





'''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
					_UpperCamelCase											=			'''platform'''

					import jax
					import jax.numpy as jnp

					from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def   lowercase_	(   lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  Tuple=None			,							lowerCAmelCase__	:  List[str]=None ):

			"""simple docstring"""





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



@require_flax
class _A  :





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

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





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size				) ,       3 ,       self.vocab_size				)
					__UpperCAmelCase   :      Optional[int]										=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,       dtype=np.intaa				)) ,       -1				)

					__UpperCAmelCase   :      List[Any]										=     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=__UpperCAmelCase ,       word_embed_proj_dim=self.word_embed_proj_dim ,       initializer_range=self.initializer_range ,       use_cache=__UpperCAmelCase ,       )
					__UpperCAmelCase   :      Dict										=     prepare_opt_inputs_dict(__UpperCAmelCase ,       __UpperCAmelCase				)
					return config, inputs_dict





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     self.prepare_config_and_inputs()
					return config, inputs_dict





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     20
					__UpperCAmelCase   :      Any										=     model_class_name(__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     inputs_dict["""input_ids"""]
					__UpperCAmelCase   :      str										=     inputs_dict["""attention_mask"""]

					__UpperCAmelCase   :      List[Any]										=     model.init_cache(input_ids.shape[0] ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     jnp.ones((input_ids.shape[0], max_length) ,       dtype="""i4"""				)

					__UpperCAmelCase   :      List[Any]										=     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=__UpperCAmelCase ,       past_key_values=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )

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

					__UpperCAmelCase   :      Tuple										=     model(__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[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 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     20
					__UpperCAmelCase   :      Any										=     model_class_name(__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[Any]										=     (
					    inputs_dict["""input_ids"""],
					    inputs_dict["""attention_mask"""],
					)

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

					__UpperCAmelCase   :      Tuple										=     model.init_cache(input_ids.shape[0] ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[int]										=     jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1) ,       )

					__UpperCAmelCase   :      Dict										=     model(
					    input_ids[:, :-1] ,       attention_mask=__UpperCAmelCase ,       past_key_values=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )
					__UpperCAmelCase   :      Optional[int]										=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype="""i4"""				)
					__UpperCAmelCase   :      Optional[Any]										=     model(
					    input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Optional[int]										=     model(__UpperCAmelCase ,       attention_mask=__UpperCAmelCase				)

					__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 _A  (    __SCREAMING_SNAKE_CASE	,				unittest.TestCase	,				__SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    str						      =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		_SCREAMING_SNAKE_CASE					:    List[str]						      =			() if is_flax_available() else ()





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     FlaxOPTModelTester(self				)





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					__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(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)





		@slow
		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					for model_class_name in self.all_model_classes:
								__UpperCAmelCase   :      Dict										=     model_class_name.from_pretrained("""facebook/opt-125m"""				)
								__UpperCAmelCase   :      List[Any]										=     np.ones((1, 1)				) * model.config.eos_token_id
								__UpperCAmelCase   :      Tuple										=     model(__UpperCAmelCase				)
								self.assertIsNotNone(__UpperCAmelCase				)



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





		@slow
		def 							__A       (     self				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     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   :      Dict										=     model(input_ids=__UpperCAmelCase				).last_hidden_state
					__UpperCAmelCase   :      Dict										=     (1, 11, 512)
					self.assertEqual(output.shape ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     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] ,       __UpperCAmelCase ,       atol=4E-2				)				)



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





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					super().setUp()
					__UpperCAmelCase   :      Dict										=     """facebook/opt-350m"""





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     FlaxOPTForCausalLM.from_pretrained(self.path_model				)
					__UpperCAmelCase   :      Tuple										=     GPTaTokenizer.from_pretrained(self.path_model				)

					__UpperCAmelCase   :      Dict										=     [
					    """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   :      List[Any]										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax""" ,       padding=__UpperCAmelCase ,       add_special_tokens=__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     model(inputs.input_ids ,       attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
					__UpperCAmelCase   :      List[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(__UpperCAmelCase ,       __UpperCAmelCase ,       atol=4E-2				)				)

