






import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     YosoForMaskedLM,
     YosoForMultipleChoice,
     YosoForQuestionAnswering,
     YosoForSequenceClassification,
     YosoForTokenClassification,
     YosoModel,
 )
 from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class  lowerCAmelCase__ :
   def __init__(		self			:  str       , snake_case__			:  int       , snake_case__			:  List[Any]=1_3       , snake_case__			:  List[str]=7       , snake_case__			:  Dict=True       , snake_case__			:  List[Any]=True       , snake_case__			:  Dict=True       , snake_case__			:  int=True       , snake_case__			:  Union[str, Any]=9_9       , snake_case__			:  str=3_2       , snake_case__			:  Optional[int]=5       , snake_case__			:  Optional[Any]=4       , snake_case__			:  Dict=3_7       , snake_case__			:  Optional[Any]="gelu"       , snake_case__			:  List[Any]=0.1       , snake_case__			:  str=0.1       , snake_case__			:  int=5_1_2       , snake_case__			:  Tuple=1_6       , snake_case__			:  Optional[Any]=2       , snake_case__			:  Optional[Any]=0.02       , snake_case__			:  List[Any]=3       , snake_case__			:  List[Any]=4       , snake_case__			:  Tuple=None       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       parent
        UpperCAmelCase__	:						str       =       batch_size
        UpperCAmelCase__	:						List[str]       =       seq_length
        UpperCAmelCase__	:						List[Any]       =       is_training
        UpperCAmelCase__	:						Any       =       use_input_mask
        UpperCAmelCase__	:						Union[str, Any]       =       use_token_type_ids
        UpperCAmelCase__	:						Tuple       =       use_labels
        UpperCAmelCase__	:						Any       =       vocab_size
        UpperCAmelCase__	:						str       =       hidden_size
        UpperCAmelCase__	:						List[str]       =       num_hidden_layers
        UpperCAmelCase__	:						List[Any]       =       num_attention_heads
        UpperCAmelCase__	:						int       =       intermediate_size
        UpperCAmelCase__	:						str       =       hidden_act
        UpperCAmelCase__	:						List[Any]       =       hidden_dropout_prob
        UpperCAmelCase__	:						str       =       attention_probs_dropout_prob
        UpperCAmelCase__	:						Optional[Any]       =       max_position_embeddings
        UpperCAmelCase__	:						Tuple       =       type_vocab_size
        UpperCAmelCase__	:						Dict       =       type_sequence_label_size
        UpperCAmelCase__	:						int       =       initializer_range
        UpperCAmelCase__	:						List[Any]       =       num_labels
        UpperCAmelCase__	:						int       =       num_choices
        UpperCAmelCase__	:						Dict       =       scope
   def 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size       )

        UpperCAmelCase__	:						Any       =       None
        if self.use_input_mask:
             UpperCAmelCase__	:						List[Any]       =       random_attention_mask([self.batch_size, self.seq_length]       )

        UpperCAmelCase__	:						str       =       None
        if self.use_token_type_ids:
             UpperCAmelCase__	:						int       =       ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size       )

        UpperCAmelCase__	:						Any       =       None
        UpperCAmelCase__	:						int       =       None
        UpperCAmelCase__	:						Union[str, Any]       =       None
        if self.use_labels:
             UpperCAmelCase__	:						str       =       ids_tensor([self.batch_size]       , self.type_sequence_label_size       )
             UpperCAmelCase__	:						Tuple       =       ids_tensor([self.batch_size, self.seq_length]       , self.num_labels       )
             UpperCAmelCase__	:						str       =       ids_tensor([self.batch_size]       , self.num_choices       )

        UpperCAmelCase__	:						Union[str, Any]       =       self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        return YosoConfig(
            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=snake_case__       , initializer_range=self.initializer_range       , )
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       self.get_config()
        UpperCAmelCase__	:						str       =       3_0_0
        return config
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        (
            UpperCAmelCase__							
        )	:						Union[str, Any]       =       self.prepare_config_and_inputs()

