
'''simple docstring'''





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowercase						:      Any									= 'src/transformers'
lowercase						:      Optional[Any]									= 'docs/source/en'
lowercase						:      List[str]									= '.'




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    List[Any])      ->					Tuple:






							'''simple docstring'''





							with open(_lowerCamelCase		,						"r"		,						encoding="utf-8"		,						newline="\n") as f:
														__UpperCamelCase			:     Union[str, Any]         =      f.readlines()
							# Find the start prompt.
							__UpperCamelCase			:     Union[str, Any]         =      0
							while not lines[start_index].startswith(_lowerCamelCase):
														start_index += 1
							start_index += 1

							__UpperCamelCase			:     Optional[Any]         =      start_index
							while not lines[end_index].startswith(_lowerCamelCase):
														end_index += 1
							end_index -= 1

							while len(lines[start_index]) <= 1:
														start_index += 1
							while len(lines[end_index]) <= 1:
														end_index -= 1
							end_index += 1
							return "".join(lines[start_index:end_index]), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowercase						:      str									= 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowercase						:      int									= re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowercase						:      List[Any]									= re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase						:      Optional[Any]									= re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
lowercase						:      int									= direct_transformers_import(TRANSFORMERS_PATH)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,						_lowerCamelCase)
							return [m.group(0) for m in matches]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Dict)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      2 if text == "✅" or text == "❌" else len(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      (width - text_length) // 2
							__UpperCamelCase			:     Any         =      width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent




def 			_SCREAMING_SNAKE_CASE      (	)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							__UpperCamelCase			:     Optional[Any]         =      {
							    name: config_maping_names[code]
							    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_maping_names
							}
							__UpperCamelCase			:     Optional[Any]         =      {name: config.replace("Config"		,						"") for name, config in model_name_to_config.items()}

							# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
							__UpperCamelCase			:     Optional[int]         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     Dict         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     Tuple         =      collections.defaultdict(_lowerCamelCase)

							# Let's lookup through all transformers object (once).
							for attr_name in dir(_lowerCamelCase):
														__UpperCamelCase			:     Any         =      None
														if attr_name.endswith("Tokenizer"):
																					__UpperCamelCase			:     List[str]         =      slow_tokenizers
																					__UpperCamelCase			:     Union[str, Any]         =      attr_name[:-9]
														elif attr_name.endswith("TokenizerFast"):
																					__UpperCamelCase			:     Any         =      fast_tokenizers
																					__UpperCamelCase			:     Optional[Any]         =      attr_name[:-13]
														elif _re_tf_models.match(_lowerCamelCase) is not None:
																					__UpperCamelCase			:     List[Any]         =      tf_models
																					__UpperCamelCase			:     Tuple         =      _re_tf_models.match(_lowerCamelCase).groups()[0]
														elif _re_flax_models.match(_lowerCamelCase) is not None:
																					__UpperCamelCase			:     int         =      flax_models
																					__UpperCamelCase			:     List[str]         =      _re_flax_models.match(_lowerCamelCase).groups()[0]
														elif _re_pt_models.match(_lowerCamelCase) is not None:
																					__UpperCamelCase			:     List[Any]         =      pt_models
																					__UpperCamelCase			:     Any         =      _re_pt_models.match(_lowerCamelCase).groups()[0]

														if lookup_dict is not None:
																					while len(_lowerCamelCase) > 0:
																												if attr_name in model_name_to_prefix.values():
																																			__UpperCamelCase			:     str         =      True
																																			break
																												# Try again after removing the last word in the name
																												__UpperCamelCase			:     Dict         =      "".join(camel_case_split(_lowerCamelCase)[:-1])

    # Let's build that table!
							__UpperCamelCase			:     str         =      list(model_name_to_config.keys())
							model_names.sort(key=str.lower)
							__UpperCamelCase			:     Union[str, Any]         =      ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
							# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
							__UpperCamelCase			:     Any         =      [len(_lowerCamelCase) + 2 for c in columns]
							__UpperCamelCase			:     List[str]         =      max([len(_lowerCamelCase) for name in model_names]) + 2

