'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      _lowerCamelCase :				Any														= """platform"""

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def  _lowerCAmelCase				(				__a			,      __a			,      __a=None			,      __a=None      )  ->      List[Any]:
     '''simple docstring'''







     if attention_mask is None:
          _UpperCamelCase  :List[str]       =np.where(input_ids != config.pad_token_id			,      1			,      0      )
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }




@require_flax
class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=13					,      lowerCAmelCase__=7					,      lowerCAmelCase__=True					,      lowerCAmelCase__=False					,      lowerCAmelCase__=99					,      lowerCAmelCase__=16					,      lowerCAmelCase__=2					,      lowerCAmelCase__=4					,      lowerCAmelCase__=4					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=20					,      lowerCAmelCase__=2					,      lowerCAmelCase__=1					,      lowerCAmelCase__=0					,      lowerCAmelCase__=16					,      lowerCAmelCase__=16					,      lowerCAmelCase__=0.02					,      )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Dict       =parent
        _UpperCamelCase  :List[Any]       =batch_size
        _UpperCamelCase  :Dict       =seq_length
        _UpperCamelCase  :List[str]       =is_training
        _UpperCamelCase  :Optional[int]       =use_labels
        _UpperCamelCase  :int       =vocab_size
        _UpperCamelCase  :List[str]       =hidden_size
        _UpperCamelCase  :Union[str, Any]       =num_hidden_layers
        _UpperCamelCase  :Union[str, Any]       =num_attention_heads
        _UpperCamelCase  :Optional[Any]       =intermediate_size
        _UpperCamelCase  :Any       =hidden_act
        _UpperCamelCase  :Dict       =hidden_dropout_prob
        _UpperCamelCase  :int       =attention_probs_dropout_prob
        _UpperCamelCase  :Optional[int]       =max_position_embeddings
        _UpperCamelCase  :str       =eos_token_id
        _UpperCamelCase  :Tuple       =pad_token_id
        _UpperCamelCase  :Any       =bos_token_id
        _UpperCamelCase  :int       =embed_dim
        _UpperCamelCase  :List[str]       =word_embed_proj_dim
        _UpperCamelCase  :int       =initializer_range
        _UpperCamelCase  :Any       =False



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Any       =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,      self.vocab_size     )					,      3					,      self.vocab_size     )
        _UpperCamelCase  :Dict       =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,      dtype=np.intaa     ))					,      -1     )

        _UpperCamelCase  :Dict       =OPTConfig(
            vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      ffn_dim=self.intermediate_size					,      dropout=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      eos_token_id=self.eos_token_id					,      bos_token_id=self.bos_token_id					,      pad_token_id=self.pad_token_id					,      embed_dim=self.embed_dim					,      is_encoder_decoder=lowerCAmelCase__					,      word_embed_proj_dim=self.word_embed_proj_dim					,      initializer_range=self.initializer_range					,      use_cache=lowerCAmelCase__					,      )
        _UpperCamelCase  :List[Any]       =prepare_opt_inputs_dict(lowerCAmelCase__					,      lowerCAmelCase__     )
        return config, inputs_dict



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =self.prepare_config_and_inputs()
        return config, inputs_dict



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =20
        _UpperCamelCase  :List[Any]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :str       =inputs_dict["""input_ids"""]
        _UpperCamelCase  :str       =inputs_dict["""attention_mask"""]

        _UpperCamelCase  :Union[str, Any]       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =jnp.ones((input_ids.shape[0], max_length)					,      dtype="""i4"""     )

        _UpperCamelCase  :Dict       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)					,      )
        _UpperCamelCase  :str       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Tuple       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :Union[str, Any]       =model(
            input_ids[:, -1:]					,      attention_mask=lowerCAmelCase__					,      past_key_values=outputs_cache.past_key_values					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :List[Any]       =model(lowerCAmelCase__     )

        _UpperCamelCase  :List[Any]       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =20
        _UpperCamelCase  :Any       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase       ,      _UpperCamelCase  :int       =(
            inputs_dict["""input_ids"""],
            inputs_dict["""attention_mask"""],
        )

        _UpperCamelCase  :Tuple       =jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
            ]					,      axis=-1					,      )

        _UpperCamelCase  :int       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)					,      )

