
"""simple docstring"""

from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
      from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.config_class(**self.inputs_dict )
      self.parent.assertTrue(hasattr(__UpperCAmelCase      ,	'hidden_sizes' ) )
      self.parent.assertTrue(hasattr(__UpperCAmelCase      ,	'neck_hidden_sizes' ) )
      self.parent.assertTrue(hasattr(__UpperCAmelCase      ,	'num_attention_heads' ) )


class __lowerCAmelCase						:





     def __init__(      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=13      ,	__UpperCAmelCase=32      ,	__UpperCAmelCase=2      ,	__UpperCAmelCase=3      ,	__UpperCAmelCase=640      ,	__UpperCAmelCase=4      ,	__UpperCAmelCase="silu"      ,	__UpperCAmelCase=3      ,	__UpperCAmelCase=32      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=0.0_2      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=10      ,	__UpperCAmelCase=None      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			parent
      __UpperCamelCase							    =			batch_size
      __UpperCamelCase							    =			image_size
      __UpperCamelCase							    =			patch_size
      __UpperCamelCase							    =			num_channels
      __UpperCamelCase							    =			last_hidden_size
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			hidden_act
      __UpperCamelCase							    =			conv_kernel_size
      __UpperCamelCase							    =			output_stride
      __UpperCamelCase							    =			hidden_dropout_prob
      __UpperCamelCase							    =			attention_probs_dropout_prob
      __UpperCamelCase							    =			classifier_dropout_prob
      __UpperCamelCase							    =			use_labels
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			num_labels
      __UpperCamelCase							    =			initializer_range
      __UpperCamelCase							    =			scope





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      __UpperCamelCase							    =			None
      __UpperCamelCase							    =			None
      if self.use_labels:
       __UpperCamelCase							    =			ids_tensor([self.batch_size]      ,	self.num_labels )
       __UpperCamelCase							    =			ids_tensor([self.batch_size, self.image_size, self.image_size]      ,	self.num_labels )

      __UpperCamelCase							    =			self.get_config()

      return config, pixel_values, labels, pixel_labels





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return MobileViTConfig(
          image_size=self.image_size      ,	patch_size=self.patch_size      ,	num_channels=self.num_channels      ,	num_attention_heads=self.num_attention_heads      ,	hidden_act=self.hidden_act      ,	conv_kernel_size=self.conv_kernel_size      ,	output_stride=self.output_stride      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	classifier_dropout_prob=self.classifier_dropout_prob      ,	initializer_range=self.initializer_range      ,	)





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			TFMobileViTModel(config=__UpperCAmelCase )
      __UpperCamelCase							    =			model(__UpperCAmelCase      ,	training=__UpperCAmelCase )
      __UpperCamelCase							    =			__UpperCamelCase							    =			self.image_size // self.output_stride
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,	(self.batch_size, self.last_hidden_size, expected_height, expected_width) )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.num_labels
      __UpperCamelCase							    =			TFMobileViTForImageClassification(__UpperCAmelCase )
      __UpperCamelCase							    =			model(__UpperCAmelCase      ,	labels=__UpperCAmelCase      ,	training=__UpperCAmelCase )
      self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_labels) )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.num_labels
      __UpperCamelCase							    =			TFMobileViTForSemanticSegmentation(__UpperCAmelCase )
      __UpperCamelCase							    =			__UpperCamelCase							    =			self.image_size // self.output_stride

      __UpperCamelCase							    =			model(__UpperCAmelCase      ,	training=__UpperCAmelCase )
      self.parent.assertEqual(
          result.logits.shape      ,	(self.batch_size, self.num_labels, expected_height, expected_width) )

      __UpperCamelCase							    =			model(__UpperCAmelCase      ,	labels=__UpperCAmelCase      ,	training=__UpperCAmelCase )
      self.parent.assertEqual(
          result.logits.shape      ,	(self.batch_size, self.num_labels, expected_height, expected_width) )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.prepare_config_and_inputs()
      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase							    =			config_and_inputs
      __UpperCamelCase							    =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_tf
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		(
         (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
     lowercase		  =		(
         {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
         if is_tf_available()
         else {}
     )

     lowercase		  =		False
     lowercase		  =		False
     lowercase		  =		False
     lowercase		  =		False
     lowercase		  =		False