							# Build the table per se
							__UpperCamelCase			:     Union[str, Any]         =      "|" + "|".join([_center_text(_lowerCamelCase		,						_lowerCamelCase) for c, w in zip(_lowerCamelCase		,						_lowerCamelCase)]) + "|\n"
							# Use ":-----:" format to center-aligned table cell texts
							table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n"

							__UpperCamelCase			:     List[Any]         =      {True: "✅", False: "❌"}
							for name in model_names:
														__UpperCamelCase			:     int         =      model_name_to_prefix[name]
														__UpperCamelCase			:     Any         =      [
														    name,
														    check[slow_tokenizers[prefix]],
														    check[fast_tokenizers[prefix]],
														    check[pt_models[prefix]],
														    check[tf_models[prefix]],
														    check[flax_models[prefix]],
														]
														table += "|" + "|".join([_center_text(_lowerCamelCase		,						_lowerCamelCase) for l, w in zip(_lowerCamelCase		,						_lowerCamelCase)]) + "|\n"
							return table




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int=False)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      _find_text_in_file(
							    filename=os.path.join(_lowerCamelCase		,						"index.md")		,						start_prompt="<!--This table is updated automatically from the auto modules"		,						end_prompt="<!-- End table-->"		,						)
							__UpperCamelCase			:     Optional[int]         =      get_model_table_from_auto_modules()

							if current_table != new_table:
														if overwrite:
																					with open(os.path.join(_lowerCamelCase		,						"index.md")		,						"w"		,						encoding="utf-8"		,						newline="\n") as f:
																												f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
														else:
																					raise ValueError(
																					    "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.")


if __name__ == "__main__":
		lowercase						:      Union[str, Any]									= argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		lowercase						:      Tuple									= parser.parse_args()

		check_model_table(args.fix_and_overwrite)

import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

lowercase						:      List[str]									= collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowercase						:      List[Any]									= 'https://storage.googleapis.com/cvdf-datasets/mnist/'




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      numpy.dtype(numpy.uintaa).newbyteorder(">")
							return numpy.frombuffer(bytestream.read(4)		,						dtype=_lowerCamelCase)[0]




@deprecated(_lowerCamelCase		,						"Please use tf.data to implement this functionality.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Any:






							'''simple docstring'''





							print("Extracting"		,						f.name)
							with gzip.GzipFile(fileobj=_lowerCamelCase) as bytestream:
														__UpperCamelCase			:     str         =      _readaa(_lowerCamelCase)
														if magic != 2_051:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name))
														__UpperCamelCase			:     List[str]         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Optional[int]         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      bytestream.read(rows * cols * num_images)
														__UpperCamelCase			:     Optional[int]         =      numpy.frombuffer(_lowerCamelCase		,						dtype=numpy.uinta)
														__UpperCamelCase			:     Dict         =      data.reshape(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						1)
														return data




@deprecated(_lowerCamelCase		,						"Please use tf.one_hot on tensors.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[str])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      labels_dense.shape[0]
							__UpperCamelCase			:     str         =      numpy.arange(_lowerCamelCase) * num_classes
							__UpperCamelCase			:     str         =      numpy.zeros((num_labels, num_classes))
							__UpperCamelCase			:     Tuple         =      1
							return labels_one_hot




@deprecated(_lowerCamelCase		,						"Please use tf.data to implement this functionality.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Optional[int]=False		,						_lowerCamelCase				:    Optional[int]=10)      ->					Dict:






							'''simple docstring'''





							print("Extracting"		,						f.name)
							with gzip.GzipFile(fileobj=_lowerCamelCase) as bytestream:
														__UpperCamelCase			:     int         =      _readaa(_lowerCamelCase)
														if magic != 2_049:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name))
														__UpperCamelCase			:     Any         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     List[Any]         =      bytestream.read(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      numpy.frombuffer(_lowerCamelCase		,						dtype=numpy.uinta)
														if one_hot:
																					return _dense_to_one_hot(_lowerCamelCase		,						_lowerCamelCase)
														return labels




class 				lowerCamelCase__							:




	'''simple docstring'''




