


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     a		(unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase   :							Optional[Any]					  =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Union[str, Any]    )     ->   Tuple:
														__snake_case		:      Dict										=       TextaTextGenerationPipeline(model=lowerCamelCase   ,     tokenizer=lowerCamelCase    )
														return generator, ["Something to write", "Something else"]

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

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

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

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

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

														__snake_case		:      Optional[Any]										=       3
														__snake_case		:      Optional[Any]										=       generator(
														    "Something there"   ,     num_return_sequences=lowerCamelCase   ,     num_beams=lowerCamelCase   ,     )
														__snake_case		:      int										=       [
														    {"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(lowerCamelCase   ,     lowerCamelCase    )

														__snake_case		:      Optional[int]										=       generator("This is a test"   ,     do_sample=lowerCamelCase   ,     num_return_sequences=2   ,     return_tensors=lowerCamelCase    )
														self.assertEqual(
														    lowerCamelCase   ,     [
														        {"generated_token_ids": ANY(torch.Tensor    )},
														        {"generated_token_ids": ANY(torch.Tensor    )},
														    ]   ,     )
														__snake_case		:      Tuple										=       generator.model.config.eos_token_id
														__snake_case		:      List[str]										=       "<pad>"
														__snake_case		:      Union[str, Any]										=       generator(
														    ["This is a test", "This is a second test"]   ,     do_sample=lowerCamelCase   ,     num_return_sequences=2   ,     batch_size=2   ,     return_tensors=lowerCamelCase   ,     )
														self.assertEqual(
														    lowerCamelCase   ,     [
														        [
														            {"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	:  str    )     ->   str:
														__snake_case		:      int										=       pipeline("text2text-generation"   ,     model="patrickvonplaten/t5-tiny-random"   ,     framework="tf"    )
														# do_sample=False necessary for reproducibility
														__snake_case		:      Optional[Any]										=       generator("Something there"   ,     do_sample=lowerCamelCase    )
														self.assertEqual(lowerCamelCase   ,     [{"generated_text": ""}]    )



from ..utils import DummyObject, requires_backends

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  ["speech"]

							def __init__(							self	:  List[Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														requires_backends(self   ,     ["speech"]    )

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  ["speech"]

							def __init__(							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  List[Any]    )     ->   Optional[int]:
														requires_backends(self   ,     ["speech"]    )

