] = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class _snake_case ( UpperCAmelCase_ ):
__lowerCAmelCase : Optional[int] = 'data2vec-audio'
def __init__( self , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=7_68 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=30_72 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0_2 , SCREAMING_SNAKE_CASE_=1E-5 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , SCREAMING_SNAKE_CASE_=(5, 2, 2, 2, 2, 2, 2) , SCREAMING_SNAKE_CASE_=(10, 3, 3, 3, 3, 2, 2) , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=19 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=0.0_5 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_="sum" , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2_56 , SCREAMING_SNAKE_CASE_=(5_12, 5_12, 5_12, 5_12, 15_00) , SCREAMING_SNAKE_CASE_=(5, 3, 3, 1, 1) , SCREAMING_SNAKE_CASE_=(1, 2, 3, 1, 1) , SCREAMING_SNAKE_CASE_=5_12 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_)
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : str = feat_extract_activation
lowercase__ : str = list(SCREAMING_SNAKE_CASE_)
lowercase__ : Optional[int] = list(SCREAMING_SNAKE_CASE_)
lowercase__ : List[str] = list(SCREAMING_SNAKE_CASE_)
lowercase__ : str = conv_bias
lowercase__ : Dict = num_conv_pos_embeddings
lowercase__ : int = num_conv_pos_embedding_groups
lowercase__ : Optional[int] = conv_pos_kernel_size
lowercase__ : Optional[Any] = len(self.conv_dim)
lowercase__ : List[Any] = num_hidden_layers
lowercase__ : List[str] = intermediate_size
lowercase__ : Tuple = hidden_act
lowercase__ : Tuple = num_attention_heads
lowercase__ : Union[str, Any] = hidden_dropout
lowercase__ : str = attention_dropout
lowercase__ : int = activation_dropout
lowercase__ : Union[str, Any] = feat_proj_dropout
lowercase__ : Any = final_dropout
lowercase__ : str = layerdrop
lowercase__ : Any = layer_norm_eps
lowercase__ : List[Any] = initializer_range
lowercase__ : Optional[int] = vocab_size
lowercase__ : Any = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
f' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel)}`.')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase__ : Any = mask_time_prob
lowercase__ : int = mask_time_length
lowercase__ : Union[str, Any] = mask_time_min_masks
lowercase__ : Optional[int] = mask_feature_prob
lowercase__ : Optional[int] = mask_feature_length
lowercase__ : Dict = mask_feature_min_masks
# ctc loss
lowercase__ : List[str] = ctc_loss_reduction
lowercase__ : str = ctc_zero_infinity
# adapter
lowercase__ : Union[str, Any] = add_adapter
lowercase__ : Tuple = adapter_kernel_size
lowercase__ : str = adapter_stride
lowercase__ : Any = num_adapter_layers
lowercase__ : str = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase__ : int = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase__ : Optional[int] = list(SCREAMING_SNAKE_CASE_)
lowercase__ : Any = list(SCREAMING_SNAKE_CASE_)
lowercase__ : List[str] = list(SCREAMING_SNAKE_CASE_)
lowercase__ : Any = xvector_output_dim
@property
def lowercase__ ( self):
'''simple docstring'''
return math.prod(self.conv_stride)
| 12
|
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = '''detr'''
__SCREAMING_SNAKE_CASE = ['''past_key_values''']
__SCREAMING_SNAKE_CASE = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=3 , lowerCamelCase=1_00 , lowerCamelCase=6 , lowerCamelCase=20_48 , lowerCamelCase=8 , lowerCamelCase=6 , lowerCamelCase=20_48 , lowerCamelCase=8 , lowerCamelCase=0.0 , lowerCamelCase=0.0 , lowerCamelCase=True , lowerCamelCase="relu" , lowerCamelCase=2_56 , lowerCamelCase=0.1 , lowerCamelCase=0.0 , lowerCamelCase=0.0 , lowerCamelCase=0.02 , lowerCamelCase=1.0 , lowerCamelCase=False , lowerCamelCase="sine" , lowerCamelCase="resnet50" , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=1 , lowerCamelCase=5 , lowerCamelCase=2 , lowerCamelCase=1 , lowerCamelCase=1 , lowerCamelCase=5 , lowerCamelCase=2 , lowerCamelCase=0.1 , **lowerCamelCase , ) -> int:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCamelCase : List[str] = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCamelCase : Dict = backbone_config.get("model_type" )
UpperCamelCase : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase : Dict = config_class.from_dict(lowerCamelCase )