	@deprecated(
	    a  ,     "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."  ,     )
	def __init__(      self	:int  ,     a	:Any  ,     a	:List[str]  ,     a	:Union[str, Any]=False  ,     a	:List[Any]=False  ,     a	:Dict=dtypes.floataa  ,     a	:int=True  ,     a	:Optional[int]=None  ,     )	->				List[str]:
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      random_seed.get_seed(a		)
								# If op level seed is not set, use whatever graph level seed is returned
								numpy.random.seed(seeda if seed is None else seeda		)
								__UpperCamelCase			:     Optional[Any]         =      dtypes.as_dtype(a		).base_dtype
								if dtype not in (dtypes.uinta, dtypes.floataa):
															raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype		)
								if fake_data:
															__UpperCamelCase			:     str         =      1_0_0_0_0
															__UpperCamelCase			:     Optional[Any]         =      one_hot
								else:
															assert (
															    images.shape[0] == labels.shape[0]
															), f'images.shape: {images.shape} labels.shape: {labels.shape}'
															__UpperCamelCase			:     Any         =      images.shape[0]

															# Convert shape from [num examples, rows, columns, depth]
															# to [num examples, rows*columns] (assuming depth == 1)
															if reshape:
																						assert images.shape[3] == 1
																						__UpperCamelCase			:     Union[str, Any]         =      images.reshape(
																						    images.shape[0]  ,     images.shape[1] * images.shape[2]		)
															if dtype == dtypes.floataa:
																						# Convert from [0, 255] -> [0.0, 1.0].
																						__UpperCamelCase			:     List[Any]         =      images.astype(numpy.floataa		)
																						__UpperCamelCase			:     Optional[Any]         =      numpy.multiply(a  ,     1.0 / 255.0		)
								__UpperCamelCase			:     Optional[Any]         =      images
								__UpperCamelCase			:     List[Any]         =      labels
								__UpperCamelCase			:     str         =      0
								__UpperCamelCase			:     Union[str, Any]         =      0







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								return self._images







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Dict:
								return self._labels







	@property
	def      _lowerCamelCase       (      self	:List[str]		)	->				str:
								return self._num_examples







	@property
	def      _lowerCamelCase       (      self	:Tuple		)	->				Dict:
								return self._epochs_completed







	def      _lowerCamelCase       (      self	:Any  ,     a	:Optional[int]  ,     a	:Optional[int]=False  ,     a	:int=True		)	->				Optional[int]:
								if fake_data:
															__UpperCamelCase			:     Any         =      [1] * 7_8_4
															__UpperCamelCase			:     Union[str, Any]         =      [1] + [0] * 9 if self.one_hot else 0
															return (
															    [fake_image for _ in range(a		)],
															    [fake_label for _ in range(a		)],
															)
								__UpperCamelCase			:     str         =      self._index_in_epoch
								# Shuffle for the first epoch
								if self._epochs_completed == 0 and start == 0 and shuffle:
															__UpperCamelCase			:     Any         =      numpy.arange(self._num_examples		)
															numpy.random.shuffle(a		)
															__UpperCamelCase			:     int         =      self.images[perma]
															__UpperCamelCase			:     str         =      self.labels[perma]
								# Go to the next epoch
								if start + batch_size > self._num_examples:
															# Finished epoch
															self._epochs_completed += 1
															# Get the rest examples in this epoch
															__UpperCamelCase			:     Optional[int]         =      self._num_examples - start
															__UpperCamelCase			:     Optional[int]         =      self._images[start : self._num_examples]
															__UpperCamelCase			:     int         =      self._labels[start : self._num_examples]
															# Shuffle the data
															if shuffle:
																						__UpperCamelCase			:     Optional[int]         =      numpy.arange(self._num_examples		)
																						numpy.random.shuffle(a		)
																						__UpperCamelCase			:     Optional[Any]         =      self.images[perm]
																						__UpperCamelCase			:     Tuple         =      self.labels[perm]
															# Start next epoch
															__UpperCamelCase			:     Tuple         =      0
															__UpperCamelCase			:     Union[str, Any]         =      batch_size - rest_num_examples
															__UpperCamelCase			:     List[str]         =      self._index_in_epoch
															__UpperCamelCase			:     Dict         =      self._images[start:end]
															__UpperCamelCase			:     str         =      self._labels[start:end]
															return (
															    numpy.concatenate((images_rest_part, images_new_part)  ,     axis=0		),
															    numpy.concatenate((labels_rest_part, labels_new_part)  ,     axis=0		),
															)
								else:
															self._index_in_epoch += batch_size
															__UpperCamelCase			:     Union[str, Any]         =      self._index_in_epoch
															return self._images[start:end], self._labels[start:end]




