from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
   import torch







class __snake_case							(		__snake_case						):
   lowerCAmelCase_								=				["image_processor", "tokenizer"]
   lowerCAmelCase_								=				"OneFormerImageProcessor"
   lowerCAmelCase_								=				("CLIPTokenizer", "CLIPTokenizerFast")




   def __init__(       self     :							List[Any]			,      _lowercase     :							Tuple=None			,      _lowercase     :							List[str]=None			,      _lowercase     :							Tuple = 77			,      _lowercase     :							Dict = 77			,      **_lowercase     :							List[Any] ):



     """simple docstring"""






     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`.""" )

     SCREAMING_SNAKE_CASE__     				= max_seq_length
     SCREAMING_SNAKE_CASE__     				= task_seq_length

     super().__init__(__UpperCamelCase			,      __UpperCamelCase )




   def       __a		(       self     :							Any			,      _lowercase     :							Optional[int]=None			,      _lowercase     :							Optional[int]=77 ):



     """simple docstring"""






     if text_list is None:
       raise ValueError("""tokens cannot be None.""" )

     SCREAMING_SNAKE_CASE__     				= self.tokenizer(__UpperCamelCase			,      padding="""max_length"""			,      max_length=__UpperCamelCase			,      truncation=__UpperCamelCase )

     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= tokens["""attention_mask"""], tokens["""input_ids"""]

     SCREAMING_SNAKE_CASE__     				= []
     for attn_mask, input_id in zip(__UpperCamelCase			,      __UpperCamelCase ):
       SCREAMING_SNAKE_CASE__     				= torch.tensor(__UpperCamelCase ) * torch.tensor(__UpperCamelCase )
       token_inputs.append(token.unsqueeze(0 ) )

     SCREAMING_SNAKE_CASE__     				= torch.cat(__UpperCamelCase			,      dim=0 )
     return token_inputs




   def __call__(       self     :							str			,      _lowercase     :							Any=None			,      _lowercase     :							List[Any]=None			,      _lowercase     :							Optional[int]=None			,      **_lowercase     :							Tuple ):



     """simple docstring"""






     if task_inputs is None:
       raise ValueError("""You have to specify the task_input. Found None.""" )
     elif images is None:
       raise ValueError("""You have to specify the image. Found None.""" )

     if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs ):
       raise ValueError("""task_inputs must be semantic, instance, or panoptic.""" )

     SCREAMING_SNAKE_CASE__     				= self.image_processor(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      **__UpperCamelCase )

     if isinstance(__UpperCamelCase			,      __UpperCamelCase ):
       SCREAMING_SNAKE_CASE__     				= [task_inputs]

     if isinstance(__UpperCamelCase			,      __UpperCamelCase ) and all(isinstance(__UpperCamelCase			,      __UpperCamelCase ) for task_input in task_inputs ):
       SCREAMING_SNAKE_CASE__     				= []
       for task in task_inputs:
         SCREAMING_SNAKE_CASE__     				= f"""the task is {task}"""
         task_token_inputs.append(__UpperCamelCase )
       SCREAMING_SNAKE_CASE__     				= self._preprocess_text(__UpperCamelCase			,      max_length=self.task_seq_length )
     else:
       raise TypeError("""Task Inputs should be a string or a list of strings.""" )

     if hasattr(__UpperCamelCase			,      """text_inputs""" ):
       SCREAMING_SNAKE_CASE__     				= encoded_inputs.text_inputs

       SCREAMING_SNAKE_CASE__     				= []
       for texts in texts_list:
         SCREAMING_SNAKE_CASE__     				= self._preprocess_text(__UpperCamelCase			,      max_length=self.max_seq_length )
         text_inputs.append(text_input_list.unsqueeze(0 ) )

       SCREAMING_SNAKE_CASE__     				= torch.cat(__UpperCamelCase			,      dim=0 )

     return encoded_inputs




   def       __a		(       self     :							Tuple			,      _lowercase     :							List[str]=None			,      _lowercase     :							str=None			,      _lowercase     :							Tuple=None			,      **_lowercase     :							str ):



     """simple docstring"""






     if task_inputs is None:
       raise ValueError("""You have to specify the task_input. Found None.""" )
     elif images is None:
       raise ValueError("""You have to specify the image. Found None.""" )

     if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs ):
       raise ValueError("""task_inputs must be semantic, instance, or panoptic.""" )

     SCREAMING_SNAKE_CASE__     				= self.image_processor.encode_inputs(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      **__UpperCamelCase )

     if isinstance(__UpperCamelCase			,      __UpperCamelCase ):
       SCREAMING_SNAKE_CASE__     				= [task_inputs]

     if isinstance(__UpperCamelCase			,      __UpperCamelCase ) and all(isinstance(__UpperCamelCase			,      __UpperCamelCase ) for task_input in task_inputs ):
       SCREAMING_SNAKE_CASE__     				= []
       for task in task_inputs:
         SCREAMING_SNAKE_CASE__     				= f"""the task is {task}"""
         task_token_inputs.append(__UpperCamelCase )
       SCREAMING_SNAKE_CASE__     				= self._preprocess_text(__UpperCamelCase			,      max_length=self.task_seq_length )
     else:
       raise TypeError("""Task Inputs should be a string or a list of strings.""" )

     if hasattr(__UpperCamelCase			,      """text_inputs""" ):
       SCREAMING_SNAKE_CASE__     				= encoded_inputs.text_inputs

       SCREAMING_SNAKE_CASE__     				= []
       for texts in texts_list:
         SCREAMING_SNAKE_CASE__     				= self._preprocess_text(__UpperCamelCase			,      max_length=self.max_seq_length )
         text_inputs.append(text_input_list.unsqueeze(0 ) )

       SCREAMING_SNAKE_CASE__     				= torch.cat(__UpperCamelCase			,      dim=0 )

     return encoded_inputs




   def       __a		(       self     :							List[Any]			,      *_lowercase     :							Tuple			,      **_lowercase     :							Tuple ):



     """simple docstring"""






     return self.image_processor.post_process_semantic_segmentation(*__UpperCamelCase			,      **__UpperCamelCase )




   def       __a		(       self     :							Optional[int]			,      *_lowercase     :							Optional[Any]			,      **_lowercase     :							Optional[int] ):



     """simple docstring"""






     return self.image_processor.post_process_instance_segmentation(*__UpperCamelCase			,      **__UpperCamelCase )




   def       __a		(       self     :							Tuple			,      *_lowercase     :							Optional[Any]			,      **_lowercase     :							Union[str, Any] ):



     """simple docstring"""






     return self.image_processor.post_process_panoptic_segmentation(*__UpperCamelCase			,      **__UpperCamelCase )


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,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Optional[Any]:
    np.random.seed(args.seed )
    torch.manual_seed(args.seed )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed )





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

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

    parser.add_argument("--seed"				,					type=snake_case__				,					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"				,					)
    A_								=					parser.parse_args()

    A_								=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
    A_								=					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(snake_case__ )

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

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

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

    A_								=					[]
    for generated_sequence_idx, generated_sequence in enumerate(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

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

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

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






