





"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


SCREAMING_SNAKE_CASE    =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE    =	{
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE    =	{
    "unc-nlp/lxmert-base-uncased": 512,
}

SCREAMING_SNAKE_CASE    =	{
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				PRETRAINED_INIT_CONFIGURATION
    lowercase__						 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				LxmertTokenizer




    def __init__(   self						:	int     ,			snake_case_						:	List[str]=None     ,			snake_case_						:	List[str]=None     ,			snake_case_						:	int=True     ,			snake_case_						:	str="[UNK]"     ,			snake_case_						:	int="[SEP]"     ,			snake_case_						:	Any="[PAD]"     ,			snake_case_						:	Union[str, Any]="[CLS]"     ,			snake_case_						:	Tuple="[MASK]"     ,			snake_case_						:	int=True     ,			snake_case_						:	Optional[int]=None     ,			**snake_case_						:	Tuple     ,			)				->							Tuple:




     '''simple docstring'''

     super().__init__(
         snake_case_     ,			tokenizer_file=snake_case_     ,			do_lower_case=snake_case_     ,			unk_token=snake_case_     ,			sep_token=snake_case_     ,			pad_token=snake_case_     ,			cls_token=snake_case_     ,			mask_token=snake_case_     ,			tokenize_chinese_chars=snake_case_     ,			strip_accents=snake_case_     ,			**snake_case_     ,			)

     A__	=    json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
     if (
         normalizer_state.get("lowercase"     ,			snake_case_				) != do_lower_case
         or normalizer_state.get("strip_accents"     ,			snake_case_				) != strip_accents
         or normalizer_state.get("handle_chinese_chars"     ,			snake_case_				) != tokenize_chinese_chars
     ):
      A__	=    getattr(snake_case_     ,			normalizer_state.pop("type"				)				)
      A__	=    do_lower_case
      A__	=    strip_accents
      A__	=    tokenize_chinese_chars
      A__	=    normalizer_class(**snake_case_				)

     A__	=    do_lower_case




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	str     ,			snake_case_						:	int=None				)				->							str:




     '''simple docstring'''

     A__	=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

     return output




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None				)				->							List[int]:




     '''simple docstring'''

     A__	=    [self.sep_token_id]
     A__	=    [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				) * [0] + len(token_ids_a + sep				) * [1]




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	str     ,			snake_case_						:	Optional[str] = None				)				->							Tuple[str]:




     '''simple docstring'''

     A__	=    self._tokenizer.model.save(snake_case_     ,			name=snake_case_				)
     return tuple(snake_case_				)











"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class      UpperCAmelCase_  (						nn.Module							):




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




     '''simple docstring'''

     super().__init__()
     A__	=    nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=snake_case_     ,			attention_head_dim=snake_case_     ,			in_channels=snake_case_     ,			num_layers=snake_case_     ,			dropout=snake_case_     ,			norm_num_groups=snake_case_     ,			cross_attention_dim=snake_case_     ,			attention_bias=snake_case_     ,			sample_size=snake_case_     ,			num_vector_embeds=snake_case_     ,			activation_fn=snake_case_     ,			num_embeds_ada_norm=snake_case_     ,			)
             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
     A__	=    0.5

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




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	bool = True     ,			)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    hidden_states

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

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

     if not return_dict:
      return (output_states,)

     return TransformeraDModelOutput(sample=snake_case_				)