# set timm attributes to None
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = None, None, None
UpperCamelCase : str = use_timm_backbone
UpperCamelCase : int = backbone_config
UpperCamelCase : List[Any] = num_channels
UpperCamelCase : int = num_queries
UpperCamelCase : List[Any] = d_model
UpperCamelCase : Union[str, Any] = encoder_ffn_dim
UpperCamelCase : Optional[int] = encoder_layers
UpperCamelCase : Tuple = encoder_attention_heads
UpperCamelCase : Tuple = decoder_ffn_dim
UpperCamelCase : int = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : Optional[int] = dropout
UpperCamelCase : List[Any] = attention_dropout
UpperCamelCase : Tuple = activation_dropout
UpperCamelCase : Any = activation_function
UpperCamelCase : List[Any] = init_std
UpperCamelCase : List[Any] = init_xavier_std
UpperCamelCase : List[str] = encoder_layerdrop
UpperCamelCase : Optional[int] = decoder_layerdrop
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = auxiliary_loss
UpperCamelCase : Dict = position_embedding_type
UpperCamelCase : Union[str, Any] = backbone
UpperCamelCase : Tuple = use_pretrained_backbone
UpperCamelCase : Any = dilation
# Hungarian matcher
UpperCamelCase : List[str] = class_cost
UpperCamelCase : Optional[Any] = bbox_cost
UpperCamelCase : Optional[Any] = giou_cost
# Loss coefficients
UpperCamelCase : Optional[int] = mask_loss_coefficient
UpperCamelCase : Dict = dice_loss_coefficient
UpperCamelCase : Tuple = bbox_loss_coefficient
UpperCamelCase : Tuple = giou_loss_coefficient
UpperCamelCase : Optional[int] = eos_coefficient
super().__init__(is_encoder_decoder=lowerCamelCase , **lowerCamelCase )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
return self.d_model
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCamelCase , **lowerCamelCase ) -> Any:
'''simple docstring'''
return cls(backbone_config=lowerCamelCase , **lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict[str, any]:
'''simple docstring'''
UpperCamelCase : Tuple = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCamelCase : Tuple = self.backbone_config.to_dict()
UpperCamelCase : Dict = self.__class__.model_type
return output
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = version.parse('''1.11''' )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> float:
'''simple docstring'''
return 1e-5
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
return 12
| 173
| 0
|
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase__:
'''simple docstring'''
@staticmethod
def UpperCAmelCase ( *lowerCAmelCase : Tuple , **lowerCAmelCase : Dict) -> int:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
class UpperCAmelCase__( unittest.TestCase ):
'''simple docstring'''
@require_torch
def UpperCAmelCase ( self : Dict) -> Optional[Any]:
"""simple docstring"""
lowercase__ = pipeline(
model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' , )
lowercase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
lowercase__ = image_classifier(lowerCAmelCase , candidate_labels=['a', 'b', 'c'])
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(lowerCAmelCase) , [
[{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}],
[{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'c'}, {'score': 0.3_33, 'label': 'b'}],
] , )
lowercase__ = image_classifier([image] * 5 , candidate_labels=['A', 'B', 'C'] , batch_size=2)
self.assertEqual(
nested_simplify(lowerCAmelCase) , [
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
] , )
@require_tf
def UpperCAmelCase ( self : Optional[Any]) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = pipeline(
model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' , framework='tf')
lowercase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
lowercase__ = image_classifier(lowerCAmelCase , candidate_labels=['a', 'b', 'c'])
self.assertEqual(
nested_simplify(lowerCAmelCase) , [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}] , )