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			TFMobileViTModelTester(self )
      __UpperCamelCase							    =			TFMobileViTConfigTester(self      ,	config_class=__UpperCAmelCase      ,	has_text_modality=__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     @unittest.skip(reason='MobileViT does not use inputs_embeds' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      pass





     @unittest.skip(reason='MobileViT does not support input and output embeddings' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      pass





     @unittest.skip(reason='MobileViT does not output attentions' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs_for_common()

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

       __UpperCamelCase							    =			['pixel_values']
       self.assertListEqual(arg_names[:1]      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			model_class(__UpperCAmelCase )

       __UpperCamelCase							    =			model(**self._prepare_for_class(__UpperCAmelCase      ,	__UpperCAmelCase ) )

       __UpperCamelCase							    =			outputs.hidden_states

       __UpperCamelCase							    =			5
       self.assertEqual(len(__UpperCAmelCase )      ,	__UpperCAmelCase )

       # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
       # with the width and height being successively divided by 2.
       __UpperCamelCase							    =			2
       for i in range(len(__UpperCAmelCase ) ):
        self.assertListEqual(
            list(hidden_states[i].shape[-2:] )      ,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]      ,	)
        divisor *= 2

       self.assertEqual(self.model_tester.output_stride      ,	divisor // 2 )

      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase							    =			True
       check_hidden_states_output(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )

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

       check_hidden_states_output(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase )





     @unittest.skipIf(
         not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0      ,	reason='TF does not support backprop for grouped convolutions on CPU.'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_dataset_conversion()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase=2E-1      ,	__UpperCAmelCase=2E-1 ):

      '''simple docstring'''




      self.assertTrue(np.allclose(__UpperCAmelCase      ,	__UpperCAmelCase      ,	atol=__UpperCAmelCase      ,	rtol=__UpperCAmelCase ) )





     @unittest.skipIf(
         not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0      ,	reason='TF does not support backprop for grouped convolutions on CPU.'      ,	)
     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
       if model_class.__name__ != "TFMobileViTModel":
        __UpperCamelCase							    =			model_class(__UpperCAmelCase )
        if getattr(__UpperCAmelCase      ,	'hf_compute_loss'      ,	__UpperCAmelCase ):
         super().test_keras_fit()





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       # set an ignore index to correctly test the masked loss used in
       # `TFMobileViTForSemanticSegmentation`.
       if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
        __UpperCamelCase							    =			5

       __UpperCamelCase							    =			model_class(__UpperCAmelCase )
       if getattr(__UpperCAmelCase      ,	'hf_compute_loss'      ,	__UpperCAmelCase ):
        # The number of elements in the loss should be the same as the number of elements in the label
        __UpperCamelCase							    =			self._prepare_for_class(inputs_dict.copy()      ,	__UpperCAmelCase      ,	return_labels=__UpperCAmelCase )
        __UpperCamelCase							    =			prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()      ,	reverse=__UpperCAmelCase )[0]
        ]
        __UpperCamelCase							    =			added_label.shape.as_list()[:1]

        # Test that model correctly compute the loss with kwargs
        __UpperCamelCase							    =			self._prepare_for_class(inputs_dict.copy()      ,	__UpperCAmelCase      ,	return_labels=__UpperCAmelCase )
        __UpperCamelCase							    =			{'input_ids', 'pixel_values', 'input_features'}
        __UpperCamelCase							    =			possible_input_names.intersection(set(__UpperCAmelCase ) ).pop()
        __UpperCamelCase							    =			prepared_for_class.pop(__UpperCAmelCase )

        __UpperCamelCase							    =			model(__UpperCAmelCase      ,	**__UpperCAmelCase )[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

