'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowercase          =  {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










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 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						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"""				)				)

           _A							:     List[Any]			=						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				)}],
               ]		,     )

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

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[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(_a		,     _a				)

           _A							:     Dict			=						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				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						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 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

