

import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
    from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase=13							,  lowerCamelCase=32							,  lowerCamelCase=2							,  lowerCamelCase=3							,  lowerCamelCase=16							,  lowerCamelCase=[1, 2, 1]							,  lowerCamelCase=[2, 2, 4]							,  lowerCamelCase=2							,  lowerCamelCase=2.0							,  lowerCamelCase=True							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.1							,  lowerCamelCase="gelu"							,  lowerCamelCase=False							,  lowerCamelCase=True							,  lowerCamelCase=0.02							,  lowerCamelCase=1e-5							,  lowerCamelCase=True							,  lowerCamelCase=None							,  lowerCamelCase=True							,  lowerCamelCase=10							,  lowerCamelCase=8							,  lowerCamelCase=["stage1", "stage2"]							,  lowerCamelCase=[1, 2]							,  )						->       Optional[int]:
        snake_case_       						=      parent
        snake_case_       						=      batch_size
        snake_case_       						=      image_size
        snake_case_       						=      patch_size
        snake_case_       						=      num_channels
        snake_case_       						=      embed_dim
        snake_case_       						=      depths
        snake_case_       						=      num_heads
        snake_case_       						=      window_size
        snake_case_       						=      mlp_ratio
        snake_case_       						=      qkv_bias
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      drop_path_rate
        snake_case_       						=      hidden_act
        snake_case_       						=      use_absolute_embeddings
        snake_case_       						=      patch_norm
        snake_case_       						=      layer_norm_eps
        snake_case_       						=      initializer_range
        snake_case_       						=      is_training
        snake_case_       						=      scope
        snake_case_       						=      use_labels
        snake_case_       						=      type_sequence_label_size
        snake_case_       						=      encoder_stride
        snake_case_       						=      out_features
        snake_case_       						=      out_indices







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        snake_case_       						=      None
        if self.use_labels:
          snake_case_       						=      ids_tensor([self.batch_size]							,  self.type_sequence_label_size   )

        snake_case_       						=      self.get_config()

        return config, pixel_values, labels







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        return SwinConfig(
            image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  embed_dim=self.embed_dim							,  depths=self.depths							,  num_heads=self.num_heads							,  window_size=self.window_size							,  mlp_ratio=self.mlp_ratio							,  qkv_bias=self.qkv_bias							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  drop_path_rate=self.drop_path_rate							,  hidden_act=self.hidden_act							,  use_absolute_embeddings=self.use_absolute_embeddings							,  path_norm=self.patch_norm							,  layer_norm_eps=self.layer_norm_eps							,  initializer_range=self.initializer_range							,  encoder_stride=self.encoder_stride							,  out_features=self.out_features							,  out_indices=self.out_indices							,  )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       List[Any]:
        snake_case_       						=      SwinModel(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase   )

        snake_case_       						=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
        snake_case_       						=      int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

        self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, expected_seq_len, expected_dim)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       str:
        snake_case_       						=      SwinBackbone(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase   )

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps   )							,  len(config.out_features   )   )
        self.parent.assertListEqual(list(result.feature_maps[0].shape   )							,  [self.batch_size, model.channels[0], 16, 16]   )

        # verify channels
        self.parent.assertEqual(len(model.channels   )							,  len(config.out_features   )   )

        # verify backbone works with out_features=None
        snake_case_       						=      None
        snake_case_       						=      SwinBackbone(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase   )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps   )							,  1   )
        self.parent.assertListEqual(list(result.feature_maps[0].shape   )							,  [self.batch_size, model.channels[-1], 4, 4]   )

        # verify channels
        self.parent.assertEqual(len(model.channels   )							,  1   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       List[str]:
        snake_case_       						=      SwinForMaskedImageModeling(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase   )
        self.parent.assertEqual(
            result.logits.shape							,  (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

        # test greyscale images
        snake_case_       						=      1
        snake_case_       						=      SwinForMaskedImageModeling(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()

        snake_case_       						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        snake_case_       						=      model(lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, 1, self.image_size, self.image_size)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Union[str, Any]:
        snake_case_       						=      self.type_sequence_label_size
        snake_case_       						=      SwinForImageClassification(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase							,  labels=lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)   )

