





"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch





@is_pipeline_test
class        _lowerCamelCase      (       unittest.TestCase ):
						UpperCAmelCase_ =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						UpperCAmelCase_ =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   int:
								UpperCamelCase   =	TextaTextGenerationPipeline(model=__a   ,							tokenizer=__a		)
								return generator, ["Something to write", "Something else"]


						def     snake_case_   (self   ,							__a   ,							__a		) ->   Union[str, Any]:
								UpperCamelCase   =	generator("Something there"		)
								self.assertEqual(__a   ,							[{"generated_text": ANY(__a		)}]		)
								# These are encoder decoder, they don't just append to incoming string
								self.assertFalse(outputs[0]["generated_text"].startswith("Something there"		)		)

								UpperCamelCase   =	generator(["This is great !", "Something else"]   ,							num_return_sequences=2   ,							do_sample=__a		)
								self.assertEqual(
								    __a   ,							[
								        [{"generated_text": ANY(__a		)}, {"generated_text": ANY(__a		)}],
								        [{"generated_text": ANY(__a		)}, {"generated_text": ANY(__a		)}],
								    ]   ,							)

								UpperCamelCase   =	generator(
								    ["This is great !", "Something else"]   ,							num_return_sequences=2   ,							batch_size=2   ,							do_sample=__a		)
								self.assertEqual(
								    __a   ,							[
								        [{"generated_text": ANY(__a		)}, {"generated_text": ANY(__a		)}],
								        [{"generated_text": ANY(__a		)}, {"generated_text": ANY(__a		)}],
								    ]   ,							)

								with self.assertRaises(__a		):
										generator(4		)


						@require_torch
						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	pipeline("text2text-generation"   ,							model="patrickvonplaten/t5-tiny-random"   ,							framework="pt"		)
								# do_sample=False necessary for reproducibility
								UpperCamelCase   =	generator("Something there"   ,							do_sample=__a		)
								self.assertEqual(__a   ,							[{"generated_text": ""}]		)

								UpperCamelCase   =	3
								UpperCamelCase   =	generator(
								    "Something there"   ,							num_return_sequences=__a   ,							num_beams=__a   ,							)
								UpperCamelCase   =	[
								    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
								    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
								    {"generated_text": ""},
								]
								self.assertEqual(__a   ,							__a		)

								UpperCamelCase   =	generator("This is a test"   ,							do_sample=__a   ,							num_return_sequences=2   ,							return_tensors=__a		)
								self.assertEqual(
								    __a   ,							[
								        {"generated_token_ids": ANY(torch.Tensor		)},
								        {"generated_token_ids": ANY(torch.Tensor		)},
								    ]   ,							)
								UpperCamelCase   =	generator.model.config.eos_token_id
								UpperCamelCase   =	"<pad>"
								UpperCamelCase   =	generator(
								    ["This is a test", "This is a second test"]   ,							do_sample=__a   ,							num_return_sequences=2   ,							batch_size=2   ,							return_tensors=__a   ,							)
								self.assertEqual(
								    __a   ,							[
								        [
								            {"generated_token_ids": ANY(torch.Tensor		)},
								            {"generated_token_ids": ANY(torch.Tensor		)},
								        ],
								        [
								            {"generated_token_ids": ANY(torch.Tensor		)},
								            {"generated_token_ids": ANY(torch.Tensor		)},
								        ],
								    ]   ,							)




						@require_tf
						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	pipeline("text2text-generation"   ,							model="patrickvonplaten/t5-tiny-random"   ,							framework="tf"		)
								# do_sample=False necessary for reproducibility
								UpperCamelCase   =	generator("Something there"   ,							do_sample=__a		)
								self.assertEqual(__a   ,							[{"generated_text": ""}]		)













"""simple docstring"""





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	test_file.split(os.path.sep    )
		if components[0:2] != ["tests", "models"]:
				raise ValueError(
				    "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
				    F"{test_file} instead."    )
		UpperCamelCase   =	components[-1]
		if not test_fn.endswith("py"    ):
				raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."    )
		if not test_fn.startswith("test_modeling_"    ):
				raise ValueError(
				    F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."    )

		UpperCamelCase   =	components[:-1] + [test_fn.replace(".py"  ,	""    )]
		UpperCamelCase   =	".".join(_SCREAMING_SNAKE_CASE    )

		return test_module_path


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_module_path(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	importlib.import_module(_SCREAMING_SNAKE_CASE    )

		return test_module


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		UpperCamelCase   =	get_test_module(_SCREAMING_SNAKE_CASE    )
		for attr in dir(_SCREAMING_SNAKE_CASE    ):
				if attr.endswith("ModelTester"    ):
						tester_classes.append(getattr(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		UpperCamelCase   =	get_test_module(_SCREAMING_SNAKE_CASE    )
		for attr in dir(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
				# `all_model_classes` is not empty (which also excludes other special classes).
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	"all_model_classes"  ,	[]    )
				if len(_SCREAMING_SNAKE_CASE    ) > 0:
						test_classes.append(_SCREAMING_SNAKE_CASE    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	set()
		for test_class in test_classes:
				model_classes.update(test_class.all_model_classes    )

		# sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	test_class()
		if hasattr(_SCREAMING_SNAKE_CASE  ,	"setUp"    ):
				test.setUp()

		UpperCamelCase   =	None
		if hasattr(_SCREAMING_SNAKE_CASE  ,	"model_tester"    ):
				# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
				if test.model_tester is not None:
						UpperCamelCase   =	test.model_tester.__class__

		return model_tester


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	[]
		for test_class in test_classes:
				if model_class in test_class.all_model_classes:
						target_test_classes.append(_SCREAMING_SNAKE_CASE    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes_for_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	[]
		for test_class in test_classes:
				UpperCamelCase   =	get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE    )
				if tester_class is not None:
						tester_classes.append(_SCREAMING_SNAKE_CASE    )

    # sort with class names
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : x.__name__    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_test_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	{test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE    ) for test_class in test_classes}
		return test_tester_mapping


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_model_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	{
		    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) for model_class in model_classes
		}
		return model_test_mapping


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_model_classes(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	{
		    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ) for model_class in model_classes
		}
		return model_to_tester_mapping






def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				return o
		elif isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				return o.__name__
		elif isinstance(_SCREAMING_SNAKE_CASE  ,	(list, tuple)    ):
				return [to_json(_SCREAMING_SNAKE_CASE    ) for x in o]
		elif isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				return {to_json(_SCREAMING_SNAKE_CASE    ): to_json(_SCREAMING_SNAKE_CASE    ) for k, v in o.items()}
		else:
				return o