					__UpperCAmelCase   :      Optional[Any]										=     jax.jit(__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     model(inputs.input_ids ,       attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
					self.assertTrue(jnp.allclose(__UpperCAmelCase ,       __UpperCAmelCase ,       atol=4E-2				)				)



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





		@property
		def 							__A       (     self				)       ->     List[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 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     """facebook/opt-125m"""

					__UpperCAmelCase   :      Union[str, Any]										=     [
					    """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   :      Tuple										=     []

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

					for prompt in self.prompts:
								__UpperCAmelCase   :      Any										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax"""				).input_ids

								__UpperCAmelCase   :      Dict										=     model.generate(__UpperCAmelCase ,       max_length=10				)
								__UpperCAmelCase   :      Union[str, Any]										=     generated_ids[0]

								__UpperCAmelCase   :      str										=     tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)
								predicted_outputs += generated_string

					self.assertListEqual(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     """facebook/opt-350m"""

					__UpperCAmelCase   :      List[Any]										=     [
					    """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   :      Tuple										=     []
					__UpperCAmelCase   :      Any										=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase				)
					__UpperCAmelCase   :      List[str]										=     GPTaTokenizer.from_pretrained(__UpperCAmelCase				)

					for prompt in self.prompts:
								__UpperCAmelCase   :      int										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax"""				).input_ids

								__UpperCAmelCase   :      List[str]										=     model.generate(__UpperCAmelCase ,       max_length=10				)
								__UpperCAmelCase   :      Optional[Any]										=     generated_ids[0]

								__UpperCAmelCase   :      Union[str, Any]										=     tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)
								predicted_outputs += generated_string

					self.assertListEqual(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     Dict:

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

					__UpperCAmelCase   :      Any										=     jax.jit(model.generate				)

					__UpperCAmelCase   :      int										=     jit_generate(inputs["""input_ids"""] ,       attention_mask=inputs["""attention_mask"""]				).sequences

					__UpperCAmelCase   :      Union[str, Any]										=     tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)

					self.assertIsNotNone(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     """facebook/opt-350m"""

					__UpperCAmelCase   :      str										=     GPTaTokenizer.from_pretrained(__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[int]										=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     """left"""

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

					__UpperCAmelCase   :      Optional[Any]										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax""" ,       padding=__UpperCAmelCase				)
					__UpperCAmelCase   :      Union[str, Any]										=     inputs["""input_ids"""]

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

					__UpperCAmelCase   :      Optional[int]										=     tokenizer(sentences[0] ,       return_tensors="""jax"""				).input_ids
					__UpperCAmelCase   :      Optional[int]										=     model.generate(input_ids=__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
					__UpperCAmelCase   :      Any										=     tokenizer(sentences[1] ,       return_tensors="""jax"""				).input_ids
					__UpperCAmelCase   :      int										=     model.generate(input_ids=__UpperCAmelCase ,       max_length=model.config.max_length - num_paddings				)

					__UpperCAmelCase   :      List[Any]										=     tokenizer.batch_decode(outputs[0] ,       skip_special_tokens=__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     tokenizer.decode(output_non_padded[0][0] ,       skip_special_tokens=__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     tokenizer.decode(output_padded[0][0] ,       skip_special_tokens=__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[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(__UpperCAmelCase ,       __UpperCAmelCase				)
					self.assertListEqual(__UpperCAmelCase ,       [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


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


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


def   lowercase_	(   lowerCAmelCase__	:  Optional[Any] ):

			"""simple docstring"""





			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   lowercase_	(   lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  Any ):

			"""simple docstring"""





			if PIPELINE_TEST_MAPPING[task].get(lowerCAmelCase__			,							lowerCAmelCase__ ) is not None:
						return PIPELINE_TEST_MAPPING[task][framework]

