




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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    ["image_processor", "tokenizer"]
      lowerCAmelCase__	:       Any			=    "LayoutLMv2ImageProcessor"
      lowerCAmelCase__	:       List[Any]			=    ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")







      def __init__(   self    :       List[str]       ,				snake_case    :       int=None       ,				snake_case    :       Optional[Any]=None       ,				**snake_case    :       List[str] ):
          __UpperCamelCase     						=  None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.'''       ,				snake_case       ,				)
              __UpperCamelCase     						=  kwargs.pop('''feature_extractor''' )

          __UpperCamelCase     						=  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__(snake_case       ,				snake_case )







      def __call__(   self    :       Optional[int]       ,				snake_case    :       Dict       ,				snake_case    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,				snake_case    :       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,				snake_case    :       Union[List[List[int]], List[List[List[int]]]] = None       ,				snake_case    :       Optional[Union[List[int], List[List[int]]]] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Union[bool, str, PaddingStrategy] = False       ,				snake_case    :       Union[bool, str, TruncationStrategy] = False       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       int = 0       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       bool = False       ,				snake_case    :       bool = False       ,				snake_case    :       bool = False       ,				snake_case    :       bool = False       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[Union[str, TensorType]] = None       ,				**snake_case    :       Dict       ,				):
          # 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.''' )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.''' )

          # first, apply the image processor
          __UpperCamelCase     						=  self.image_processor(images=snake_case       ,				return_tensors=snake_case )

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

          __UpperCamelCase     						=  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=snake_case       ,				add_special_tokens=snake_case       ,				padding=snake_case       ,				truncation=snake_case       ,				max_length=snake_case       ,				stride=snake_case       ,				pad_to_multiple_of=snake_case       ,				return_token_type_ids=snake_case       ,				return_attention_mask=snake_case       ,				return_overflowing_tokens=snake_case       ,				return_special_tokens_mask=snake_case       ,				return_offsets_mapping=snake_case       ,				return_length=snake_case       ,				verbose=snake_case       ,				return_tensors=snake_case       ,				**snake_case       ,				)

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

          return encoded_inputs







      def 					snake_case		(   self    :       Any       ,				snake_case    :       int       ,				snake_case    :       Optional[int] ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          __UpperCamelCase     						=  []
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx] )

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

          return images_with_overflow







      def 					snake_case		(   self    :       int       ,				*snake_case    :       List[Any]       ,				**snake_case    :       Union[str, Any] ):
          return self.tokenizer.batch_decode(*snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Any       ,				*snake_case    :       List[str]       ,				**snake_case    :       str ):
          return self.tokenizer.decode(*snake_case       ,				**snake_case )







      @property
      def 					snake_case		(   self    :       Optional[Any] ):
          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







      @property
      def 					snake_case		(   self    :       Union[str, Any] ):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,				snake_case       ,				)
          return self.image_processor_class







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














import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
snake_case			 =						logging.getLogger(__name__)


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  np.random.seed(args.seed )
  torch.manual_seed(args.seed )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed )


def      lowerCamelCase__	(   ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[str]		  =			argparse.ArgumentParser()
  parser.add_argument(
      "--model_name_or_path"  ,   default=lowercase  ,   type=lowercase  ,   required=lowercase  ,   )
  parser.add_argument("--prompt"  ,   type=lowercase  ,   default="" )
  parser.add_argument("--length"  ,   type=lowercase  ,   default=20 )
  parser.add_argument("--stop_token"  ,   type=lowercase  ,   default=lowercase  ,   help="Token at which text generation is stopped" )
  parser.add_argument(
      "--temperature"  ,   type=lowercase  ,   default=1.0  ,   help="temperature of 1.0 has no effect, lower tend toward greedy sampling"  ,   )
  parser.add_argument(
      "--repetition_penalty"  ,   type=lowercase  ,   default=1.0  ,   help="primarily useful for CTRL model; in that case, use 1.2" )
  parser.add_argument("--k"  ,   type=lowercase  ,   default=0 )
  parser.add_argument("--penalty_alpha"  ,   type=lowercase  ,   default=0.0 )
  parser.add_argument("--p"  ,   type=lowercase  ,   default=0.9 )

  parser.add_argument("--prefix"  ,   type=lowercase  ,   default=""  ,   help="Text added prior to input." )
  parser.add_argument("--padding_text"  ,   type=lowercase  ,   default=""  ,   help="Deprecated, the use of `--prefix` is preferred." )
  parser.add_argument("--xlm_language"  ,   type=lowercase  ,   default=""  ,   help="Optional language when used with the XLM model." )

  parser.add_argument("--seed"  ,   type=lowercase  ,   default=42  ,   help="random seed for initialization" )
  parser.add_argument("--no_cuda"  ,   action="store_true"  ,   help="Avoid using CUDA when available" )
  parser.add_argument(
      "--fp16"  ,   action="store_true"  ,   help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"  ,   )
  SCREAMING_SNAKE_CASE				:					str		  =			parser.parse_args()

  SCREAMING_SNAKE_CASE				:					Dict		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}''' )

  set_seed(lowercase )

  # Initialize the model and tokenizer
  SCREAMING_SNAKE_CASE				:					Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
  SCREAMING_SNAKE_CASE				:					int		  =			AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device )

  if args.fpaa:
    model.half()

  logger.info(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			args.prompt if args.prompt else input("Model prompt >>> " )

  SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(lowercase  ,   return_tensors="pt"  ,   add_special_tokens=lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			{key: value.to(args.device ) for key, value in inputs.items()}

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model.generate(
      **lowercase  ,   max_length=args.length + len(inputs["input_ids"][0] )  ,   penalty_alpha=args.penalty_alpha  ,   top_k=args.k  ,   )

  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for generated_sequence_idx, generated_sequence in enumerate(lowercase ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			generated_sequence.tolist()

    # Decode text
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tokenizer.decode(lowercase  ,   clean_up_tokenization_spaces=lowercase  ,   add_special_tokens=lowercase )

    # Remove all text after the stop token
    SCREAMING_SNAKE_CASE				:					List[str]		  =			text[: text.find(args.stop_token ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    SCREAMING_SNAKE_CASE				:					str		  =			(
        prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]  ,   clean_up_tokenization_spaces=lowercase ) ) :]
    )

    generated_sequences.append(lowercase )
    print(lowercase )

  return generated_sequences


if __name__ == "__main__":
   main()






