





"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__UpperCamelCase     :						Union[str, Any]						       =  logging.get_logger(__name__)


__UpperCamelCase     :						int						       =  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__UpperCamelCase     :						Dict						       =  {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

__UpperCamelCase     :						str						       =  {'''facebook/blenderbot-3B''': 128}







class       a							(   a__  ):
 snake_case__             =       VOCAB_FILES_NAMES
 snake_case__             =       PRETRAINED_VOCAB_FILES_MAP
 snake_case__             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case__             =       ['''input_ids''', '''attention_mask''']
 snake_case__             =       BlenderbotTokenizer



 def __init__(     self				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case="replace"				,       _snake_case="<s>"				,       _snake_case="</s>"				,       _snake_case="</s>"				,       _snake_case="<s>"				,       _snake_case="<unk>"				,       _snake_case="<pad>"				,       _snake_case="<mask>"				,       _snake_case=False				,       _snake_case=True				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(
      _snake_case				,       _snake_case				,       tokenizer_file=_snake_case				,       errors=_snake_case				,       bos_token=_snake_case				,       eos_token=_snake_case				,       sep_token=_snake_case				,       cls_token=_snake_case				,       unk_token=_snake_case				,       pad_token=_snake_case				,       mask_token=_snake_case				,       add_prefix_space=_snake_case				,       trim_offsets=_snake_case				,       **_snake_case				,       )

  lowerCAmelCase    				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
  if pre_tok_state.get('add_prefix_space'				,       _snake_case     ) != add_prefix_space:
   lowerCAmelCase    				= getattr(_snake_case				,       pre_tok_state.pop('type'     )     )
   lowerCAmelCase    				= add_prefix_space
   lowerCAmelCase    				= pre_tok_class(**_snake_case     )

  lowerCAmelCase    				= add_prefix_space

  lowerCAmelCase    				= 'post_processor'
  lowerCAmelCase    				= getattr(self.backend_tokenizer				,       _snake_case				,       _snake_case     )
  if tokenizer_component_instance:
   lowerCAmelCase    				= json.loads(tokenizer_component_instance.__getstate__()     )

   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
   if "sep" in state:
    lowerCAmelCase    				= tuple(state['sep']     )
   if "cls" in state:
    lowerCAmelCase    				= tuple(state['cls']     )

   lowerCAmelCase    				= False

   if state.get('add_prefix_space'				,       _snake_case     ) != add_prefix_space:
    lowerCAmelCase    				= add_prefix_space
    lowerCAmelCase    				= True

   if state.get('trim_offsets'				,       _snake_case     ) != trim_offsets:
    lowerCAmelCase    				= trim_offsets
    lowerCAmelCase    				= True

   if changes_to_apply:
    lowerCAmelCase    				= getattr(_snake_case				,       state.pop('type'     )     )
    lowerCAmelCase    				= component_class(**_snake_case     )
    setattr(self.backend_tokenizer				,       _snake_case				,       _snake_case     )



 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self._mask_token is None:
   if self.verbose:
    logger.error('Using mask_token, but it is not set yet.'     )
   return None
  return str(self._mask_token     )



 @mask_token.setter
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case     ) if isinstance(_snake_case				,       _snake_case     ) else value
  lowerCAmelCase    				= value



 def    UpperCamelCase__  (     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= kwargs.get('is_split_into_words'				,       _snake_case     )
  assert self.add_prefix_space or not is_split_into_words, (
      F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
      "to use it with pretokenized inputs."
  )

  return super()._batch_encode_plus(*_snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= kwargs.get('is_split_into_words'				,       _snake_case     )

  assert self.add_prefix_space or not is_split_into_words, (
      F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
      "to use it with pretokenized inputs."
  )

  return super()._encode_plus(*_snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= self._tokenizer.model.save(_snake_case				,       name=_snake_case     )
  return tuple(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= [self.sep_token_id]
  lowerCAmelCase    				= [self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep     ) * [0]
  return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  return token_ids_a + [self.eos_token_id]