        UpperCAmelCase__	:						List[Any]       =       True
        UpperCAmelCase__	:						Union[str, Any]       =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
        UpperCAmelCase__	:						Optional[Any]       =       ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2       )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )
   def 			__a       (		self			:  Tuple       , snake_case__			:  Dict       , snake_case__			:  List[str]       , snake_case__			:  Optional[int]       , snake_case__			:  str       , snake_case__			:  Tuple       , snake_case__			:  Any       , snake_case__			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       YosoModel(config=snake_case__       )
        model.to(snake_case__       )
        model.eval()
        UpperCAmelCase__	:						List[str]       =       model(snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       )
        UpperCAmelCase__	:						Dict       =       model(snake_case__       , token_type_ids=snake_case__       )
        UpperCAmelCase__	:						Optional[int]       =       model(snake_case__       )
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)       )
   def 			__a       (		self			:  str       , snake_case__			:  Dict       , snake_case__			:  Union[str, Any]       , snake_case__			:  List[str]       , snake_case__			:  List[Any]       , snake_case__			:  Optional[int]       , snake_case__			:  Dict       , snake_case__			:  str       , snake_case__			:  Union[str, Any]       , snake_case__			:  Union[str, Any]       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       True
        UpperCAmelCase__	:						Optional[Any]       =       YosoModel(snake_case__       )
        model.to(snake_case__       )
        model.eval()
        UpperCAmelCase__	:						Optional[int]       =       model(
            snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       , encoder_hidden_states=snake_case__       , encoder_attention_mask=snake_case__       , )
        UpperCAmelCase__	:						Union[str, Any]       =       model(
            snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       , encoder_hidden_states=snake_case__       , )
        UpperCAmelCase__	:						Optional[int]       =       model(snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       )
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)       )
   def 			__a       (		self			:  Tuple       , snake_case__			:  Optional[int]       , snake_case__			:  Tuple       , snake_case__			:  List[str]       , snake_case__			:  Dict       , snake_case__			:  str       , snake_case__			:  List[Any]       , snake_case__			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       YosoForMaskedLM(config=snake_case__       )
        model.to(snake_case__       )
        model.eval()
        UpperCAmelCase__	:						Dict       =       model(snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       , labels=snake_case__       )
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)       )
   def 			__a       (		self			:  str       , snake_case__			:  Optional[Any]       , snake_case__			:  Tuple       , snake_case__			:  Optional[int]       , snake_case__			:  int       , snake_case__			:  Tuple       , snake_case__			:  Optional[Any]       , snake_case__			:  List[str]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       YosoForQuestionAnswering(config=snake_case__       )
        model.to(snake_case__       )
        model.eval()
        UpperCAmelCase__	:						str       =       model(
            snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       , start_positions=snake_case__       , end_positions=snake_case__       , )
        self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length)       )
        self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length)       )
   def 			__a       (		self			:  str       , snake_case__			:  Tuple       , snake_case__			:  List[str]       , snake_case__			:  List[Any]       , snake_case__			:  Optional[Any]       , snake_case__			:  List[str]       , snake_case__			:  List[Any]       , snake_case__			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       self.num_labels
        UpperCAmelCase__	:						Tuple       =       YosoForSequenceClassification(snake_case__       )
        model.to(snake_case__       )
        model.eval()
        UpperCAmelCase__	:						Optional[Any]       =       model(snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       , labels=snake_case__       )
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels)       )
   def 			__a       (		self			:  str       , snake_case__			:  Dict       , snake_case__			:  List[str]       , snake_case__			:  int       , snake_case__			:  List[Any]       , snake_case__			:  Tuple       , snake_case__			:  Optional[int]       , snake_case__			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       self.num_labels
        UpperCAmelCase__	:						Optional[int]       =       YosoForTokenClassification(config=snake_case__       )
        model.to(snake_case__       )
        model.eval()
        UpperCAmelCase__	:						Tuple       =       model(snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       , labels=snake_case__       )
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels)       )
   def 			__a       (		self			:  List[Any]       , snake_case__			:  Optional[Any]       , snake_case__			:  List[Any]       , snake_case__			:  str       , snake_case__			:  int       , snake_case__			:  Optional[int]       , snake_case__			:  Optional[Any]       , snake_case__			:  List[str]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       self.num_choices
        UpperCAmelCase__	:						Tuple       =       YosoForMultipleChoice(config=snake_case__       )
        model.to(snake_case__       )
        model.eval()
        UpperCAmelCase__	:						Optional[int]       =       input_ids.unsqueeze(1       ).expand(-1       , self.num_choices       , -1       ).contiguous()
        UpperCAmelCase__	:						Optional[int]       =       token_type_ids.unsqueeze(1       ).expand(-1       , self.num_choices       , -1       ).contiguous()
        UpperCAmelCase__	:						Tuple       =       input_mask.unsqueeze(1       ).expand(-1       , self.num_choices       , -1       ).contiguous()
        UpperCAmelCase__	:						Optional[Any]       =       model(
            snake_case__       , attention_mask=snake_case__       , token_type_ids=snake_case__       , labels=snake_case__       , )
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices)       )