        _UpperCamelCase  :Union[str, Any]       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )
        _UpperCamelCase  :Any       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :Any       =model(
            input_ids[:, -1:]					,      past_key_values=outputs_cache.past_key_values					,      attention_mask=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Tuple       =model(lowerCAmelCase__					,      attention_mask=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




@require_flax
class    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase       ,				__snake_case     ):
   __UpperCAmelCase           =						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   __UpperCAmelCase           =						() if is_flax_available() else ()



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =FlaxOPTModelTester(self     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )




   @slow
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase  :Dict       =model_class_name.from_pretrained("""facebook/opt-125m"""     )
             _UpperCamelCase  :Optional[int]       =np.ones((1, 1)     ) * model.config.eos_token_id
             _UpperCamelCase  :Union[str, Any]       =model(lowerCAmelCase__     )
             self.assertIsNotNone(lowerCAmelCase__     )




@require_sentencepiece
@require_flax
class    lowerCamelCase__  (   unittest.TestCase     ):



   @slow
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =FlaxOPTModel.from_pretrained("""facebook/opt-350m"""     )
        _UpperCamelCase  :Union[str, Any]       =jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     )
        _UpperCamelCase  :Optional[Any]       =model(input_ids=lowerCAmelCase__     ).last_hidden_state
        _UpperCamelCase  :int       =(1, 11, 512)
        self.assertEqual(output.shape					,      lowerCAmelCase__     )
        _UpperCamelCase  :Optional[int]       =jnp.array(
            [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]     )
        self.assertTrue(jnp.allclose(output[:, :3, :3]					,      lowerCAmelCase__					,      atol=4e-2     )     )




@require_flax
@slow
class    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        super().setUp()
        _UpperCamelCase  :List[str]       ="""facebook/opt-350m"""




   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Any       =FlaxOPTForCausalLM.from_pretrained(self.path_model     )
        _UpperCamelCase  :Optional[Any]       =GPTaTokenizer.from_pretrained(self.path_model     )

        _UpperCamelCase  :List[str]       =[
            """Today is a beautiful day and I want to""",
            """In the city of""",
            """Paris is the capital of France and""",
            """Computers and mobile phones have taken""",
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        _UpperCamelCase  :int       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""					,      padding=lowerCAmelCase__					,      add_special_tokens=lowerCAmelCase__     )
        _UpperCamelCase  :int       =model(inputs.input_ids					,      attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
        _UpperCamelCase  :Optional[Any]       =jnp.array(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]     )
        self.assertTrue(jnp.allclose(lowerCAmelCase__					,      lowerCAmelCase__					,      atol=4e-2     )     )

        _UpperCamelCase  :Optional[int]       =jax.jit(lowerCAmelCase__     )
        _UpperCamelCase  :int       =model(inputs.input_ids					,      attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
        self.assertTrue(jnp.allclose(lowerCAmelCase__					,      lowerCAmelCase__					,      atol=4e-2     )     )




@require_flax
@slow
class    lowerCamelCase__  (   unittest.TestCase     ):



   @property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       ="""facebook/opt-125m"""

        _UpperCamelCase  :Dict       =[
            """Today is a beautiful day and I want to""",
            """In the city of New York, the city""",
            """Paris is the capital of France and the capital""",
            """Computers and mobile phones have taken over the""",
        ]

        _UpperCamelCase  :int       =[]

        _UpperCamelCase  :Union[str, Any]       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Tuple       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )

        for prompt in self.prompts:
             _UpperCamelCase  :Tuple       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""     ).input_ids

             _UpperCamelCase  :str       =model.generate(lowerCAmelCase__					,      max_length=10     )
             _UpperCamelCase  :str       =generated_ids[0]

             _UpperCamelCase  :Dict       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )
             predicted_outputs += generated_string

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       ="""facebook/opt-350m"""

        _UpperCamelCase  :List[str]       =[
            """Today is a beautiful day and I want to""",
            """In the city of San Francisco, the city""",
            """Paris is the capital of France and the capital""",
            """Computers and mobile phones have taken over the""",
        ]

        _UpperCamelCase  :Dict       =[]
        _UpperCamelCase  :Tuple       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :str       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )

        for prompt in self.prompts:
             _UpperCamelCase  :Dict       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""     ).input_ids

