

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  UpperCAmelCase__	( unittest.TestCase ):






   """simple docstring"""


   a     =      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   a     =      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






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






   def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[str]      )	->							str:
        SCREAMING_SNAKE_CASE__					=					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'''      )      )

        SCREAMING_SNAKE_CASE__					=					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      )}],
            ]			,     )

        SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	int      )	->							str:
        SCREAMING_SNAKE_CASE__					=					pipeline('''text2text-generation'''			,     model='''patrickvonplaten/t5-tiny-random'''			,     framework='''pt'''      )
        # do_sample=False necessary for reproducibility
        SCREAMING_SNAKE_CASE__					=					generator('''Something there'''			,     do_sample=__lowerCamelCase      )
        self.assertEqual(__lowerCamelCase			,     [{'''generated_text''': ''''''}]      )

        SCREAMING_SNAKE_CASE__					=					3
        SCREAMING_SNAKE_CASE__					=					generator(
            '''Something there'''			,     num_return_sequences=__lowerCamelCase			,     num_beams=__lowerCamelCase			,     )
        SCREAMING_SNAKE_CASE__					=					[
            {'''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      )

        SCREAMING_SNAKE_CASE__					=					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      )},
            ]			,     )
        SCREAMING_SNAKE_CASE__					=					generator.model.config.eos_token_id
        SCREAMING_SNAKE_CASE__					=					'''<pad>'''
        SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[Any]      )	->							List[Any]:
        SCREAMING_SNAKE_CASE__					=					pipeline('''text2text-generation'''			,     model='''patrickvonplaten/t5-tiny-random'''			,     framework='''tf'''      )
        # do_sample=False necessary for reproducibility
        SCREAMING_SNAKE_CASE__					=					generator('''Something there'''			,     do_sample=__lowerCamelCase      )
        self.assertEqual(__lowerCamelCase			,     [{'''generated_text''': ''''''}]      )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


