
'''simple docstring'''

import json
import os
from typing import Dict, List, Optional, Tuple

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


snake_case									=    logging.get_logger(__name__)


snake_case									=    {
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

snake_case									=    {
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

snake_case									=    """</w>"""
snake_case									=    """@@ """


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Any     		=	set()
 lowerCAmelCase__     :   List[Any]     		=	word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)			)
  lowerCAmelCase__     :   Dict     		=	char
 return pairs


# Speech2Text2 has no max input length
snake_case									=    {"""facebook/s2t-wav2vec2-large-en-de""": 10_24}







class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				int	=			VOCAB_FILES_NAMES
 A_				:				Optional[Any]	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				Union[str, Any]	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A_				:				Optional[Any]	=			["""input_ids""", """attention_mask"""]


 def __init__(    self  : Dict			,							a__  : Union[str, Any]			,							a__  : str="<s>"			,							a__  : Union[str, Any]="<pad>"			,							a__  : Optional[Any]="</s>"			,							a__  : str="<unk>"			,							a__  : List[str]=False			,							a__  : List[Any]=None			,							**a__  : Union[str, Any]			,							):
  '''simple docstring'''

  super().__init__(
      unk_token=a__			,							bos_token=a__			,							eos_token=a__			,							pad_token=a__			,							do_lower_case=a__			,							**a__			,							)

  lowerCAmelCase__     :   int     		=	do_lower_case

  with open(a__			,							encoding="utf-8"    ) as vocab_handle:
   lowerCAmelCase__     :   List[str]     		=	json.load(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	{v: k for k, v in self.encoder.items()}

  if merges_file is None:
   logger.info(F'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''    )

   lowerCAmelCase__     :   List[str]     		=	None
   lowerCAmelCase__     :   Any     		=	None
  else:
   with open(a__			,							encoding="utf-8"    ) as merges_handle:
    lowerCAmelCase__     :   Union[str, Any]     		=	merges_handle.read().split("\n"    )[:-1]

   lowerCAmelCase__     :   str     		=	[tuple(merge.split()[:2]    ) for merge in merges]
   lowerCAmelCase__     :   Optional[int]     		=	dict(zip(a__			,							range(len(a__    )    )    )    )
   lowerCAmelCase__     :   List[str]     		=	{}


 @property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return len(self.decoder    )


 def    _A	(    self  : int    ):
  '''simple docstring'''

  return dict(self.encoder			,							**self.added_tokens_encoder    )


 def    _A	(    self  : Optional[int]			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	tuple(token[:-1]    ) + (token[-1] + BPE_TOKEN_MERGES,)
  if token in self.cache:
   return self.cache[token]
  lowerCAmelCase__     :   Tuple     		=	get_pairs(a__    )

  if not pairs:
   return token

  while True:
   lowerCAmelCase__     :   List[str]     		=	min(a__			,							key=lambda a__    : self.bpe_ranks.get(a__			,							float("inf"    )    )    )
   if bigram not in self.bpe_ranks:
    break
   lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	bigram
   lowerCAmelCase__     :   Tuple     		=	[]
   lowerCAmelCase__     :   List[Any]     		=	0
   while i < len(a__    ):
    try:
     lowerCAmelCase__     :   Dict     		=	word.index(a__			,							a__    )
    except ValueError:
     new_word.extend(word[i:]    )
     break
    else:
     new_word.extend(word[i:j]    )
     lowerCAmelCase__     :   List[Any]     		=	j

    if word[i] == first and i < len(a__    ) - 1 and word[i + 1] == second:
     new_word.append(first + second    )
     i += 2
    else:
     new_word.append(word[i]    )
     i += 1
   lowerCAmelCase__     :   Any     		=	tuple(a__    )
   lowerCAmelCase__     :   List[str]     		=	new_word
   if len(a__    ) == 1:
    break
   else:
    lowerCAmelCase__     :   Optional[int]     		=	get_pairs(a__    )
  lowerCAmelCase__     :   Dict     		=	" ".join(a__    )
  if word == "\n  " + BPE_TOKEN_MERGES:
   lowerCAmelCase__     :   Optional[Any]     		=	"\n" + BPE_TOKEN_MERGES

  if word.endswith(a__    ):
   lowerCAmelCase__     :   Optional[Any]     		=	word.replace(a__			,							""    )