        # Test that model correctly compute the loss when we mask some positions
        __UpperCamelCase							    =			self._prepare_for_class(inputs_dict.copy()      ,	__UpperCAmelCase      ,	return_labels=__UpperCAmelCase )
        __UpperCamelCase							    =			{'input_ids', 'pixel_values', 'input_features'}
        __UpperCamelCase							    =			possible_input_names.intersection(set(__UpperCAmelCase ) ).pop()
        __UpperCamelCase							    =			prepared_for_class.pop(__UpperCAmelCase )
        if "labels" in prepared_for_class:
         __UpperCamelCase							    =			prepared_for_class['labels'].numpy()
         if len(labels.shape ) > 1 and labels.shape[1] != 1:
          # labels[0] = -100
          __UpperCamelCase							    =			tf.convert_to_tensor(__UpperCAmelCase )
          __UpperCamelCase							    =			model(__UpperCAmelCase      ,	**__UpperCAmelCase )[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
          self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )

                # Test that model correctly compute the loss with a dict
        __UpperCamelCase							    =			self._prepare_for_class(inputs_dict.copy()      ,	__UpperCAmelCase      ,	return_labels=__UpperCAmelCase )
        __UpperCamelCase							    =			model(__UpperCAmelCase )[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

        # Test that model correctly compute the loss with a tuple
        __UpperCamelCase							    =			self._prepare_for_class(inputs_dict.copy()      ,	__UpperCAmelCase      ,	return_labels=__UpperCAmelCase )

        # Get keys that were added with the _prepare_for_class function
        __UpperCamelCase							    =			prepared_for_class.keys() - inputs_dict.keys()
        __UpperCamelCase							    =			inspect.signature(model.call ).parameters
        __UpperCamelCase							    =			list(signature.keys() )

        # Create a dictionary holding the location of the tensors in the tuple
        __UpperCamelCase							    =			{0: input_name}
        for label_key in label_keys:
         __UpperCamelCase							    =			signature_names.index(__UpperCAmelCase )
         __UpperCamelCase							    =			label_key
        __UpperCamelCase							    =			sorted(tuple_index_mapping.items() )
        # Initialize a list with their default values, update the values and convert to a tuple
        __UpperCamelCase							    =			[]

        for name in signature_names:
         if name != "kwargs":
          list_input.append(signature[name].default )

        for index, value in sorted_tuple_index_mapping:
         __UpperCamelCase							    =			prepared_for_class[value]

        __UpperCamelCase							    =			tuple(__UpperCAmelCase )

        # Send to model
        __UpperCamelCase							    =			model(tuple_input[:-1] )[0]

        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )







     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase							    =			TFMobileViTModel.from_pretrained(__UpperCAmelCase )
       self.assertIsNotNone(__UpperCAmelCase )






def 				A		(							) ->      Optional[int]:
 __UpperCamelCase							    =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
 return image





@require_tf
class __lowerCAmelCase						( unittest.TestCase     ):





     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' )

      __UpperCamelCase							    =			MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' )
      __UpperCamelCase							    =			prepare_img()
      __UpperCamelCase							    =			image_processor(images=__UpperCAmelCase      ,	return_tensors='tf' )

      # forward pass
      __UpperCamelCase							    =			model(**__UpperCAmelCase      ,	training=__UpperCAmelCase )

      # verify the logits
      __UpperCamelCase							    =			tf.TensorShape((1, 1000) )
      self.assertEqual(outputs.logits.shape      ,	__UpperCAmelCase )

      __UpperCamelCase							    =			tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] )

      tf.debugging.assert_near(outputs.logits[0, :3]      ,	__UpperCAmelCase      ,	atol=1E-4      ,	rtol=1E-04 )







     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )

      __UpperCamelCase							    =			MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )

      __UpperCamelCase							    =			prepare_img()
      __UpperCamelCase							    =			image_processor(images=__UpperCAmelCase      ,	return_tensors='tf' )

      # forward pass
      __UpperCamelCase							    =			model(inputs.pixel_values      ,	training=__UpperCAmelCase )
      __UpperCamelCase							    =			outputs.logits

