





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'''

     __snake_case   :     Any		     =      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __snake_case   :     int		     =      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TextaTextGenerationPipeline(model=lowerCamelCase__						,tokenizer=lowerCamelCase__	)
           return generator, ["Something to write", "Something else"]






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[str]:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[int]:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Union[str, Any]:




           '''simple docstring'''


           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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


