
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

lowercase_ 				=			logging.get_logger(__name__)


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

lowercase_ 				=			{
    '''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'''
    },
}

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





class     __UpperCamelCase (							a__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			VOCAB_FILES_NAMES
     lowerCAmelCase_							   =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_							   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_							   =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase_							   =			BlenderbotTokenizer



     def __init__(				self							:							List[str]     ,						_A							:							Optional[Any]=None     ,						_A							:							Optional[Any]=None     ,						_A							:							Optional[int]=None     ,						_A							:							Tuple="replace"     ,						_A							:							Tuple="<s>"     ,						_A							:							Dict="</s>"     ,						_A							:							Tuple="</s>"     ,						_A							:							List[Any]="<s>"     ,						_A							:							Optional[int]="<unk>"     ,						_A							:							List[str]="<pad>"     ,						_A							:							Dict="<mask>"     ,						_A							:							Optional[Any]=False     ,						_A							:							Optional[int]=True     ,						**_A							:							Optional[int]     ,						):







       """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     ,						)

       __SCREAMING_SNAKE_CASE      :	Tuple					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get('''add_prefix_space'''     ,						_snake_case ) != add_prefix_space:
         __SCREAMING_SNAKE_CASE      :	int					=		getattr(_snake_case     ,						pre_tok_state.pop('''type''' ) )
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		add_prefix_space
         __SCREAMING_SNAKE_CASE      :	int					=		pre_tok_class(**_snake_case )

       __SCREAMING_SNAKE_CASE      :	Dict					=		add_prefix_space

       __SCREAMING_SNAKE_CASE      :	Tuple					=		'''post_processor'''
       __SCREAMING_SNAKE_CASE      :	Tuple					=		getattr(self.backend_tokenizer     ,						_snake_case     ,						_snake_case )
       if tokenizer_component_instance:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		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:
           __SCREAMING_SNAKE_CASE      :	str					=		tuple(state['''sep'''] )
         if "cls" in state:
           __SCREAMING_SNAKE_CASE      :	int					=		tuple(state['''cls'''] )

         __SCREAMING_SNAKE_CASE      :	Dict					=		False

         if state.get('''add_prefix_space'''     ,						_snake_case ) != add_prefix_space:
           __SCREAMING_SNAKE_CASE      :	int					=		add_prefix_space
           __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		True

         if state.get('''trim_offsets'''     ,						_snake_case ) != trim_offsets:
           __SCREAMING_SNAKE_CASE      :	List[str]					=		trim_offsets
           __SCREAMING_SNAKE_CASE      :	Dict					=		True

         if changes_to_apply:
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		getattr(_snake_case     ,						state.pop('''type''' ) )
           __SCREAMING_SNAKE_CASE      :	List[str]					=		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							:							List[str] ):







       """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							:							List[str]     ,						_A							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		AddedToken(_snake_case     ,						lstrip=_snake_case     ,						rstrip=_snake_case ) if isinstance(_snake_case     ,						_snake_case ) else value
       __SCREAMING_SNAKE_CASE      :	Tuple					=		value



     def  UpperCAmelCase__							(				self							:							str     ,						*_A							:							int     ,						**_A							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		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							:							Union[str, Any]     ,						*_A							:							str     ,						**_A							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		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							:							Union[str, Any]     ,						_A							:							str     ,						_A							:							List[Any] = None ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		self._tokenizer.model.save(_snake_case     ,						name=_snake_case )
       return tuple(_snake_case )



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							Tuple     ,						_A							:							List[str] = None ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		[self.sep_token_id]
       __SCREAMING_SNAKE_CASE      :	Any					=		[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							:							str     ,						_A							:							Union[str, Any]     ,						_A							:							str = None ):







       """simple docstring"""




       return token_ids_a + [self.eos_token_id]




     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		[]
       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 )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		'''  '''.join(_snake_case )
       __SCREAMING_SNAKE_CASE      :	Dict					=		self.encode(_snake_case )
       if len(_snake_case ) > self.model_max_length:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		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