        # test greyscale images
        snake_case_       						=      1
        snake_case_       						=      SwinForImageClassification(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()

        snake_case_       						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        snake_case_       						=      model(lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        snake_case_       						=      self.prepare_config_and_inputs()
        (
            (
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				
        )       						=      config_and_inputs
        snake_case_       						=      {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class  __lowerCamelCase				(	__snake_case  ,							__snake_case  ,							unittest.TestCase	):
      lowerCamelCase_	:      Union[str, Any]          =					(
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase_	:      List[Any]          =					(
          {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCamelCase_	:      int          =					True

      lowerCamelCase_	:      List[Any]          =					False
      lowerCamelCase_	:      Union[str, Any]          =					False
      lowerCamelCase_	:      List[Any]          =					False







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      SwinModelTester(self   )
        snake_case_       						=      ConfigTester(self							,  config_class=lowerCamelCase							,  embed_dim=37   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







      def 	lowerCAmelCase_    (     self   )						->       int:
        return







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase   )







      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""   )
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        pass







      def 	lowerCAmelCase_    (     self   )						->       int:
        super().test_training_gradient_checkpointing()







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase   )







      @unittest.skip(reason="""Swin does not use inputs_embeds"""   )
      def 	lowerCAmelCase_    (     self   )						->       str:
        pass







      @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""   )
      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        pass







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          snake_case_       						=      model_class(lowerCamelCase   )
          self.assertIsInstance(model.get_input_embeddings()							,  (nn.Module)   )
          snake_case_       						=      model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowerCamelCase							,  nn.Linear   )   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          snake_case_       						=      model_class(lowerCamelCase   )
          snake_case_       						=      inspect.signature(model.forward   )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          snake_case_       						=      [*signature.parameters.keys()]

          snake_case_       						=      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_       						=      True

        for model_class in self.all_model_classes:
          snake_case_       						=      True
          snake_case_       						=      False
          snake_case_       						=      True
          snake_case_       						=      model_class(lowerCamelCase   )
          model.to(lowerCamelCase   )
          model.eval()
          with torch.no_grad():
            snake_case_       						=      model(**self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )   )
          snake_case_       						=      outputs.attentions
          snake_case_       						=      len(self.model_tester.depths   )
          self.assertEqual(len(lowerCamelCase   )							,  lowerCamelCase   )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          snake_case_       						=      True
          snake_case_       						=      config.window_size**2
          snake_case_       						=      model_class(lowerCamelCase   )
          model.to(lowerCamelCase   )
          model.eval()
          with torch.no_grad():
            snake_case_       						=      model(**self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )   )
          snake_case_       						=      outputs.attentions
          self.assertEqual(len(lowerCamelCase   )							,  lowerCamelCase   )

          self.assertListEqual(
              list(attentions[0].shape[-3:]   )							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )
          snake_case_       						=      len(lowerCamelCase   )

          # Check attention is always last and order is fine
          snake_case_       						=      True
          snake_case_       						=      True
          snake_case_       						=      model_class(lowerCamelCase   )
          model.to(lowerCamelCase   )
          model.eval()
          with torch.no_grad():
            snake_case_       						=      model(**self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )   )

          # also another +1 for reshaped_hidden_states
          snake_case_       						=      1 if model_class.__name__ == """SwinBackbone""" else 2
          self.assertEqual(out_len + added_hidden_states							,  len(lowerCamelCase   )   )

          snake_case_       						=      outputs.attentions

          self.assertEqual(len(lowerCamelCase   )							,  lowerCamelCase   )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]   )							,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,  )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       List[Any]:
        snake_case_       						=      model_class(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()

        with torch.no_grad():
          snake_case_       						=      model(**self._prepare_for_class(lowerCamelCase							,  lowerCamelCase   )   )

        snake_case_       						=      outputs.hidden_states

        snake_case_       						=      getattr(
            self.model_tester							,  """expected_num_hidden_layers"""							,  len(self.model_tester.depths   ) + 1   )
        self.assertEqual(len(lowerCamelCase   )							,  lowerCamelCase   )