             _UpperCamelCase  :List[str]       =model.generate(lowerCAmelCase__					,      max_length=10     )
             _UpperCamelCase  :Tuple       =generated_ids[0]

             _UpperCamelCase  :List[str]       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )
             predicted_outputs += generated_string

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :int       ="""facebook/opt-125m"""
        _UpperCamelCase  :Union[str, Any]       =[
            """Today is a beautiful day and I want to thank""",
            """In the city of Rome Canaver Canaver Canaver Canaver""",
        ]
        _UpperCamelCase  :Any       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :str       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =tokenizer(
            [
                """Today is a beautiful day and I want to""",
                """In the city of""",
            ]					,      return_tensors="""jax"""					,      padding=lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =jax.jit(model.generate     )

        _UpperCamelCase  :Union[str, Any]       =jit_generate(inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]     ).sequences

        _UpperCamelCase  :List[Any]       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )

        self.assertIsNotNone(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :List[str]       ="""facebook/opt-350m"""

        _UpperCamelCase  :List[Any]       =GPTaTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       ="""left"""

        # use different length sentences to test batching
        _UpperCamelCase  :Tuple       =[
            """Hello, my dog is a little""",
            """Today, I""",
        ]

        _UpperCamelCase  :Tuple       =tokenizer(lowerCAmelCase__					,      return_tensors="""jax"""					,      padding=lowerCAmelCase__     )
        _UpperCamelCase  :Any       =inputs["""input_ids"""]

        _UpperCamelCase  :List[str]       =model.generate(input_ids=lowerCAmelCase__					,      attention_mask=inputs["""attention_mask"""]					,      trace=lowerCAmelCase__     )

        _UpperCamelCase  :int       =tokenizer(sentences[0]					,      return_tensors="""jax"""     ).input_ids
        _UpperCamelCase  :Any       =model.generate(input_ids=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       =inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
        _UpperCamelCase  :Dict       =tokenizer(sentences[1]					,      return_tensors="""jax"""     ).input_ids
        _UpperCamelCase  :Optional[Any]       =model.generate(input_ids=lowerCAmelCase__					,      max_length=model.config.max_length - num_paddings     )

        _UpperCamelCase  :int       =tokenizer.batch_decode(outputs[0]					,      skip_special_tokens=lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =tokenizer.decode(output_non_padded[0][0]					,      skip_special_tokens=lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.decode(output_padded[0][0]					,      skip_special_tokens=lowerCAmelCase__     )

        _UpperCamelCase  :Any       =[
            """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
            """Today, I was in the middle of a conversation with a friend about the""",
        ]
        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )
        self.assertListEqual(lowerCAmelCase__					,      [non_padded_sentence, padded_sentence]     )


'''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


_lowerCamelCase    =  {}
for task, _ in pipeline_test_mapping.items():
							_lowerCamelCase    =  {'pt': None, 'tf': None}


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






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):

	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 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	# Use the cached results
	if PIPELINE_TEST_MAPPING[task].get(snake_case_							,       snake_case_	) is not None:
		return PIPELINE_TEST_MAPPING[task][framework]

	_lowercase							=     pipeline_test_mapping[task]["""test"""]
	_lowercase							=     None

	if framework == "pt":
		_lowercase							=     getattr(snake_case_							,       """model_mapping"""							,       snake_case_	)
	elif framework == "tf":
		_lowercase							=     getattr(snake_case_							,       """tf_model_mapping"""							,       snake_case_	)

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

	# cache the results
	_lowercase							=     mapping
	return mapping






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	_lowercase							=     get_framework(snake_case_	)
	if framework is None:
		return None
	_lowercase							=     get_mapping_for_task(snake_case_							,       snake_case_	)
	if mapping is None:
		return None

	_lowercase							=     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.
	_lowercase							=     mapping.get(config_classes[0]							,       snake_case_	)
	if isinstance(snake_case_							,       (tuple, list)	):
		_lowercase							=     sorted(snake_case_							,       key=lambda snake_case_	: x.__name__	)

	return model_class






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     [(task, get_model_for_pipeline_test(snake_case_							,       snake_case_	)) for task in pipeline_test_mapping]
	_lowercase							=     sorted([(task, model) for task, model in mapping if model is not None]							,       key=lambda snake_case_	: x[0]	)

	return dict(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     get_framework(snake_case_	)
	if framework == "pt":
		_lowercase							=     """torch"""
	_lowercase							=     """{}"""

