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,
)
lowerCAmelCase__					=				logging.getLogger(__name__)






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






def    __lowerCamelCase	(		):
							lowerCAmelCase__  			=      argparse.ArgumentParser()
							parser.add_argument(
							    '--model_name_or_path'			,						default=lowerCAmelCase__			,						type=lowerCAmelCase__			,						required=lowerCAmelCase__			,						)
							parser.add_argument('--prompt'			,						type=lowerCAmelCase__			,						default=''			)
							parser.add_argument('--length'			,						type=lowerCAmelCase__			,						default=2_0			)
							parser.add_argument('--stop_token'			,						type=lowerCAmelCase__			,						default=lowerCAmelCase__			,						help='Token at which text generation is stopped'			)
							parser.add_argument(
							    '--temperature'			,						type=lowerCAmelCase__			,						default=1.0			,						help='temperature of 1.0 has no effect, lower tend toward greedy sampling'			,						)
							parser.add_argument(
							    '--repetition_penalty'			,						type=lowerCAmelCase__			,						default=1.0			,						help='primarily useful for CTRL model; in that case, use 1.2'			)
							parser.add_argument('--k'			,						type=lowerCAmelCase__			,						default=0			)
							parser.add_argument('--penalty_alpha'			,						type=lowerCAmelCase__			,						default=0.0			)
							parser.add_argument('--p'			,						type=lowerCAmelCase__			,						default=0.9			)

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

							parser.add_argument('--seed'			,						type=lowerCAmelCase__			,						default=4_2			,						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'			,						)
							lowerCAmelCase__  			=      parser.parse_args()

							lowerCAmelCase__  			=      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'			)
							lowerCAmelCase__  			=      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(lowerCAmelCase__			)

							# Initialize the model and tokenizer
							lowerCAmelCase__  			=      AutoTokenizer.from_pretrained(args.model_name_or_path			)
							lowerCAmelCase__  			=      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(lowerCAmelCase__			)
							lowerCAmelCase__  			=      args.prompt if args.prompt else input('Model prompt >>> '			)

							lowerCAmelCase__  			=      tokenizer(lowerCAmelCase__			,						return_tensors='pt'			,						add_special_tokens=lowerCAmelCase__			)
							lowerCAmelCase__  			=      {key: value.to(args.device			) for key, value in inputs.items()}

							lowerCAmelCase__  			=      model.generate(
							    **lowerCAmelCase__			,						max_length=args.length + len(inputs['input_ids'][0]			)			,						penalty_alpha=args.penalty_alpha			,						top_k=args.k			,						)

							lowerCAmelCase__  			=      []
							for generated_sequence_idx, generated_sequence in enumerate(lowerCAmelCase__			):
														print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""			)
														lowerCAmelCase__  			=      generated_sequence.tolist()

														# Decode text
														lowerCAmelCase__  			=      tokenizer.decode(lowerCAmelCase__			,						clean_up_tokenization_spaces=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			)

														# Remove all text after the stop token
														lowerCAmelCase__  			=      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
														lowerCAmelCase__  			=      (
														    prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]			,						clean_up_tokenization_spaces=lowerCAmelCase__			)			) :]
														)

														generated_sequences.append(lowerCAmelCase__			)
														print(lowerCAmelCase__			)

							return generated_sequences


if __name__ == "__main__":
				main()

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        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




 def __init__(    self      :						List[Any]    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						int=None    ,					**lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.'    ,					lowercase__    ,					)
               lowerCAmelCase__  			=      kwargs.pop('feature_extractor')

        lowerCAmelCase__  			=      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__(lowercase__    ,					lowercase__)




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







        '''simple docstring'''



        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
        lowerCAmelCase__  			=      self.image_processor(images=lowercase__    ,					return_tensors=lowercase__)

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

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

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

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx])

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

        return images_with_overflow




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



        return self.tokenizer.batch_decode(*lowercase__    ,					**lowercase__)




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						str    ,					**lowercase__      :						List[Any]):







        '''simple docstring'''



        return self.tokenizer.decode(*lowercase__    ,					**lowercase__)




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					lowercase__    ,					)
        return self.image_processor_class




 @property
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					lowercase__    ,					)
        return self.image_processor