   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       self.prepare_config_and_inputs()
        (
            UpperCAmelCase__							
        )	:						Optional[Any]       =       config_and_inputs
        UpperCAmelCase__	:						Optional[int]       =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_torch
class  lowerCAmelCase__ (						__magic_name__       ,	__magic_name__       ,	unittest.TestCase      ):
   SCREAMING_SNAKE_CASE_         =(
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   SCREAMING_SNAKE_CASE_         =False
   SCREAMING_SNAKE_CASE_         =False
   SCREAMING_SNAKE_CASE_         =False

   SCREAMING_SNAKE_CASE_         =()
   SCREAMING_SNAKE_CASE_         =(
       {
           '''feature-extraction''': YosoModel,
           '''fill-mask''': YosoForMaskedLM,
           '''question-answering''': YosoForQuestionAnswering,
           '''text-classification''': YosoForSequenceClassification,
           '''token-classification''': YosoForTokenClassification,
           '''zero-shot''': YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   def 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       YosoModelTester(self       )
        UpperCAmelCase__	:						List[Any]       =       ConfigTester(self       , config_class=snake_case__       , hidden_size=3_7       )
   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__       )
   def 			__a       (		self			:  List[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCAmelCase__	:						Dict       =       type
             self.model_tester.create_and_check_model(*snake_case__       )
   def 			__a       (		self			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*snake_case__       )
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*snake_case__       )
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*snake_case__       )
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*snake_case__       )
   def 			__a       (		self			:  List[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[Any]       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*snake_case__       )
   @slow
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase__	:						List[Any]       =       YosoModel.from_pretrained(snake_case__       )
             self.assertIsNotNone(snake_case__       )




   def 			__a       (		self			:  Any       ):


        '''simple docstring'''
        return




@require_torch
class  lowerCAmelCase__ (						unittest.TestCase      ):
   @slow
   def 			__a       (		self			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       YosoModel.from_pretrained("uw-madison/yoso-4096"       )
        UpperCAmelCase__	:						Union[str, Any]       =       torch.tensor([[0, 1, 2, 3, 4, 5]]       )

        with torch.no_grad():
             UpperCAmelCase__	:						Tuple       =       model(snake_case__       )[0]

        UpperCAmelCase__	:						Any       =       torch.Size((1, 6, 7_6_8)       )
        self.assertEqual(output.shape       , snake_case__       )

        UpperCAmelCase__	:						str       =       torch.tensor(
            [[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]       )

        self.assertTrue(torch.allclose(output[:, :3, :3]       , snake_case__       , atol=1e-4       )       )
   @slow
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[Any]       =       YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"       )
        UpperCAmelCase__	:						int       =       torch.tensor([[0, 1, 2, 3, 4, 5]]       )

        with torch.no_grad():
             UpperCAmelCase__	:						Optional[int]       =       model(snake_case__       )[0]

        UpperCAmelCase__	:						Tuple       =       5_0_2_6_5

        UpperCAmelCase__	:						Optional[Any]       =       torch.Size((1, 6, vocab_size)       )
        self.assertEqual(output.shape       , snake_case__       )

