


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class _lowerCamelCase(						_a   ):
       lowercase_ :	Optional[int]     =			["""image_processor""", """tokenizer"""]
       lowercase_ :	Dict     =			"""BlipImageProcessor"""
       lowercase_ :	List[str]     =			"""AutoTokenizer"""
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           super().__init__(lowerCamelCase,      lowerCamelCase)

           # add QFormer tokenizer
           _lowercase       :							int      		=		qformer_tokenizer
       def __call__(					self,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = True,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = 0,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = True,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   BatchFeature:




           """simple docstring"""

           if images is None and text is None:
               raise ValueError('You have to specify at least images or text.')

           _lowercase       :							int      		=		BatchFeature()

           if text is not None:
               _lowercase       :							Union[str, Any]      		=		self.tokenizer(
                   text=lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      stride=lowerCamelCase,      pad_to_multiple_of=lowerCamelCase,      return_attention_mask=lowerCamelCase,      return_overflowing_tokens=lowerCamelCase,      return_special_tokens_mask=lowerCamelCase,      return_offsets_mapping=lowerCamelCase,      return_token_type_ids=lowerCamelCase,      return_length=lowerCamelCase,      verbose=lowerCamelCase,      return_tensors=lowerCamelCase,      **lowerCamelCase,      )
               encoding.update(lowerCamelCase)
               _lowercase       :							str      		=		self.qformer_tokenizer(
                   text=lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      stride=lowerCamelCase,      pad_to_multiple_of=lowerCamelCase,      return_attention_mask=lowerCamelCase,      return_overflowing_tokens=lowerCamelCase,      return_special_tokens_mask=lowerCamelCase,      return_offsets_mapping=lowerCamelCase,      return_token_type_ids=lowerCamelCase,      return_length=lowerCamelCase,      verbose=lowerCamelCase,      return_tensors=lowerCamelCase,      **lowerCamelCase,      )
               _lowercase       :							Tuple      		=		qformer_text_encoding.pop('input_ids')
               _lowercase       :							Tuple      		=		qformer_text_encoding.pop('attention_mask')

           if images is not None:
               _lowercase       :							Any      		=		self.image_processor(lowerCamelCase,      return_tensors=lowerCamelCase)
               encoding.update(lowerCamelCase)

           return encoding
       def 			UpperCamelCase     (					self,      *lowerCamelCase,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      *lowerCamelCase,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           return self.tokenizer.decode(*lowerCamelCase,      **lowerCamelCase)
       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.tokenizer.model_input_names
           _lowercase       :							Union[str, Any]      		=		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      **lowerCamelCase)       ->   Optional[Any]:




           """simple docstring"""

           if os.path.isfile(lowerCamelCase):
               raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''')
           os.makedirs(lowerCamelCase,      exist_ok=lowerCamelCase)
           _lowercase       :							Tuple      		=		os.path.join(lowerCamelCase,      'qformer_tokenizer')
           self.qformer_tokenizer.save_pretrained(lowerCamelCase)
           return super().save_pretrained(lowerCamelCase,      **lowerCamelCase)




       @classmethod
       def 			UpperCamelCase     (					cls,      lowerCamelCase,      **lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		AutoTokenizer.from_pretrained(lowerCamelCase,      subfolder='qformer_tokenizer')
           _lowercase       :							Any      		=		cls._get_arguments_from_pretrained(lowerCamelCase,      **lowerCamelCase)
           args.append(lowerCamelCase)
           return cls(*lowerCamelCase)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Tuple												= ["LlamaTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= ["LlamaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "LlamaForCausalLM",
     "LlamaModel",
     "LlamaPreTrainedModel",
     "LlamaForSequenceClassification",
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama_fast import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


