
"""simple docstring"""





import json
from typing import 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_roberta import RobertaTokenizer


_a		       =							logging.get_logger(__name__)

_a		       =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_a		       =							{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

_a		       =							{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Dict       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Any       =		["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		RobertaTokenizer







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

     """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_				,   )

     UpperCAmelCase_					:					Optional[int]			     =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
     if pre_tok_state.get("add_prefix_space"				,   lowercase_  ) != add_prefix_space:
        UpperCAmelCase_					:					Union[str, Any]			     =    getattr(lowercase_				,   pre_tok_state.pop("type"  )  )
        UpperCAmelCase_					:					Any			     =    add_prefix_space
        UpperCAmelCase_					:					Tuple			     =    pre_tok_class(**lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    add_prefix_space

     UpperCAmelCase_					:					List[Any]			     =    "post_processor"
     UpperCAmelCase_					:					Union[str, Any]			     =    getattr(self.backend_tokenizer				,   lowercase_				,   lowercase_  )
     if tokenizer_component_instance:
        UpperCAmelCase_					:					str			     =    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:
           UpperCAmelCase_					:					Any			     =    tuple(state["sep"]  )
        if "cls" in state:
           UpperCAmelCase_					:					Optional[int]			     =    tuple(state["cls"]  )

        UpperCAmelCase_					:					List[Any]			     =    False

        if state.get("add_prefix_space"				,   lowercase_  ) != add_prefix_space:
           UpperCAmelCase_					:					int			     =    add_prefix_space
           UpperCAmelCase_					:					Optional[int]			     =    True

        if state.get("trim_offsets"				,   lowercase_  ) != trim_offsets:
           UpperCAmelCase_					:					List[str]			     =    trim_offsets
           UpperCAmelCase_					:					List[str]			     =    True

        if changes_to_apply:
           UpperCAmelCase_					:					Optional[Any]			     =    getattr(lowercase_				,   state.pop("type"  )  )
           UpperCAmelCase_					:					Any			     =    component_class(**lowercase_  )
           setattr(self.backend_tokenizer				,   lowercase_				,   lowercase_  )







  @property
  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				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else value
     UpperCAmelCase_					:					Optional[Any]			     =    value







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

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    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        UpperCamelCase__					(		self				,   *lowercase_				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self._tokenizer.model.save(lowercase_				,   name=lowercase_  )
     return tuple(lowercase_  )







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

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







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

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Optional[int]			     =    [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]









'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