        UpperCAmelCase__	:						Dict       =       torch.tensor(
            [[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]       )

        self.assertTrue(torch.allclose(output[:, :3, :3]       , snake_case__       , atol=1e-4       )       )




   @slow
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"       )
        UpperCAmelCase__	:						Tuple       =       torch.arange(4_0_9_6       ).unsqueeze(0       )

        with torch.no_grad():
             UpperCAmelCase__	:						Optional[Any]       =       model(snake_case__       )[0]

        UpperCAmelCase__	:						Tuple       =       5_0_2_6_5

        UpperCAmelCase__	:						Union[str, Any]       =       torch.Size((1, 4_0_9_6, vocab_size)       )
        self.assertEqual(output.shape       , snake_case__       )

        UpperCAmelCase__	:						Dict       =       torch.tensor(
            [[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]       )

        self.assertTrue(torch.allclose(output[:, :3, :3]       , snake_case__       , atol=1e-4       )       )










"""simple docstring"""



import argparse
import collections
import importlib.util
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_table.py
_lowerCAmelCase  :  List[str]   =			"""src/diffusers"""
_lowerCAmelCase  :  Union[str, Any]   =			"""docs/source/en"""
_lowerCAmelCase  :  List[str]   =			"""."""


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[Any] ,				snake_case					:   Optional[Any] ,				snake_case					:   Optional[Any]							)->		str:






     '''simple docstring'''



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

     UpperCAmelCase__	:						Union[str, Any]       =       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 |
_lowerCAmelCase  :  str   =			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase  :  List[Any]   =			re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCAmelCase  :  Optional[int]   =			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  :  str   =			re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_lowerCAmelCase  :  Any   =			importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_lowerCAmelCase  :  int   =			spec.loader.load_module()


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[int]							)->		List[Any]:






     '''simple docstring'''



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


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int ,				snake_case					:   Tuple							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       2 if text == "✅" or text == "❌" else len(snake_case							)
     UpperCAmelCase__	:						int       =       (width - text_length) // 2
     UpperCAmelCase__	:						Any       =       width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent


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






     '''simple docstring'''



     UpperCAmelCase__	:						Tuple       =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__	:						Any       =       {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__	:						Optional[int]       =       {name: config.replace("ConfigMixin" ,				""							) 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(snake_case							)
     UpperCAmelCase__	:						Dict       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						List[str]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Tuple       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Any       =       collections.defaultdict(snake_case							)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(snake_case							):
          UpperCAmelCase__	:						Optional[int]       =       None
          if attr_name.endswith("Tokenizer"							):
               UpperCAmelCase__	:						Optional[Any]       =       slow_tokenizers
               UpperCAmelCase__	:						Optional[Any]       =       attr_name[:-9]
          elif attr_name.endswith("TokenizerFast"							):
               UpperCAmelCase__	:						Union[str, Any]       =       fast_tokenizers
               UpperCAmelCase__	:						str       =       attr_name[:-13]
          elif _re_tf_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						List[str]       =       tf_models
               UpperCAmelCase__	:						List[str]       =       _re_tf_models.match(snake_case							).groups()[0]
          elif _re_flax_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						str       =       flax_models
               UpperCAmelCase__	:						Union[str, Any]       =       _re_flax_models.match(snake_case							).groups()[0]
          elif _re_pt_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						int       =       pt_models
               UpperCAmelCase__	:						Optional[Any]       =       _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():
                         UpperCAmelCase__	:						int       =       True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase__	:						List[str]       =       "".join(camel_case_split(snake_case							)[:-1]							)

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

     # Build the table per se
     UpperCAmelCase__	:						Any       =       "|" + "|".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"

     UpperCAmelCase__	:						str       =       {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__	:						str       =       model_name_to_prefix[name]
          UpperCAmelCase__	:						Dict       =       [
              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   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any]=False							)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       _find_text_in_file(
         filename=os.path.join(snake_case ,				"index.mdx"							) ,				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(snake_case ,				"index.mdx"							) ,				"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.mdx` has not been updated. Run `make fix-copies` to fix this."							)


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

 check_model_table(args.fix_and_overwrite)