			__UpperCAmelCase   :      Optional[int]										=     pipeline_test_mapping[task]["""test"""]
			__UpperCAmelCase   :      Optional[Any]										=     None

			if framework == "pt":
						__UpperCAmelCase   :      List[str]										=     getattr(lowerCAmelCase__			,							"""model_mapping"""			,							lowerCAmelCase__ )
			elif framework == "tf":
						__UpperCAmelCase   :      List[Any]										=     getattr(lowerCAmelCase__			,							"""tf_model_mapping"""			,							lowerCAmelCase__ )

			if mapping is not None:
						__UpperCAmelCase   :      Union[str, Any]										=     dict(mapping.items() )

			# cache the results
			__UpperCAmelCase   :      Union[str, Any]										=     mapping
			return mapping


def   lowercase_	(   lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Tuple ):

			"""simple docstring"""





			__UpperCAmelCase   :      int										=     get_framework(lowerCAmelCase__ )
			if framework is None:
						return None
			__UpperCAmelCase   :      Tuple										=     get_mapping_for_task(lowerCAmelCase__			,							lowerCAmelCase__ )
			if mapping is None:
						return None

			__UpperCAmelCase   :      Union[str, Any]										=     list({model_class.config_class for model_class in test_class.all_model_classes} )
			if len(lowerCAmelCase__ ) != 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   :      Union[str, Any]										=     mapping.get(config_classes[0]			,							lowerCAmelCase__ )
			if isinstance(lowerCAmelCase__			,							(tuple, list) ):
						__UpperCAmelCase   :      int										=     sorted(lowerCAmelCase__			,							key=lambda lowerCAmelCase__ : x.__name__ )

			return model_class


def   lowercase_	(   lowerCAmelCase__	:  int ):

			"""simple docstring"""





			__UpperCAmelCase   :      Any										=     [(task, get_model_for_pipeline_test(lowerCAmelCase__			,							lowerCAmelCase__ )) for task in pipeline_test_mapping]
			__UpperCAmelCase   :      int										=     sorted([(task, model) for task, model in mapping if model is not None]			,							key=lambda lowerCAmelCase__ : x[0] )

			return dict(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  Tuple ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[str]										=     get_framework(lowerCAmelCase__ )
			if framework == "pt":
						__UpperCAmelCase   :      str										=     """torch"""
			__UpperCAmelCase   :      str										=     """{}"""

			__UpperCAmelCase   :      Optional[int]										=     get_pipeline_model_mapping(lowerCAmelCase__ )
			if len(lowerCAmelCase__ ) == 0:
						return ""

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

			return text


def   lowercase_	(   lowerCAmelCase__	:  Tuple ):

			"""simple docstring"""





			__UpperCAmelCase   :      Dict										=     {"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
			if not issubclass(lowerCAmelCase__			,							unittest.TestCase ):
						return False
			return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__] ) ) > 0


def   lowercase_	(   lowerCAmelCase__	:  Optional[Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Optional[int]										=     [x for x in get_test_classes(lowerCAmelCase__ ) if is_valid_test_class(lowerCAmelCase__ )]

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

			return target_test_class


def   lowercase_	(   lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  Optional[int]			,							lowerCAmelCase__	:  List[Any] ):

			"""simple docstring"""





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

			return end_idx


def   lowercase_	(   lowerCAmelCase__	:  str			,							lowerCAmelCase__	:  Optional[Any]=False ):

			"""simple docstring"""





			if getattr(lowerCAmelCase__			,							"""pipeline_model_mapping"""			,							lowerCAmelCase__ ) is not None:
						if not overwrite:
									return "", -1

			__UpperCAmelCase   :      Dict										=     get_pipeline_model_mapping_string(lowerCAmelCase__ )
			if len(lowerCAmelCase__ ) == 0:
						return "", -1
			__UpperCAmelCase   :      str										=     line_to_add + """\n"""

