

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
lowerCAmelCase__								=		"""src/transformers"""
lowerCAmelCase__								=		"""docs/source/en"""
lowerCAmelCase__								=		"""."""






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Union[str, Any]	)						->				Dict:




	'''simple docstring'''




	with open(UpperCAmelCase_							,       'r'							,       encoding='utf-8'							,       newline='\n'	) as f:
		_UpperCamelCase   :		Optional[Any]			=						f.readlines()
	# Find the start prompt.
	_UpperCamelCase   :		Dict			=						0
	while not lines[start_index].startswith(UpperCAmelCase_	):
		start_index += 1
	start_index += 1

	_UpperCamelCase   :		int			=						start_index
	while not lines[end_index].startswith(UpperCAmelCase_	):
		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 |
lowerCAmelCase__								=		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase__								=		re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowerCAmelCase__								=		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.
lowerCAmelCase__								=		re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'							,       UpperCAmelCase_	)
	return [m.group(0	) for m in matches]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Union[str, Any]	)						->				str:




	'''simple docstring'''




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






def 					lowerCamelCase_     (							)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	_UpperCamelCase   :		List[Any]			=						{
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	_UpperCamelCase   :		Optional[int]			=						{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[Any]			=						collections.defaultdict(UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						collections.defaultdict(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						collections.defaultdict(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[Any]			=						collections.defaultdict(UpperCAmelCase_	)
	_UpperCamelCase   :		Union[str, Any]			=						collections.defaultdict(UpperCAmelCase_	)

	# Let's lookup through all transformers object (once).
	for attr_name in dir(UpperCAmelCase_	):
		_UpperCamelCase   :		int			=						None
		if attr_name.endswith('Tokenizer'	):
			_UpperCamelCase   :		Optional[int]			=						slow_tokenizers
			_UpperCamelCase   :		Any			=						attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'	):
			_UpperCamelCase   :		int			=						fast_tokenizers
			_UpperCamelCase   :		Optional[Any]			=						attr_name[:-1_3]
		elif _re_tf_models.match(UpperCAmelCase_	) is not None:
			_UpperCamelCase   :		Tuple			=						tf_models
			_UpperCamelCase   :		Union[str, Any]			=						_re_tf_models.match(UpperCAmelCase_	).groups()[0]
		elif _re_flax_models.match(UpperCAmelCase_	) is not None:
			_UpperCamelCase   :		List[Any]			=						flax_models
			_UpperCamelCase   :		Union[str, Any]			=						_re_flax_models.match(UpperCAmelCase_	).groups()[0]
		elif _re_pt_models.match(UpperCAmelCase_	) is not None:
			_UpperCamelCase   :		Any			=						pt_models
			_UpperCamelCase   :		Optional[int]			=						_re_pt_models.match(UpperCAmelCase_	).groups()[0]

		if lookup_dict is not None:
			while len(UpperCAmelCase_	) > 0:
				if attr_name in model_name_to_prefix.values():
					_UpperCamelCase   :		Optional[Any]			=						True
					break
				# Try again after removing the last word in the name
				_UpperCamelCase   :		Any			=						''.join(camel_case_split(UpperCAmelCase_	)[:-1]	)

    # Let's build that table!
	_UpperCamelCase   :		Optional[Any]			=						list(model_name_to_config.keys()	)
	model_names.sort(key=str.lower	)
	_UpperCamelCase   :		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   :		List[Any]			=						[len(UpperCAmelCase_	) + 2 for c in columns]
	_UpperCamelCase   :		Optional[int]			=						max([len(UpperCAmelCase_	) for name in model_names]	) + 2

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

	_UpperCamelCase   :		str			=						{True: '✅', False: '❌'}
	for name in model_names:
		_UpperCamelCase   :		Tuple			=						model_name_to_prefix[name]
		_UpperCamelCase   :		int			=						[
		    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(UpperCAmelCase_							,       UpperCAmelCase_	) for l, w in zip(UpperCAmelCase_							,       UpperCAmelCase_	)]	) + "|\n"
	return table






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]=False	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						_find_text_in_file(
	    filename=os.path.join(UpperCAmelCase_							,       'index.md'	)							,       start_prompt='<!--This table is updated automatically from the auto modules'							,       end_prompt='<!-- End table-->'							,       )
	_UpperCamelCase   :		Union[str, Any]			=						get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(UpperCAmelCase_							,       '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__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							lowerCAmelCase__								=		parser.parse_args()

							check_model_table(args.fix_and_overwrite)









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__								=		["""bert-base-uncased""", """bert-base-cased"""]
lowerCAmelCase__								=		"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():







							class 	lowercase    (				tf.keras.Model							):


									"""simple docstring"""




									def __init__(				self	,       __snake_case):
										super().__init__()
										_UpperCamelCase   :		List[Any]			=						tokenizer
										_UpperCamelCase   :		List[Any]			=						AutoConfig.from_pretrained(__snake_case)
										_UpperCamelCase   :		Dict			=						TFAutoModel.from_config(__snake_case)






									def 		A__					(				self	,       __snake_case):
										_UpperCamelCase   :		Any			=						self.tokenizer(__snake_case)
										_UpperCamelCase   :		Dict			=						self.bert(**__snake_case)
										return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self):
			super().setUp()

			_UpperCamelCase   :		Optional[Any]			=						[
			    BertTokenizer.from_pretrained(__snake_case) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
			]  # repeat for when fast_bert_tokenizer=false
			_UpperCamelCase   :		Optional[Any]			=						[TFBertTokenizer.from_pretrained(__snake_case) for checkpoint in TOKENIZER_CHECKPOINTS] + [
			    TFBertTokenizer.from_pretrained(__snake_case	,       use_fast_bert_tokenizer=__snake_case)
			    for checkpoint in TOKENIZER_CHECKPOINTS
			]
			assert len(self.tokenizers) == len(self.tf_tokenizers)

			_UpperCamelCase   :		Optional[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',
			]
			_UpperCamelCase   :		Dict			=						list(zip(self.test_sentences	,       self.test_sentences[::-1]))


		def 		A__					(				self):
			for tokenizer, tf_tokenizer in zip(self.tokenizers	,       self.tf_tokenizers):
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		List[str]			=						tokenizer(__snake_case	,       return_tensors='tf'	,       padding='longest')
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					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 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf_tokenizer(self.paired_sentences)
				_UpperCamelCase   :		Optional[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 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf.function(__snake_case)
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		Optional[int]			=						tf.constant(__snake_case)
					_UpperCamelCase   :		Union[str, Any]			=						compiled_tokenizer(__snake_case)
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					for key in eager_outputs.keys():
						self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))






		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Any			=						ModelToSave(tokenizer=__snake_case)
				_UpperCamelCase   :		Any			=						tf.convert_to_tensor(self.test_sentences)
				_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)  # Build model with some sample inputs
				with TemporaryDirectory() as tempdir:
					_UpperCamelCase   :		int			=						Path(__snake_case) / 'saved.model'
					model.save(__snake_case)
					_UpperCamelCase   :		Optional[int]			=						tf.keras.models.load_model(__snake_case)
				_UpperCamelCase   :		int			=						loaded_model(__snake_case)
				# 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)