      # verify the logits
      __UpperCamelCase							    =			tf.TensorShape((1, 21, 32, 32) )
      self.assertEqual(logits.shape      ,	__UpperCAmelCase )

      __UpperCamelCase							    =			tf.constant(
          [
              [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
              [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
              [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
          ] )

      tf.debugging.assert_near(logits[0, :3, :3, :3]      ,	__UpperCAmelCase      ,	rtol=1E-4      ,	atol=1E-4 )





"""simple docstring"""

import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


UpperCamelCase      :							Any          =					{}
for task, _ in pipeline_test_mapping.items():
      UpperCamelCase      :							List[str]          =					{"pt": None, "tf": None}


# DO **NOT** add item to this set (unless the reason is approved)
UpperCamelCase      :							int          =					{
    "tests/models/esm/test_modeling_esmfold.py",  # The pipeline test mapping is added to `test_modeling_esm.py`
}






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

 if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  return "pt"
 elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  return "tf"
 elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  return "flax"
 else:
  return None






def 				A		(							snake_case :Union[str, Any]							,   snake_case :Union[str, Any]						) ->      Optional[Any]:
 # Use the cached results
 if PIPELINE_TEST_MAPPING[task].get(snake_case							,   snake_case						) is not None:
  return PIPELINE_TEST_MAPPING[task][framework]

 __UpperCamelCase							    =			pipeline_test_mapping[task]['test']
 __UpperCamelCase							    =			None

 if framework == "pt":
  __UpperCamelCase							    =			getattr(snake_case							,   'model_mapping'							,   snake_case						)
 elif framework == "tf":
  __UpperCamelCase							    =			getattr(snake_case							,   'tf_model_mapping'							,   snake_case						)

 if mapping is not None:
  __UpperCamelCase							    =			dict(mapping.items()						)

 # cache the results
 __UpperCamelCase							    =			mapping
 return mapping






def 				A		(							snake_case :int							,   snake_case :Tuple						) ->      List[Any]:
 __UpperCamelCase							    =			get_framework(snake_case						)
 if framework is None:
  return None
 __UpperCamelCase							    =			get_mapping_for_task(snake_case							,   snake_case						)
 if mapping is None:
  return None

 __UpperCamelCase							    =			list({model_class.config_class for model_class in test_class.all_model_classes}						)
 if len(snake_case						) != 1:
  raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'						)

 # This could be a list/tuple of model classes, but it's rare.
 __UpperCamelCase							    =			mapping.get(config_classes[0]							,   snake_case						)
 if isinstance(snake_case							,   (tuple, list)						):
  __UpperCamelCase							    =			sorted(snake_case							,   key=lambda snake_case						: x.__name__						)

 return model_class






def 				A		(							snake_case :List[Any]						) ->      Any:
 __UpperCamelCase							    =			[(task, get_model_for_pipeline_test(snake_case							,   snake_case						)) for task in pipeline_test_mapping]
 __UpperCamelCase							    =			sorted([(task, model) for task, model in mapping if model is not None]							,   key=lambda snake_case						: x[0]						)

 return dict(snake_case						)






def 				A		(							snake_case :Optional[int]						) ->      List[str]:
 __UpperCamelCase							    =			get_framework(snake_case						)
 if framework == "pt":
  __UpperCamelCase							    =			'torch'
 __UpperCamelCase							    =			'{}'

 __UpperCamelCase							    =			get_pipeline_model_mapping(snake_case						)
 if len(snake_case						) == 0:
  return ""

 __UpperCamelCase							    =			[]
 for task, model_classes in mapping.items():
  if isinstance(snake_case							,   (tuple, list)						):
   # A list/tuple of model classes
   __UpperCamelCase							    =			'(' + ', '.join([x.__name__ for x in model_classes]						) + ')'
  else:
   # A single model class
   __UpperCamelCase							    =			model_classes.__name__
  texts.append(f'"{task}": {value}'						)
 __UpperCamelCase							    =			'{' + ', '.join(snake_case						) + '}'
 __UpperCamelCase							    =			f'pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'

 return text






def 				A		(							snake_case :Optional[int]						) ->      Optional[int]:
 __UpperCamelCase							    =			{'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
 if not issubclass(snake_case							,   unittest.TestCase						):
  return False
 return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]						)						) > 0






def 				A		(							snake_case :Dict						) ->      Tuple:
 __UpperCamelCase							    =			[x for x in get_test_classes(snake_case						) if is_valid_test_class(snake_case						)]

