





'''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__   :				Optional[int]      =   '''src/transformers'''
lowercase__   :				List[Any]      =   '''docs/source/en'''
lowercase__   :				Any      =   '''.'''






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			Optional[int]			,							__snake_case  :			List[str]      )					->					Optional[Any]:
					with open(__snake_case			,							'r'			,							encoding='utf-8'			,							newline='\n'      ) as f:
										__A						:			int								=	f.readlines()
					# Find the start prompt.
					__A						:			int								=	0
					while not lines[start_index].startswith(__snake_case      ):
										start_index += 1
					start_index += 1

					__A						:			Tuple								=	start_index
					while not lines[end_index].startswith(__snake_case      ):
										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__   :				Optional[Any]      =   re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowercase__   :				List[str]      =   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__   :				Optional[Any]      =   direct_transformers_import(TRANSFORMERS_PATH)






def 	_lowerCAmelCase      (  __snake_case  :			Tuple      )					->					Optional[Any]:
					__A						:			Optional[Any]								=	re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'			,							__snake_case      )
					return [m.group(0      ) for m in matches]






def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			Union[str, Any]      )					->					int:
					__A						:			int								=	2 if text == '✅' or text == '❌' else len(__snake_case      )
					__A						:			Union[str, Any]								=	(width - text_length) // 2
					__A						:			Optional[int]								=	width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent






def 	_lowerCAmelCase      (  )					->					Optional[int]:
					__A						:			str								=	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					__A						:			List[Any]								=	{
					    name: config_maping_names[code]
					    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_maping_names
					}
					__A						:			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.
					__A						:			Optional[Any]								=	collections.defaultdict(__snake_case      )
					__A						:			int								=	collections.defaultdict(__snake_case      )
					__A						:			Any								=	collections.defaultdict(__snake_case      )
					__A						:			Optional[int]								=	collections.defaultdict(__snake_case      )
					__A						:			List[str]								=	collections.defaultdict(__snake_case      )

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

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

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

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

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






def 	_lowerCAmelCase      (  __snake_case  :			Any=False      )					->					Dict:
					__A						:			Union[str, Any]								=	_find_text_in_file(
					    filename=os.path.join(__snake_case			,							'index.md'      )			,							start_prompt='<!--This table is updated automatically from the auto modules'			,							end_prompt='<!-- End table-->'			,							)
					__A						:			Optional[int]								=	get_model_table_from_auto_modules()

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

		check_model_table(args.fix_and_overwrite)






'''simple docstring'''




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class 			SCREAMING_SNAKE_CASE   (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	SMALL_MODEL_IDENTIFIER
          __A						:			Any								=	'pt'
          __A						:			str								=	'tf'


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	AutoModel.from_pretrained(self.test_model)
          model_pt.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	TFAutoModel.from_pretrained(self.test_model						,     from_pt=_UpperCAmelCase)
          model_tf.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	'mock_framework'

          # Framework provided - return whatever the user provides
          __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model						,     _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # Local checkpoint and framework provided - return provided framework
          # PyTorch checkpoint
          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			Optional[int]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Invalid local checkpoint
          with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase):
               __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # PyTorch not in environment -> use TensorFlow
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			List[Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Both in environment -> use PyTorch
          __A						:			Any								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Dict								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			int								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # Both not in environment -> raise error
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			int								=	FeaturesManager.determine_framework(self.test_model)