			# The code defined the class `test_class`
			__UpperCAmelCase				,  __UpperCAmelCase   :      Optional[Any]										=     inspect.getsourcelines(lowerCAmelCase__ )
			# `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(lowerCAmelCase__ ):
						if line.lstrip().startswith("""class """ ):
									__UpperCAmelCase   :      List[Any]										=     class_lines[idx:]
									class_start_line_no += idx
									break
			__UpperCAmelCase   :      List[str]										=     class_start_line_no + len(lowerCAmelCase__ ) - 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   :      str										=     None
			# The indent level of the line at `class_lines[start_idx]` (if defined)
			__UpperCAmelCase   :      List[Any]										=     0
			# To record if `pipeline_model_mapping` is found in `test_class`.
			__UpperCAmelCase   :      Any										=     None
			for idx, line in enumerate(lowerCAmelCase__ ):
						if line.strip().startswith("""all_model_classes = """ ):
									__UpperCAmelCase   :      Optional[Any]										=     len(lowerCAmelCase__ ) - len(line.lstrip() )
									__UpperCAmelCase   :      List[str]										=     idx
						elif line.strip().startswith("""all_generative_model_classes = """ ):
									__UpperCAmelCase   :      Union[str, Any]										=     len(lowerCAmelCase__ ) - len(line.lstrip() )
									__UpperCAmelCase   :      Optional[int]										=     idx
						elif line.strip().startswith("""pipeline_model_mapping = """ ):
									__UpperCAmelCase   :      List[str]										=     len(lowerCAmelCase__ ) - len(line.lstrip() )
									__UpperCAmelCase   :      Optional[int]										=     idx
									__UpperCAmelCase   :      Dict										=     line
									break

			if start_idx is None:
						return "", -1
			# Find the ending index (inclusive) of the above found block.
			__UpperCAmelCase   :      Optional[int]										=     find_block_ending(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )

			# 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   :      str										=     re.compile(r"""\s(is_\S+?_available\(\))\s""" )
			for line in class_lines[start_idx : end_idx + 1]:
						__UpperCAmelCase   :      List[Any]										=     r.search(lowerCAmelCase__ )
						if backend_condition is not None:
									# replace the leading and trailing whitespaces to the space character " ".
									__UpperCAmelCase   :      int										=     """ """ + backend_condition[0][1:-1] + """ """
									__UpperCAmelCase   :      Tuple										=     r.sub(lowerCAmelCase__			,							lowerCAmelCase__ )
									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   :      int										=     end_idx
			else:
						# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
						__UpperCAmelCase   :      Optional[int]										=     start_idx - 1
						# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
						for idx in range(lowerCAmelCase__			,							end_idx + 1 ):
									# These lines are going to be removed before writing to the test file.
									__UpperCAmelCase   :      int										=     None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
			__UpperCAmelCase   :      Any										=     [x.__name__ for x in test_class.__bases__]
			if "PipelineTesterMixin" not in parent_classes:
						# Put `PipelineTesterMixin` just before `unittest.TestCase`
						__UpperCAmelCase   :      Optional[Any]										=     [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   :      List[Any]										=     """, """.join(_parent_classes )
						for idx, line in enumerate(lowerCAmelCase__ ):
									# 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   :      int										=     None  # noqa
												break
        # Add the new, one-line, class declaration for `test_class`
						__UpperCAmelCase   :      List[Any]										=     f'class {test_class.__name__}({parent_classes}):\n'

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

			__UpperCAmelCase   :      str										=     inspect.getsourcefile(lowerCAmelCase__ )
			with open(lowerCAmelCase__			,							"""w"""			,							encoding="""UTF-8"""			,							newline="""\n""" ) as fp:
						fp.write(lowerCAmelCase__ )

			return line_to_add


def   lowercase_	(   lowerCAmelCase__	:  Union[str, Any]			,							lowerCAmelCase__	:  Optional[int]=False ):

			"""simple docstring"""





			__UpperCAmelCase   :      int										=     find_test_class(lowerCAmelCase__ )
			if test_class:
						add_pipeline_model_mapping(lowerCAmelCase__			,							overwrite=lowerCAmelCase__ )


if __name__ == "__main__":
					_UpperCamelCase											=			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											=			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											=			[]
					if args.test_file:
										_UpperCamelCase											=			[args.test_file]
					else:
										_UpperCamelCase											=			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)




