






'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowerCAmelCase         =						logging.get_logger(__name__)  # pylint: disable=invalid-name






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __init__(   self		:   Union[str, Any]			,				a__		:   Optional[Any]			,				a__		:   Tuple      ):
      super().__init__()
      self.register_modules(unet=a__			,				scheduler=a__      )



 @torch.no_grad()
 def __call__(   self		:   Tuple			,				a__		:   int = 1			,				a__		:   int = 100			,				a__		:   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				a__		:   Optional[float] = None			,				a__		:   bool = True			,				):

      if audio_length_in_s is None:
           __magic_name__						       =    self.unet.config.sample_size / self.unet.config.sample_rate

      __magic_name__						       =    audio_length_in_s * self.unet.config.sample_rate

      __magic_name__						       =    2 ** len(self.unet.up_blocks      )
      if sample_size < 3 * down_scale_factor:
           raise ValueError(
               F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
               F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''      )

      __magic_name__						       =    int(a__      )
      if sample_size % down_scale_factor != 0:
           __magic_name__						       =    (
               (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
           ) * down_scale_factor
           logger.info(
               F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
               F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
               ''' process.'''      )
      __magic_name__						       =    int(a__      )

      __magic_name__						       =    next(iter(self.unet.parameters()      )      ).dtype
      __magic_name__						       =    (batch_size, self.unet.config.in_channels, sample_size)
      if isinstance(a__			,				a__      ) and len(a__      ) != batch_size:
           raise ValueError(
               F'''You have passed a list of generators of length {len(a__      )}, but requested an effective batch'''
               F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

      __magic_name__						       =    randn_tensor(a__			,				generator=a__			,				device=self.device			,				dtype=a__      )

      # set step values
      self.scheduler.set_timesteps(a__			,				device=audio.device      )
      __magic_name__						       =    self.scheduler.timesteps.to(a__      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
           # 1. predict noise model_output
           __magic_name__						       =    self.unet(a__			,				a__      ).sample

           # 2. compute previous image: x_t -> t_t-1
           __magic_name__						       =    self.scheduler.step(a__			,				a__			,				a__      ).prev_sample

      __magic_name__						       =    audio.clamp(-1			,				1      ).float().cpu().numpy()

      __magic_name__						       =    audio[:, :, :original_sample_size]

      if not return_dict:
           return (audio,)

      return AudioPipelineOutput(audios=a__      )









'''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   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __SCREAMING_SNAKE_CASE					:Any      	=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Tuple			,				a__		:   int			,				a__		:   int      ):
      __magic_name__						       =    TextaTextGenerationPipeline(model=a__			,				tokenizer=a__      )
      return generator, ["Something to write", "Something else"]



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    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'''      )      )

      __magic_name__						       =    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__      )}],
          ]			,				)

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

      __magic_name__						       =    3
      __magic_name__						       =    generator(
          '''Something there'''			,				num_return_sequences=a__			,				num_beams=a__			,				)
      __magic_name__						       =    [
          {'''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__      )

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






