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
UpperCAmelCase_           =				'''src/transformers'''
UpperCAmelCase_           =				'''docs/source/en'''
UpperCAmelCase_           =				'''.'''




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Union[str, Any]	,       lowercase:				Tuple		)     ->					List[str]:







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

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


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




def    lowerCAmelCase_  (		lowercase:				List[Any]		)     ->					Optional[int]:







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




def    lowerCAmelCase_  (		lowercase:				Dict	,       lowercase:				List[str]		)     ->					Dict:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			2 if text == '''✅''' or text == '''❌''' else len(lowercase		)
 _UpperCamelCase:  str    						=			(width - text_length) // 2
 _UpperCamelCase:  List[Any]    						=			width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent




def    lowerCAmelCase_  (		)     ->					Dict:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 _UpperCamelCase:  Dict    						=			{
     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:  Tuple    						=			collections.defaultdict(lowercase		)
 _UpperCamelCase:  Dict    						=			collections.defaultdict(lowercase		)
 _UpperCamelCase:  Tuple    						=			collections.defaultdict(lowercase		)
 _UpperCamelCase:  List[str]    						=			collections.defaultdict(lowercase		)
 _UpperCamelCase:  List[Any]    						=			collections.defaultdict(lowercase		)

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

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

    # Let's build that table!
 _UpperCamelCase:  Tuple    						=			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:  List[str]    						=			[len(lowercase		) + 2 for c in columns]
 _UpperCamelCase:  Optional[Any]    						=			max([len(lowercase		) for name in model_names]		) + 2

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

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




def    lowerCAmelCase_  (		lowercase:				Tuple=False		)     ->					List[Any]:







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

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

  check_model_table(args.fix_and_overwrite)
import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Any		,	_lowercase       :	Optional[int]		,	_lowercase       :	Tuple=13		,	_lowercase       :	str=7		,	_lowercase       :	List[Any]=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	str=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Dict=99		,	_lowercase       :	List[Any]=32		,	_lowercase       :	List[str]=5		,	_lowercase       :	str=4		,	_lowercase       :	int=37		,	_lowercase       :	List[str]="gelu"		,	_lowercase       :	Any=0.1		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Dict=512		,	_lowercase       :	int=16		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Dict=0.02		,	_lowercase       :	List[Any]=4		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			parent
    _UpperCamelCase:  int    						=			batch_size
    _UpperCamelCase:  List[str]    						=			seq_length
    _UpperCamelCase:  Optional[int]    						=			is_training
    _UpperCamelCase:  Optional[Any]    						=			use_attention_mask
    _UpperCamelCase:  Any    						=			use_token_type_ids
    _UpperCamelCase:  List[str]    						=			use_labels
    _UpperCamelCase:  Optional[int]    						=			vocab_size
    _UpperCamelCase:  List[str]    						=			hidden_size
    _UpperCamelCase:  Union[str, Any]    						=			num_hidden_layers
    _UpperCamelCase:  Any    						=			num_attention_heads
    _UpperCamelCase:  List[str]    						=			intermediate_size
    _UpperCamelCase:  Union[str, Any]    						=			hidden_act
    _UpperCamelCase:  Dict    						=			hidden_dropout_prob
    _UpperCamelCase:  List[str]    						=			attention_probs_dropout_prob
    _UpperCamelCase:  str    						=			max_position_embeddings
    _UpperCamelCase:  Dict    						=			type_vocab_size
    _UpperCamelCase:  Tuple    						=			type_sequence_label_size
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  str    						=			num_choices



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Any    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[Any]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  Tuple    						=			AlbertConfig(
        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=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  int    						=			config_and_inputs
    _UpperCamelCase:  int    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModelTester(self				)



   @slow
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    for model_class_name in self.all_model_classes:
     _UpperCamelCase:  int    						=			model_class_name.from_pretrained('''albert-base-v2'''				)
     _UpperCamelCase:  List[Any]    						=			model(np.ones((1, 1)				)				)
     self.assertIsNotNone(_lowercase				)




@require_flax
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   @slow
   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModel.from_pretrained('''albert-base-v2'''				)
    _UpperCamelCase:  List[Any]    						=			np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]				)
    _UpperCamelCase:  Optional[int]    						=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
    _UpperCamelCase:  Optional[int]    						=			model(_lowercase		,	attention_mask=_lowercase				)[0]
    _UpperCamelCase:  Tuple    						=			(1, 11, 768)
    self.assertEqual(output.shape		,	_lowercase				)
    _UpperCamelCase:  Tuple    						=			np.array(
        [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]				)

    self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]		,	_lowercase		,	atol=1E-4				)				)