



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""image_processor""", """tokenizer"""]
     lowerCAmelCase					=			"""FlavaImageProcessor"""
     lowerCAmelCase					=			("""BertTokenizer""", """BertTokenizerFast""")





     def __init__(       self :							int     ,  UpperCamelCase :							Dict=None     ,  UpperCamelCase :							Optional[Any]=None     ,  **UpperCamelCase :							Optional[int]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	None
      if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead."     ,  UpperCamelCase     ,  )
       __SCREAMING_SNAKE_CASE       :    Tuple       =	kwargs.pop("feature_extractor"				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError("You need to specify an `image_processor`."				)
      if tokenizer is None:
       raise ValueError("You need to specify a `tokenizer`."				)

      super().__init__(UpperCamelCase     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processor





     def __call__(       self :							Union[str, Any]     ,  UpperCamelCase :							Optional[ImageInput] = None     ,  UpperCamelCase :							Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Union[bool, str, PaddingStrategy] = False     ,  UpperCamelCase :							Union[bool, str, TruncationStrategy] = False     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase :							Union[str, Any]     ,  )->Tuple:

      if text is None and images is None:
       raise ValueError("You have to specify either text or images. Both cannot be none."				)

      if text is not None:
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.tokenizer(
           text=UpperCamelCase     ,  add_special_tokens=UpperCamelCase     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  max_length=UpperCamelCase     ,  stride=UpperCamelCase     ,  pad_to_multiple_of=UpperCamelCase     ,  return_token_type_ids=UpperCamelCase     ,  return_attention_mask=UpperCamelCase     ,  return_overflowing_tokens=UpperCamelCase     ,  return_special_tokens_mask=UpperCamelCase     ,  return_offsets_mapping=UpperCamelCase     ,  return_length=UpperCamelCase     ,  verbose=UpperCamelCase     ,  return_tensors=UpperCamelCase     ,  **UpperCamelCase     ,  )
      if images is not None:
       __SCREAMING_SNAKE_CASE       :    int       =	self.image_processor(
           UpperCamelCase     ,  return_image_mask=UpperCamelCase     ,  return_codebook_pixels=UpperCamelCase     ,  return_tensors=UpperCamelCase     ,  **UpperCamelCase     ,  )

      if text is not None and images is not None:
       encoding.update(UpperCamelCase				)
       return encoding
      elif text is not None:
       return encoding
      else:
       return BatchEncoding(data=dict(**UpperCamelCase				)     ,  tensor_type=UpperCamelCase				)





     def 	__snake_case    (       self :							Any     ,  *UpperCamelCase :							Optional[int]     ,  **UpperCamelCase :							List[Any]				)->Optional[Any]:
      return self.tokenizer.batch_decode(*UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Union[str, Any]     ,  *UpperCamelCase :							Any     ,  **UpperCamelCase :							str				)->Optional[Any]:
      return self.tokenizer.decode(*UpperCamelCase     ,  **UpperCamelCase				)





     @property
     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Any       =	self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





     @property
     def 	__snake_case    (       self :							Tuple				)->Tuple:
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,  UpperCamelCase     ,  )
      return self.image_processor_class






     @property
     def 	__snake_case    (       self :							List[str]				)->List[str]:
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,  UpperCamelCase     ,  )
      return self.image_processor











import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment

@require_huggingface_suite
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	inspect.getfile(accelerate.test_utils				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	os.path.sep.join(
          mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

      from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

      __SCREAMING_SNAKE_CASE       :    Tuple       =	test_metrics





     @require_cpu
     def 	__snake_case    (       self :							Union[str, Any]				)->List[Any]:
      debug_launcher(self.test_metrics.main     ,  num_processes=1				)





     @require_cpu
     def 	__snake_case    (       self :							str				)->List[str]:
      debug_launcher(self.test_metrics.main				)





     @require_single_gpu
     def 	__snake_case    (       self :							Union[str, Any]				)->str:
      self.test_metrics.main()






     @require_multi_gpu
     def 	__snake_case    (       self :							List[str]				)->int:
      print(F"""Found {torch.cuda.device_count()} devices."""				)
      __SCREAMING_SNAKE_CASE       :    Any       =	["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1				):
       execute_subprocess_async(UpperCamelCase     ,  env=os.environ.copy()				)






