
"""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__    :							Optional[Any]									=		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     UpperCAmelCase__    :							List[Any]									=		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   int       )						->						Optional[Any]:
         snake_case								= TextaTextGenerationPipeline(model=A__    ,   tokenizer=A__       )
         return generator, ["Something to write", "Something else"]





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   int    ,   _A				:   int       )						->						Dict:
         snake_case								= 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"       )       )

         snake_case								= 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__       )}],
             ]    ,   )

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

         snake_case								= 3
         snake_case								= generator(
             "Something there"    ,   num_return_sequences=A__    ,   num_beams=A__    ,   )
         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(A__    ,   A__       )

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



from ....utils import logging


_A   =			logging.get_logger(__name__)







class 					lowerCamelCase		(  A_  ):





     def __init__(self				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   Tuple=None    ,   _A				:   Union[str, Any]=2_0_4_8       )						->						List[Any]:
         snake_case								= config.__dict__
         snake_case								= modal_hidden_size
         if num_labels:
             snake_case								= num_labels