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  for is_user, text in conversation.iter_texts():
   if is_user:
    # We need to space prefix as it's being done within blenderbot
    inputs.append(' ' + text     )
   else:
    # Generated responses should contain them already.
    inputs.append(_snake_case     )

  lowerCAmelCase    				= '  '.join(_snake_case     )
  lowerCAmelCase    				= self.encode(_snake_case     )
  if len(_snake_case     ) > self.model_max_length:
   lowerCAmelCase    				= input_ids[-self.model_max_length :]
   logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'     )
  return input_ids












"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class       a							(   a__   , unittest.TestCase  ):
 snake_case__             =       DanceDiffusionPipeline
 snake_case__             =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {
     '''callback''',
     '''latents''',
     '''callback_steps''',
     '''output_type''',
     '''num_images_per_prompt''',
 }
 snake_case__             =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
 snake_case__             =       False
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= UNetaDModel(
      block_out_channels=(32, 32, 64)				,       extra_in_channels=16				,       sample_size=5_12				,       sample_rate=1_60_00				,       in_channels=2				,       out_channels=2				,       flip_sin_to_cos=_snake_case				,       use_timestep_embedding=_snake_case				,       time_embedding_type='fourier'				,       mid_block_type='UNetMidBlock1D'				,       down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')				,       up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')				,       )
  lowerCAmelCase    				= IPNDMScheduler()

  lowerCAmelCase    				= {
      'unet': unet,
      'scheduler': scheduler,
  }
  return components



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )
  lowerCAmelCase    				= {
      'batch_size': 1,
      'generator': generator,
      'num_inference_steps': 4,
  }
  return inputs



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 'cpu'  # ensure determinism for the device-dependent torch.Generator
  lowerCAmelCase    				= self.get_dummy_components()
  lowerCAmelCase    				= DanceDiffusionPipeline(**_snake_case     )
  lowerCAmelCase    				= pipe.to(_snake_case     )
  pipe.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= self.get_dummy_inputs(_snake_case     )
  lowerCAmelCase    				= pipe(**_snake_case     )
  lowerCAmelCase    				= output.audios

  lowerCAmelCase    				= audio[0, -3:, -3:]

  assert audio.shape == (1, 2, components["unet"].sample_size)
  lowerCAmelCase    				= np.array([-0.7_265, 1.0_000, -0.8_388, 0.1_175, 0.9_498, -1.0_000]     )
  assert np.abs(audio_slice.flatten() - expected_slice     ).max() < 1E-2



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_save_load_local()



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_save_load_optional_components()



 @skip_mps
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return super().test_attention_slicing_forward_pass()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







@slow
@require_torch_gpu
class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= torch_device

  lowerCAmelCase    				= DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'     )
  lowerCAmelCase    				= pipe.to(_snake_case     )
  pipe.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= pipe(generator=_snake_case				,       num_inference_steps=1_00				,       audio_length_in_s=4.096     )
  lowerCAmelCase    				= output.audios

  lowerCAmelCase    				= audio[0, -3:, -3:]

  assert audio.shape == (1, 2, pipe.unet.sample_size)
  lowerCAmelCase    				= np.array([-0.0_192, -0.0_231, -0.0_318, -0.0_059, 0.0_002, -0.0_020]     )

  assert np.abs(audio_slice.flatten() - expected_slice     ).max() < 1E-2



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= torch_device

  lowerCAmelCase    				= DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'				,       torch_dtype=torch.floataa     )
  lowerCAmelCase    				= pipe.to(_snake_case     )
  pipe.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= pipe(generator=_snake_case				,       num_inference_steps=1_00				,       audio_length_in_s=4.096     )
  lowerCAmelCase    				= output.audios

  lowerCAmelCase    				= audio[0, -3:, -3:]

  assert audio.shape == (1, 2, pipe.unet.sample_size)
  lowerCAmelCase    				= np.array([-0.0_367, -0.0_488, -0.0_771, -0.0_525, -0.0_444, -0.0_341]     )

  assert np.abs(audio_slice.flatten() - expected_slice     ).max() < 1E-2