        # Swin has a different seq_length
        snake_case_       						=      (
            config.patch_size
            if isinstance(config.patch_size							,  collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

        snake_case_       						=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )							,  [num_patches, self.model_tester.embed_dim]							,  )

        if not model_class.__name__ == "SwinBackbone":
          snake_case_       						=      outputs.reshaped_hidden_states
          self.assertEqual(len(lowerCamelCase   )							,  lowerCamelCase   )

          snake_case_						,				snake_case_						,				snake_case_						,				snake_case_       						=      reshaped_hidden_states[0].shape
          snake_case_       						=      (
              reshaped_hidden_states[0].view(lowerCamelCase							,  lowerCamelCase							,  height * width   ).permute(0							,  2							,  1   )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]   )							,  [num_patches, self.model_tester.embed_dim]							,  )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_       						=      (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size							,  collections.abc.Iterable   )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
          snake_case_       						=      True
          self.check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          snake_case_       						=      True

          self.check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_       						=      3

        snake_case_       						=      (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size							,  collections.abc.Iterable   )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        snake_case_       						=      (
            config.patch_size
            if isinstance(config.patch_size							,  collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

        snake_case_       						=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        snake_case_       						=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
          snake_case_       						=      True
          self.check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  (padded_height, padded_width)   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          snake_case_       						=      True
          self.check_hidden_states_output(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  (padded_height, padded_width)   )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case_       						=      SwinModel.from_pretrained(lowerCamelCase   )
          self.assertIsNotNone(lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_						,				snake_case_       						=      self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_       						=      _config_zero_init(lowerCamelCase   )
        for model_class in self.all_model_classes:
          snake_case_       						=      model_class(config=lowerCamelCase   )
          for name, param in model.named_parameters():
            if "embeddings" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()							,  [0.0, 1.0]							,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,  )







@require_vision
@require_torch
class  __lowerCamelCase				(	unittest.TestCase	):







      @cached_property
      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        return (
            AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   )
            if is_vision_available()
            else None
        )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       Any:
        snake_case_       						=      SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   ).to(lowerCamelCase   )
        snake_case_       						=      self.default_image_processor

        snake_case_       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
        snake_case_       						=      image_processor(images=lowerCamelCase							,  return_tensors="""pt"""   ).to(lowerCamelCase   )

        # forward pass
        with torch.no_grad():
          snake_case_       						=      model(**lowerCamelCase   )

        # verify the logits
        snake_case_       						=      torch.Size((1, 1000)   )
        self.assertEqual(outputs.logits.shape							,  lowerCamelCase   )
        snake_case_       						=      torch.tensor([-0.0948, -0.6454, -0.0921]   ).to(lowerCamelCase   )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]							,  lowerCamelCase							,  atol=1e-4   )   )







@require_torch
class  __lowerCamelCase				(	unittest.TestCase  ,							__snake_case	):
      lowerCamelCase_	:      Union[str, Any]          =					(SwinBackbone,) if is_torch_available() else ()
      lowerCamelCase_	:      List[Any]          =					SwinConfig







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      SwinModelTester(self   )


"""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
__A     :    List[str]    =		'src/diffusers'
__A     :    Optional[int]    =		'docs/source/en'
__A     :    Union[str, Any]    =		'.'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							"r"			,							encoding="utf-8"			,							newline="\n"      ) as f:
										A			=					f.readlines()
					# Find the start prompt.
					A			=					0
					while not lines[start_index].startswith(lowercase__      ):
										start_index += 1
					start_index += 1

					A			=					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 |
__A     :    Any    =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A     :    Union[str, Any]    =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A     :    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.
__A     :    Any    =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__A     :    Optional[int]    =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__A     :    Tuple    =		spec.loader.load_module()
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							lowercase__      )
					return [m.group(0      ) for m in matches]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					2 if text == "✅" or text == "❌" else len(lowercase__      )
					A			=					(width - text_length) // 2
					A			=					width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A			=					{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A			=					{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.
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )

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

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

					# Build the table per se
					A			=					"|" + "|".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"

					A			=					{True: "✅", False: "❌"}
					for name in model_names:
										A			=					model_name_to_prefix[name]
										A			=					[
										    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    __SCREAMING_SNAKE_CASE						(						lowercase__=False      ):
					"""simple docstring"""
					A			,    A			,    A			,    A			=					_find_text_in_file(
					    filename=os.path.join(lowercase__			,							"index.mdx"      )			,							start_prompt="<!--This table is updated automatically from the auto modules"			,							end_prompt="<!-- End table-->"			,							)
					A			=					get_model_table_from_auto_modules()

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

	check_model_table(args.fix_and_overwrite)





