

import torch

from diffusers import DiffusionPipeline



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Tuple    ,						UpperCamelCase: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         super().__init__()

         self.register_modules(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )





    def __call__(     self: Optional[int]       ):






         """simple docstring"""


         A__										=  torch.randn(
             (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,						)
         A__										=  1

         A__										=  self.unet(UpperCamelCase    ,						UpperCamelCase       ).sample
         A__										=  self.scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       ).prev_sample

         A__										=  scheduler_output - scheduler_output + torch.ones_like(UpperCamelCase       )

         return result









"""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 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

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

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

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

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


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

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""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(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""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: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






