
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

_lowerCamelCase       :				Optional[Any]       =			logging.get_logger(__name__)


_lowerCamelCase       :				List[Any]       =			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowerCamelCase       :				str       =			{
    '''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'''
    },
}

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




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Any       =					VOCAB_FILES_NAMES
	_UpperCAmelCase       :		int       =					PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase       :		Dict       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase       :		str       =					["input_ids", "attention_mask"]
	_UpperCAmelCase       :		Dict       =					BlenderbotTokenizer



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


				'''simple docstring'''


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

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

				_snake_case		=			add_prefix_space

				_snake_case		=			'post_processor'
				_snake_case		=			getattr(self.backend_tokenizer			,  lowercase			,  lowercase	)
				if tokenizer_component_instance:
							_snake_case		=			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:
										_snake_case		=			tuple(state['sep']	)
							if "cls" in state:
										_snake_case		=			tuple(state['cls']	)

							_snake_case		=			False

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

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

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



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


				'''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  A							(  self	:    Optional[Any]			,  lowercase	:    int	):


				'''simple docstring'''


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



	def  A							(  self	:    int			,  *lowercase	:    Any			,  **lowercase	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			kwargs.get('is_split_into_words'			,  lowercase	)
				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(*lowercase			,  **lowercase	)



	def  A							(  self	:    Dict			,  *lowercase	:    List[Any]			,  **lowercase	:    List[str]	):


				'''simple docstring'''


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

				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(*lowercase			,  **lowercase	)



	def  A							(  self	:    List[Any]			,  lowercase	:    str			,  lowercase	:    Optional[str] = None	):


				'''simple docstring'''


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



	def  A							(  self	:    Optional[Any]			,  lowercase	:    List[int]			,  lowercase	:    Optional[List[int]] = None	):


				'''simple docstring'''


				_snake_case		=			[self.sep_token_id]
				_snake_case		=			[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  A							(  self	:    List[Any]			,  lowercase	:    List[int]			,  lowercase	:    Optional[List[int]] = None	):


				'''simple docstring'''


				return token_ids_a + [self.eos_token_id]





	def  A							(  self	:    Tuple			,  lowercase	:    "Conversation"	):


				'''simple docstring'''


				_snake_case		=			[]
				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(lowercase	)

				_snake_case		=			'  '.join(lowercase	)
				_snake_case		=			self.encode(lowercase	)
				if len(lowercase	) > self.model_max_length:
							_snake_case		=			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

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)