	_lowercase							=     get_pipeline_model_mapping(snake_case_	)
	if len(snake_case_	) == 0:
		return ""

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

	return text






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     {"""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 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     [x for x in get_test_classes(snake_case_	) if is_valid_test_class(snake_case_	)]

	_lowercase							=     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:
			_lowercase							=     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:
		_lowercase							=     sorted(snake_case_							,       key=lambda snake_case_	: (len(x.__name__	), x.__name__)	)[0]

	return target_test_class






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     start_idx
	for idx, line in enumerate(lines[start_idx:]	):
		_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
		if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
			_lowercase							=     start_idx + idx
		elif idx > 0 and indent <= indent_level:
			# Outside the definition block of `pipeline_model_mapping`
			break

	return end_idx






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False	):
	if getattr(snake_case_							,       """pipeline_model_mapping"""							,       snake_case_	) is not None:
		if not overwrite:
			return "", -1

	_lowercase							=     get_pipeline_model_mapping_string(snake_case_	)
	if len(snake_case_	) == 0:
		return "", -1
	_lowercase							=     line_to_add + """\n"""

	# The code defined the class `test_class`
	_lowercase		,		_lowercase							=     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 """	):
			_lowercase							=     class_lines[idx:]
			class_start_line_no += idx
			break
	_lowercase							=     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`.
	_lowercase							=     None
	# The indent level of the line at `class_lines[start_idx]` (if defined)
	_lowercase							=     0
	# To record if `pipeline_model_mapping` is found in `test_class`.
	_lowercase							=     None
	for idx, line in enumerate(snake_case_	):
		if line.strip().startswith("""all_model_classes = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
		elif line.strip().startswith("""all_generative_model_classes = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
		elif line.strip().startswith("""pipeline_model_mapping = """	):
			_lowercase							=     len(snake_case_	) - len(line.lstrip()	)
			_lowercase							=     idx
			_lowercase							=     line
			break

	if start_idx is None:
		return "", -1
	# Find the ending index (inclusive) of the above found block.
	_lowercase							=     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
	_lowercase							=     re.compile(r"""\s(is_\S+?_available\(\))\s"""	)
	for line in class_lines[start_idx : end_idx + 1]:
		_lowercase							=     r.search(snake_case_	)
		if backend_condition is not None:
			# replace the leading and trailing whitespaces to the space character " ".
			_lowercase							=     """ """ + backend_condition[0][1:-1] + """ """
			_lowercase							=     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`.
		_lowercase							=     end_idx
	else:
		# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
		_lowercase							=     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.
			_lowercase							=     None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
	_lowercase							=     [x.__name__ for x in test_class.__bases__]
	if "PipelineTesterMixin" not in parent_classes:
		# Put `PipelineTesterMixin` just before `unittest.TestCase`
		_lowercase							=     [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"""	)
		_lowercase							=     """, """.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	):
					_lowercase							=     None  # noqa
				break
        # Add the new, one-line, class declaration for `test_class`
		_lowercase							=     F"""class {test_class.__name__}({parent_classes}):\n"""

	# Add indentation
	_lowercase							=     """ """ * indent_level + line_to_add
	# Insert `pipeline_model_mapping` to `class_lines`.
	# (The line at `target_idx` should be kept by definition!)
	_lowercase							=     class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
	# Remove the lines that are marked to be removed
	_lowercase							=     [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)
	_lowercase							=     inspect.getsourcelines(inspect.getmodule(snake_case_	)	)[0]
	# Be careful with the 1-off between line numbers and array indices
	_lowercase							=     module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
	_lowercase							=     """""".join(snake_case_	)

	_lowercase							=     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 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False	):
	_lowercase							=     find_test_class(snake_case_	)
	if test_class:
		add_pipeline_model_mapping(snake_case_							,       overwrite=snake_case_	)


if __name__ == "__main__":
							_lowerCamelCase    =  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`.',
							)
							_lowerCamelCase    =  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.')

							_lowerCamelCase    =  []
							if args.test_file:
														_lowerCamelCase    =  [args.test_file]
							else:
														_lowerCamelCase    =  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)