 __UpperCamelCase							    =			None
 for test_class in test_classes:
  # If a test class has defined `pipeline_model_mapping`, let's take it
  if getattr(snake_case							,   'pipeline_model_mapping'							,   snake_case						) is not None:
   __UpperCamelCase							    =			test_class
   break
    # Take the test class with the shortest name (just a heuristic)
 if target_test_class is None and len(snake_case						) > 0:
  __UpperCamelCase							    =			sorted(snake_case							,   key=lambda snake_case						: (len(x.__name__						), x.__name__)						)[0]

 return target_test_class






def 				A		(							snake_case :Optional[int]							,   snake_case :List[str]							,   snake_case :int						) ->      List[Any]:
 __UpperCamelCase							    =			start_idx
 for idx, line in enumerate(lines[start_idx:]						):
  __UpperCamelCase							    =			len(snake_case						) - len(line.lstrip()						)
  if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
   __UpperCamelCase							    =			start_idx + idx
  elif idx > 0 and indent <= indent_level:
   # Outside the definition block of `pipeline_model_mapping`
   break

 return end_idx






def 				A		(							snake_case :List[str]							,   snake_case :str=False						) ->      List[str]:
 if getattr(snake_case							,   'pipeline_model_mapping'							,   snake_case						) is not None:
  if not overwrite:
   return "", -1

 __UpperCamelCase							    =			get_pipeline_model_mapping_string(snake_case						)
 if len(snake_case						) == 0:
  return "", -1
 __UpperCamelCase							    =			line_to_add + '\n'

 # The code defined the class `test_class`
 __UpperCamelCase    ,      __UpperCamelCase							    =			inspect.getsourcelines(snake_case						)
 # `inspect` gives the code for an object, including decorator(s) if any.
 # We (only) need the exact line of the class definition.
 for idx, line in enumerate(snake_case						):
  if line.lstrip().startswith('class '						):
   __UpperCamelCase							    =			class_lines[idx:]
   class_start_line_no += idx
   break
 __UpperCamelCase							    =			class_start_line_no + len(snake_case						) - 1

 # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
 # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
 # block that appears in a `test_class`.
 __UpperCamelCase							    =			None
 # The indent level of the line at `class_lines[start_idx]` (if defined)
 __UpperCamelCase							    =			0
 # To record if `pipeline_model_mapping` is found in `test_class`.
 __UpperCamelCase							    =			None
 for idx, line in enumerate(snake_case						):
  if line.strip().startswith('all_model_classes = '						):
   __UpperCamelCase							    =			len(snake_case						) - len(line.lstrip()						)
   __UpperCamelCase							    =			idx
  elif line.strip().startswith('all_generative_model_classes = '						):
   __UpperCamelCase							    =			len(snake_case						) - len(line.lstrip()						)
   __UpperCamelCase							    =			idx
  elif line.strip().startswith('pipeline_model_mapping = '						):
   __UpperCamelCase							    =			len(snake_case						) - len(line.lstrip()						)
   __UpperCamelCase							    =			idx
   __UpperCamelCase							    =			line
   break

 if start_idx is None:
  return "", -1
 # Find the ending index (inclusive) of the above found block.
 __UpperCamelCase							    =			find_block_ending(snake_case							,   snake_case							,   snake_case						)

 # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
 # `is_timm_available()` instead of `is_torch_available()`.
 # Keep leading and trailing whitespaces
 __UpperCamelCase							    =			re.compile(r'\s(is_\S+?_available\(\))\s'						)
 for line in class_lines[start_idx : end_idx + 1]:
  __UpperCamelCase							    =			r.search(snake_case						)
  if backend_condition is not None:
   # replace the leading and trailing whitespaces to the space character " ".
   __UpperCamelCase							    =			' ' + backend_condition[0][1:-1] + ' '
   __UpperCamelCase							    =			r.sub(snake_case							,   snake_case						)
   break

 if def_line is None:
  # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
  # `all_model_classes` or `all_generative_model_classes`.
  __UpperCamelCase							    =			end_idx
 else:
  # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
  __UpperCamelCase							    =			start_idx - 1
  # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
  for idx in range(snake_case							,   end_idx + 1						):
   # These lines are going to be removed before writing to the test file.
   __UpperCamelCase							    =			None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
 __UpperCamelCase							    =			[x.__name__ for x in test_class.__bases__]
 if "PipelineTesterMixin" not in parent_classes:
  # Put `PipelineTesterMixin` just before `unittest.TestCase`
  __UpperCamelCase							    =			[x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
  if "TestCase" in parent_classes:
   # Here we **assume** the original string is always with `unittest.TestCase`.
   _parent_classes.append('unittest.TestCase'						)
  __UpperCamelCase							    =			', '.join(_parent_classes						)
  for idx, line in enumerate(snake_case						):
   # Find the ending of the declaration of `test_class`
   if line.strip().endswith('):'						):
    # mark the lines of the declaration of `test_class` to be removed
    for _idx in range(idx + 1						):
     __UpperCamelCase							    =			None  # noqa
    break
        # Add the new, one-line, class declaration for `test_class`
  __UpperCamelCase							    =			f'class {test_class.__name__}({parent_classes}):\n'

 # Add indentation
 __UpperCamelCase							    =			' ' * indent_level + line_to_add
 # Insert `pipeline_model_mapping` to `class_lines`.
 # (The line at `target_idx` should be kept by definition!)
 __UpperCamelCase							    =			class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
 # Remove the lines that are marked to be removed
 __UpperCamelCase							    =			[x for x in class_lines if x is not None]

 # Move from test class to module (in order to write to the test file)
 __UpperCamelCase							    =			inspect.getsourcelines(inspect.getmodule(snake_case						)						)[0]
 # Be careful with the 1-off between line numbers and array indices
 __UpperCamelCase							    =			module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
 __UpperCamelCase							    =			''.join(snake_case						)

 __UpperCamelCase							    =			inspect.getsourcefile(snake_case						)
 with open(snake_case							,   'w'							,   encoding='UTF-8'							,   newline='\n'						) as fp:
  fp.write(snake_case						)

 return line_to_add






def 				A		(							snake_case :int							,   snake_case :str=False						) ->      Dict:
 __UpperCamelCase							    =			find_test_class(snake_case						)
 if test_class:
  add_pipeline_model_mapping(snake_case							,   overwrite=snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							Optional[int]          =					argparse.ArgumentParser()
      parser.add_argument(
          "--test_file", type=str, help="A path to the test file, starting with the repository's `tests` directory."
      )
      parser.add_argument(
          "--all",
          action="store_true",
          help="If to check and modify all test files.",
      )
      parser.add_argument(
          "--overwrite",
          action="store_true",
          help="If to overwrite a test class if it has already defined `pipeline_model_mapping`.",
      )
      UpperCamelCase      :							int          =					parser.parse_args()

      if not args.all and not args.test_file:
            raise ValueError("Please specify either `test_file` or pass `--all` to check/modify all test files.")
      elif args.all and args.test_file:
            raise ValueError("Only one of `--test_file` and `--all` could be specified.")

      UpperCamelCase      :							List[Any]          =					[]
      if args.test_file:
            UpperCamelCase      :							Union[str, Any]          =					[args.test_file]
      else:
            UpperCamelCase      :							List[Any]          =					os.path.join("tests", "models", "**", "test_modeling_*.py")
            for test_file in glob.glob(pattern):
                  # `Flax` is not concerned at this moment
                  if not test_file.startswith("test_modeling_flax_"):
                        test_files.append(test_file)

      for test_file in test_files:
            if test_file in TEST_FILE_TO_IGNORE:
                  print(f'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
                  continue
            add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)