lowercase__ = image_classifier([image] * 5 , candidate_labels=['A', 'B', 'C'] , batch_size=2)
self.assertEqual(
nested_simplify(lowerCAmelCase) , [
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
[
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
{'score': 0.3_33, 'label': ANY(lowerCAmelCase)},
],
] , )
@slow
@require_torch
def UpperCAmelCase ( self : int) -> int:
"""simple docstring"""
lowercase__ = pipeline(
task='zero-shot-image-classification' , model='openai/clip-vit-base-patch32' , )
# This is an image of 2 cats with remotes and no planes
lowercase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
lowercase__ = image_classifier(lowerCAmelCase , candidate_labels=['cat', 'plane', 'remote'])
self.assertEqual(
nested_simplify(lowerCAmelCase) , [
{'score': 0.5_11, 'label': 'remote'},
{'score': 0.4_85, 'label': 'cat'},
{'score': 0.0_04, 'label': 'plane'},
] , )
lowercase__ = image_classifier([image] * 5 , candidate_labels=['cat', 'plane', 'remote'] , batch_size=2)
self.assertEqual(
nested_simplify(lowerCAmelCase) , [
[
{'score': 0.5_11, 'label': 'remote'},
{'score': 0.4_85, 'label': 'cat'},
{'score': 0.0_04, 'label': 'plane'},
],
]
* 5 , )
@slow
@require_tf
def UpperCAmelCase ( self : int) -> Dict:
"""simple docstring"""
lowercase__ = pipeline(
task='zero-shot-image-classification' , model='openai/clip-vit-base-patch32' , framework='tf')
# This is an image of 2 cats with remotes and no planes
lowercase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
lowercase__ = image_classifier(lowerCAmelCase , candidate_labels=['cat', 'plane', 'remote'])
self.assertEqual(
nested_simplify(lowerCAmelCase) , [
{'score': 0.5_11, 'label': 'remote'},
{'score': 0.4_85, 'label': 'cat'},
{'score': 0.0_04, 'label': 'plane'},
] , )
lowercase__ = image_classifier([image] * 5 , candidate_labels=['cat', 'plane', 'remote'] , batch_size=2)
self.assertEqual(
nested_simplify(lowerCAmelCase) , [
[
{'score': 0.5_11, 'label': 'remote'},
{'score': 0.4_85, 'label': 'cat'},
{'score': 0.0_04, 'label': 'plane'},
],
]
* 5 , )
| 642
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : int = "speech_to_text"
A : Optional[Any] = ["past_key_values"]
A : Optional[int] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Optional[int] , lowerCAmelCase : Tuple=1_00_00 , lowerCAmelCase : int=12 , lowerCAmelCase : int=20_48 , lowerCAmelCase : Union[str, Any]=4 , lowerCAmelCase : str=6 , lowerCAmelCase : Dict=20_48 , lowerCAmelCase : Dict=4 , lowerCAmelCase : Optional[int]=0.0 , lowerCAmelCase : Union[str, Any]=0.0 , lowerCAmelCase : int=True , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Dict="relu" , lowerCAmelCase : Tuple=2_56 , lowerCAmelCase : Dict=0.1 , lowerCAmelCase : Optional[Any]=0.0 , lowerCAmelCase : List[Any]=0.0 , lowerCAmelCase : Any=0.02 , lowerCAmelCase : List[Any]=2 , lowerCAmelCase : Tuple=True , lowerCAmelCase : Tuple=1 , lowerCAmelCase : List[str]=0 , lowerCAmelCase : Union[str, Any]=2 , lowerCAmelCase : Any=60_00 , lowerCAmelCase : Optional[int]=10_24 , lowerCAmelCase : Optional[Any]=2 , lowerCAmelCase : Optional[Any]=(5, 5) , lowerCAmelCase : Union[str, Any]=10_24 , lowerCAmelCase : List[Any]=80 , lowerCAmelCase : List[str]=1 , **lowerCAmelCase : List[str] , ) -> Dict:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = d_model
lowercase__ = encoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = encoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = use_cache
lowercase__ = encoder_layers
lowercase__ = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase__ = max_source_positions
lowercase__ = max_target_positions
lowercase__ = num_conv_layers
lowercase__ = list(lowerCAmelCase)
lowercase__ = conv_channels
lowercase__ = input_feat_per_channel
lowercase__ = input_channels
if len(self.conv_kernel_sizes) != self.num_conv_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '''
f'''`config.num_conv_layers = {self.num_conv_layers}`.''')
super().__init__(
pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , is_encoder_decoder=lowerCAmelCase , decoder_start_token_id=lowerCAmelCase , **lowerCAmelCase , )
| 642
| 1
|
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