@deprecated(_lowerCamelCase		,						"Please write your own downloading logic.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Union[str, Any])      ->					Tuple:






							'''simple docstring'''





							if not gfile.Exists(_lowerCamelCase):
														gfile.MakeDirs(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      os.path.join(_lowerCamelCase		,						_lowerCamelCase)
							if not gfile.Exists(_lowerCamelCase):
														urllib.request.urlretrieve(_lowerCamelCase		,						_lowerCamelCase)  # noqa: S310
														with gfile.GFile(_lowerCamelCase) as f:
																					__UpperCamelCase			:     Any         =      f.size()
														print("Successfully downloaded"		,						_lowerCamelCase		,						_lowerCamelCase		,						"bytes.")
							return filepath




@deprecated(
    _lowerCamelCase		,						"Please use alternatives such as:" " tensorflow_datasets.load('mnist')")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[Any]=False		,						_lowerCamelCase				:    str=False		,						_lowerCamelCase				:    List[str]=dtypes.floataa		,						_lowerCamelCase				:    Any=True		,						_lowerCamelCase				:    Union[str, Any]=5_000		,						_lowerCamelCase				:    str=None		,						_lowerCamelCase				:    Optional[int]=DEFAULT_SOURCE_URL		,						)      ->					List[Any]:






							'''simple docstring'''





							if fake_data:

														def fake():
																					return _DataSet(
																					    []		,						[]		,						fake_data=_lowerCamelCase		,						one_hot=_lowerCamelCase		,						dtype=_lowerCamelCase		,						seed=_lowerCamelCase)

														__UpperCamelCase			:     Optional[int]         =      fake()
														__UpperCamelCase			:     Tuple         =      fake()
														__UpperCamelCase			:     List[str]         =      fake()
														return _Datasets(train=_lowerCamelCase		,						validation=_lowerCamelCase		,						test=_lowerCamelCase)

							if not source_url:  # empty string check
														__UpperCamelCase			:     str         =      DEFAULT_SOURCE_URL

							__UpperCamelCase			:     Optional[int]         =      "train-images-idx3-ubyte.gz"
							__UpperCamelCase			:     Dict         =      "train-labels-idx1-ubyte.gz"
							__UpperCamelCase			:     List[str]         =      "t10k-images-idx3-ubyte.gz"
							__UpperCamelCase			:     List[str]         =      "t10k-labels-idx1-ubyte.gz"

							__UpperCamelCase			:     Optional[int]         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + train_images_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     int         =      _extract_images(_lowerCamelCase)

							__UpperCamelCase			:     Optional[Any]         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + train_labels_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     int         =      _extract_labels(_lowerCamelCase		,						one_hot=_lowerCamelCase)

							__UpperCamelCase			:     int         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + test_images_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     Optional[int]         =      _extract_images(_lowerCamelCase)

							__UpperCamelCase			:     str         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + test_labels_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     List[str]         =      _extract_labels(_lowerCamelCase		,						one_hot=_lowerCamelCase)

							if not 0 <= validation_size <= len(_lowerCamelCase):
														__UpperCamelCase			:     str         =      (
														    "Validation size should be between 0 and "
														    F'{len(_lowerCamelCase)}. Received: {validation_size}.'
														)
														raise ValueError(_lowerCamelCase)

							__UpperCamelCase			:     Any         =      train_images[:validation_size]
							__UpperCamelCase			:     Optional[Any]         =      train_labels[:validation_size]
							__UpperCamelCase			:     Optional[int]         =      train_images[validation_size:]
							__UpperCamelCase			:     Tuple         =      train_labels[validation_size:]

							__UpperCamelCase			:     List[str]         =      {"dtype": dtype, "reshape": reshape, "seed": seed}

							__UpperCamelCase			:     Union[str, Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     str         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)

							return _Datasets(train=_lowerCamelCase		,						validation=_lowerCamelCase		,						test=_lowerCamelCase)