  lowerCAmelCase__     :   Tuple     		=	word.replace(" "			,							a__    )
  lowerCAmelCase__     :   str     		=	word
  return word


 def    _A	(    self  : Union[str, Any]			,							a__  : Dict    ):
  '''simple docstring'''

  if self.bpe_ranks is None:
   raise ValueError(
       "This tokenizer was instantiated without a `merges.txt` file, so"
       " that it can only be used for decoding, not for encoding."
       "Make sure to provide `merges.txt` file at instantiation to enable "
       "encoding."    )

  if self.do_lower_case:
   lowerCAmelCase__     :   Optional[int]     		=	text.lower()

  lowerCAmelCase__     :   int     		=	text.split()

  lowerCAmelCase__     :   Optional[int]     		=	[]
  for token in text:
   if token:
    split_tokens.extend(list(self.bpe(a__    ).split(" "    )    )    )

  return split_tokens


 def    _A	(    self  : int			,							a__  : str    ):
  '''simple docstring'''

  return self.encoder.get(a__			,							self.encoder.get(self.unk_token    )    )


 def    _A	(    self  : List[Any]			,							a__  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	self.decoder.get(a__			,							self.unk_token    )
  return result


 def    _A	(    self  : Tuple			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	" ".join(a__    )

  # make sure @@ tokens are concatenated
  lowerCAmelCase__     :   List[str]     		=	"".join(string.split(a__    )    )

  return string


 def    _A	(    self  : Union[str, Any]			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

  if not os.path.isdir(a__    ):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
   return
  lowerCAmelCase__     :   Union[str, Any]     		=	os.path.join(
      a__			,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
  lowerCAmelCase__     :   Tuple     		=	os.path.join(
      a__			,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

  with open(a__			,							"w"			,							encoding="utf-8"    ) as f:
   f.write(json.dumps(self.encoder			,							indent=2			,							sort_keys=a__			,							ensure_ascii=a__    ) + "\n"    )

  lowerCAmelCase__     :   Any     		=	0
  if self.bpe_ranks is None:
   return (vocab_file,)

  with open(a__			,							"w"			,							encoding="utf-8"    ) as writer:
   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,							key=lambda a__    : kv[1]    ):
    if index != token_index:
     logger.warning(
         F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
         " Please check that the tokenizer is not corrupted!"    )
     lowerCAmelCase__     :   Any     		=	token_index
    writer.write(" ".join(a__    ) + "\n"    )
    index += 1

  return (vocab_file, merges_file)







'''simple docstring'''

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

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


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




class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Union[str, Any]			,							a__  : int			,							a__  : List[Any]=768    ):
  '''simple docstring'''

  super().__init__(a__    )
  lowerCAmelCase__     :   int     		=	proj_size

  lowerCAmelCase__     :   str     		=	CLIPVisionModel(a__    )
  lowerCAmelCase__     :   int     		=	PaintByExampleMapper(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	nn.LayerNorm(config.hidden_size    )
  lowerCAmelCase__     :   Optional[Any]     		=	nn.Linear(config.hidden_size			,							self.proj_size    )

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


 def    _A	(    self  : Tuple			,							a__  : int			,							a__  : Optional[Any]=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.model(pixel_values=a__    )
  lowerCAmelCase__     :   Any     		=	clip_output.pooler_output
  lowerCAmelCase__     :   Union[str, Any]     		=	self.mapper(latent_states[:, None]    )
  lowerCAmelCase__     :   List[Any]     		=	self.final_layer_norm(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	self.proj_out(a__    )
  if return_uncond_vector:
   return latent_states, self.uncond_vector

  return latent_states




class    lowerCAmelCase       (     nn.Module  ):


 def __init__(    self  : Optional[Any]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  super().__init__()
  lowerCAmelCase__     :   Optional[Any]     		=	(config.num_hidden_layers + 1) // 5
  lowerCAmelCase__     :   str     		=	config.hidden_size
  lowerCAmelCase__     :   Dict     		=	1
  lowerCAmelCase__     :   int     		=	nn.ModuleList(
      [
          BasicTransformerBlock(a__			,							a__			,							a__			,							activation_fn="gelu"			,							attention_bias=a__    )
          for _ in range(a__    )
      ]    )


 def    _A	(    self  : int			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  for block in self.blocks:
   lowerCAmelCase__     :   List[Any]     		=	block(a__    )

  return hidden_states





