import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)

__magic_name__								='''▁'''

__magic_name__								={'''vocab_file''': '''sentencepiece.bpe.model'''}

__magic_name__								={
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__magic_name__								={
    '''facebook/xglm-564M''': 2048,
}



class     _A   (    _UpperCAmelCase							):

  '''simple docstring'''





  SCREAMING_SNAKE_CASE_	:	int						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	str						=PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	Any						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=['input_ids', 'attention_mask']






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="<unk>"   ,				SCREAMING_SNAKE_CASE_="<pad>"   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      None:





       '''simple docstring'''





       UpperCamelCase__										=      {} if sp_model_kwargs is None else sp_model_kwargs

       # Compatibility with the original tokenizer
       UpperCamelCase__										=      7
       UpperCamelCase__										=      [F"<madeupword{i}>" for i in range(self.num_madeup_words	)]

       UpperCamelCase__										=      kwargs.get('''additional_special_tokens'''   ,				[]	)
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           bos_token=__UpperCamelCase   ,				eos_token=__UpperCamelCase   ,				unk_token=__UpperCamelCase   ,				sep_token=__UpperCamelCase   ,				cls_token=__UpperCamelCase   ,				pad_token=__UpperCamelCase   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__UpperCamelCase   ,				)

       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(__UpperCamelCase	)	)
       UpperCamelCase__										=      vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       UpperCamelCase__										=      1

       # Mimic fairseq token-to-id alignment for the first 4 token
       UpperCamelCase__										=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       UpperCamelCase__										=      len(self.sp_model	)
       UpperCamelCase__										=      {F"<madeupword{i}>": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words	)}
       self.fairseq_tokens_to_ids.update(__UpperCamelCase	)

       UpperCamelCase__										=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def __getstate__(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.__dict__.copy()
       UpperCamelCase__										=      None
       UpperCamelCase__										=      self.sp_model.serialized_model_proto()
       return state






  def __setstate__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      d

       # for backward compatibility
       if not hasattr(self   ,				'''sp_model_kwargs'''	):
            UpperCamelCase__										=      {}

       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      List[int]:





       '''simple docstring'''





       if token_ids_a is None:
            return [self.sep_token_id] + token_ids_a
       UpperCamelCase__										=      [self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False	)		->      List[int]:





       '''simple docstring'''





       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__UpperCamelCase   ,				token_ids_a=__UpperCamelCase   ,				already_has_special_tokens=__UpperCamelCase	)

       if token_ids_a is None:
            return [1] + ([0] * len(__UpperCamelCase	))
       return [1] + ([0] * len(__UpperCamelCase	)) + [1, 1] + ([0] * len(__UpperCamelCase	))






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      List[int]:





       '''simple docstring'''





       UpperCamelCase__										=      [self.sep_token_id]

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






  @property
  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       return len(self.sp_model	) + self.fairseq_offset + self.num_madeup_words






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      {self.convert_ids_to_tokens(__UpperCamelCase	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       return self.sp_model.encode(__UpperCamelCase   ,				out_type=__UpperCamelCase	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       UpperCamelCase__										=      self.sp_model.PieceToId(__UpperCamelCase	)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      ''''''.join(__UpperCamelCase	).replace(__UpperCamelCase   ,				''' '''	).strip()
       return out_string






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple[str]:





       '''simple docstring'''





       if not os.path.isdir(__UpperCamelCase	):
            logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
            return
       UpperCamelCase__										=      os.path.join(
           __UpperCamelCase   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

       if os.path.abspath(self.vocab_file	) != os.path.abspath(__UpperCamelCase	) and os.path.isfile(self.vocab_file	):
            copyfile(self.vocab_file   ,				__UpperCamelCase	)
       elif not os.path.isfile(self.vocab_file	):
            with open(__UpperCamelCase   ,				'''wb'''	) as fi:
                 UpperCamelCase__										=      self.sp_model.serialized_model_proto()
                 fi.write(__UpperCamelCase	)

       return (out_vocab_file,)




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)  # pylint: disable=invalid-name



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=768	)		->      Dict:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      proj_size

       UpperCamelCase__										=      CLIPVisionModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      PaintByExampleMapper(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      nn.LayerNorm(config.hidden_size	)
       UpperCamelCase__										=      nn.Linear(config.hidden_size   ,				self.proj_size	)

       # uncondition for scaling
       UpperCamelCase__										=      nn.Parameter(torch.randn((1, 1, self.proj_size)	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model(pixel_values=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      clip_output.pooler_output
       UpperCamelCase__										=      self.mapper(latent_states[:, None]	)
       UpperCamelCase__										=      self.final_layer_norm(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.proj_out(SCREAMING_SNAKE_CASE_	)
       if return_uncond_vector:
            return latent_states, self.uncond_vector

       return latent_states



class     _A   (    nn.Module							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      (config.num_hidden_layers + 1) // 5
       UpperCamelCase__										=      config.hidden_size
       UpperCamelCase__										=      1
       UpperCamelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				activation_fn='''gelu'''   ,				attention_bias=SCREAMING_SNAKE_CASE_	)
               for _ in range(SCREAMING_SNAKE_CASE_	)
           ]	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       for block in self.blocks:
            UpperCamelCase__										=      block(SCREAMING_SNAKE_CASE_	)

       return hidden_states



