
import warnings
from typing import List, Optional, Union

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




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     ['image_processor', 'tokenizer']
      _A						:		Tuple    	=     'LayoutLMv3ImageProcessor'
      _A						:		int    	=     ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
      def __init__(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						=				None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."		,	lowerCamelCase		,	)
              snake_case__						=				kwargs.pop("feature_extractor" )

          snake_case__						=				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__(lowerCamelCase		,	lowerCamelCase )
      def __call__(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = True		,	lowerCamelCase = False		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = 0		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = True		,	lowerCamelCase = None		,	**lowerCamelCase		,	):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  "You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )

          # first, apply the image processor
          snake_case__						=				self.image_processor(images=lowerCamelCase		,	return_tensors=lowerCamelCase )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(lowerCamelCase		,	lowerCamelCase ):
                  snake_case__						=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
              snake_case__						=				features["words"]

          snake_case__						=				self.tokenizer(
              text=text if text is not None else features["words"]		,	text_pair=text_pair if text_pair is not None else None		,	boxes=boxes if boxes is not None else features["boxes"]		,	word_labels=lowerCamelCase		,	add_special_tokens=lowerCamelCase		,	padding=lowerCamelCase		,	truncation=lowerCamelCase		,	max_length=lowerCamelCase		,	stride=lowerCamelCase		,	pad_to_multiple_of=lowerCamelCase		,	return_token_type_ids=lowerCamelCase		,	return_attention_mask=lowerCamelCase		,	return_overflowing_tokens=lowerCamelCase		,	return_special_tokens_mask=lowerCamelCase		,	return_offsets_mapping=lowerCamelCase		,	return_length=lowerCamelCase		,	verbose=lowerCamelCase		,	return_tensors=lowerCamelCase		,	**lowerCamelCase		,	)

          # add pixel values
          snake_case__						=				features.pop("pixel_values" )
          if return_overflowing_tokens is True:
              snake_case__						=				self.get_overflowing_images(lowerCamelCase		,	encoded_inputs["overflow_to_sample_mapping"] )
          snake_case__						=				images

          return encoded_inputs
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          snake_case__						=				[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx] )

          if len(lowerCamelCase ) != len(lowerCamelCase ):
              raise ValueError(
                  "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                  F""" {len(lowerCamelCase )} and {len(lowerCamelCase )}""" )

          return images_with_overflow
      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          return self.tokenizer.batch_decode(*lowerCamelCase		,	**lowerCamelCase )
      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          return self.tokenizer.decode(*lowerCamelCase		,	**lowerCamelCase )
      @property
      def A_			(			self ):
          return ["input_ids", "bbox", "attention_mask", "pixel_values"]
      @property
      def A_			(			self ):
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,	lowerCamelCase		,	)
          return self.image_processor_class



      @property
      def A_			(			self ):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,	lowerCamelCase		,	)
          return self.image_processor








import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return EnvironmentCommand()





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return EnvironmentCommand(args.accelerate_config_file							)





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				parser.add_parser("env" )
          download_parser.set_defaults(func=lowerCamelCase )
          download_parser.add_argument(
              "--accelerate-config_file"		,	default=lowerCamelCase		,	help="The accelerate config file to use for the default values in the launching script."		,	)
          download_parser.set_defaults(func=lowerCamelCase )
      def __init__(			self		,	lowerCamelCase		,	*lowerCamelCase ):
          snake_case__						=				accelerate_config_file
      def A_			(			self ):
          snake_case__						=				"not installed"
          if is_safetensors_available():
              import safetensors

              snake_case__						=				safetensors.__version__
          elif importlib.util.find_spec("safetensors" ) is not None:
              import safetensors

              snake_case__						=				F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

          snake_case__						=				"not installed"
          snake_case__						=				snake_case__						=				"not found"
          if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              snake_case__						=				accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase ):
                  snake_case__						=				load_config_from_file(self._accelerate_config_file ).to_dict()

              snake_case__						=				(
                  "\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
                  if isinstance(lowerCamelCase		,	lowerCamelCase )
                  else F"""\t{accelerate_config}"""
              )

          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_torch_available():
              import torch

              snake_case__						=				torch.__version__
              snake_case__						=				torch.cuda.is_available()

          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_tf_available():
              import tensorflow as tf

              snake_case__						=				tf.__version__
              try:
                  # deprecated in v2.1
                  snake_case__						=				tf.test.is_gpu_available()
              except AttributeError:
                  # returns list of devices, convert to bool
                  snake_case__						=				bool(tf.config.list_physical_devices("GPU" ) )

          snake_case__						=				"not installed"
          snake_case__						=				"not installed"
          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_flax_available():
              import flax
              import jax
              import jaxlib

              snake_case__						=				flax.__version__
              snake_case__						=				jax.__version__
              snake_case__						=				jaxlib.__version__
              snake_case__						=				jax.lib.xla_bridge.get_backend().platform

          snake_case__						=				{
              "`transformers` version": version,
              "Platform": platform.platform(),
              "Python version": platform.python_version(),
              "Huggingface_hub version": huggingface_hub.__version__,
              "Safetensors version": F"""{safetensors_version}""",
              "Accelerate version": F"""{accelerate_version}""",
              "Accelerate config": F"""{accelerate_config_str}""",
              "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
              "Tensorflow version (GPU?)": F"""{tf_version} ({tf_cuda_available})""",
              "Flax version (CPU?/GPU?/TPU?)": F"""{flax_version} ({jax_backend})""",
              "Jax version": F"""{jax_version}""",
              "JaxLib version": F"""{jaxlib_version}""",
              "Using GPU in script?": "<fill in>",
              "Using distributed or parallel set-up in script?": "<fill in>",
          }

          print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
          print(self.format_dict(lowerCamelCase ) )

          return info



      @staticmethod
      def A_			(			lowerCamelCase ):
          return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"



