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,
)
UpperCamelCase													=		logging.getLogger(__name__)

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

def     lowercase_       (    ):
    lowercase__				:     Tuple					      =       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=20)
    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=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"	,			)
    lowercase__				:     Optional[Any]					      =       parser.parse_args()

    lowercase__				:     str					      =       torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
    lowercase__				:     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(_lowerCamelCase)

    # Initialize the model and tokenizer
    lowercase__				:     Dict					      =       AutoTokenizer.from_pretrained(args.model_name_or_path)
    lowercase__				:     Optional[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(_lowerCamelCase)
    lowercase__				:     List[str]					      =       args.prompt if args.prompt else input("Model prompt >>> ")

    lowercase__				:     Optional[Any]					      =       tokenizer(_lowerCamelCase	,			return_tensors="pt"	,			add_special_tokens=_lowerCamelCase)
    lowercase__				:     Union[str, Any]					      =       {key: value.to(args.device) for key, value in inputs.items()}

    lowercase__				:     str					      =       model.generate(
        **_lowerCamelCase	,			max_length=args.length + len(inputs["input_ids"][0])	,			penalty_alpha=args.penalty_alpha	,			top_k=args.k	,			)

    lowercase__				:     Tuple					      =       []
    for generated_sequence_idx, generated_sequence in enumerate(_lowerCamelCase):
        print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''')
        lowercase__				:     Optional[Any]					      =       generated_sequence.tolist()

        # Decode text
        lowercase__				:     Optional[int]					      =       tokenizer.decode(_lowerCamelCase	,			clean_up_tokenization_spaces=_lowerCamelCase	,			add_special_tokens=_lowerCamelCase)

        # Remove all text after the stop token
        lowercase__				:     Dict					      =       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
        lowercase__				:     Any					      =       (
            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()




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}



class 						snake_case_ (	__A ):
   __A			:	Any        =       "mobilevit"






   def __init__(   self       :  Union[str, Any]			,		lowercase_       :  List[Any]=3			,		lowercase_       :  List[Any]=2_56			,		lowercase_       :  Union[str, Any]=2			,		lowercase_       :  int=[1_44, 1_92, 2_40]			,		lowercase_       :  List[str]=[16, 32, 64, 96, 1_28, 1_60, 6_40]			,		lowercase_       :  Tuple=4			,		lowercase_       :  Optional[int]=2.0			,		lowercase_       :  int=4.0			,		lowercase_       :  str="silu"			,		lowercase_       :  Optional[Any]=3			,		lowercase_       :  Dict=32			,		lowercase_       :  Union[str, Any]=0.1			,		lowercase_       :  str=0.0			,		lowercase_       :  str=0.1			,		lowercase_       :  List[str]=0.02			,		lowercase_       :  Union[str, Any]=1E-5			,		lowercase_       :  Tuple=True			,		lowercase_       :  Optional[Any]=2_56			,		lowercase_       :  List[str]=[6, 12, 18]			,		lowercase_       :  Any=0.1			,		lowercase_       :  List[str]=2_55			,		**lowercase_       :  Tuple			,		)      ->		Optional[Any]:
       super().__init__(**lowercase_						)

       lowercase__				:     List[str]					      =       num_channels
       lowercase__				:     Optional[int]					      =       image_size
       lowercase__				:     Any					      =       patch_size
       lowercase__				:     int					      =       hidden_sizes
       lowercase__				:     str					      =       neck_hidden_sizes
       lowercase__				:     Optional[int]					      =       num_attention_heads
       lowercase__				:     Tuple					      =       mlp_ratio
       lowercase__				:     Tuple					      =       expand_ratio
       lowercase__				:     List[str]					      =       hidden_act
       lowercase__				:     Optional[int]					      =       conv_kernel_size
       lowercase__				:     Union[str, Any]					      =       output_stride
       lowercase__				:     Optional[int]					      =       hidden_dropout_prob
       lowercase__				:     List[str]					      =       attention_probs_dropout_prob
       lowercase__				:     Union[str, Any]					      =       classifier_dropout_prob
       lowercase__				:     Union[str, Any]					      =       initializer_range
       lowercase__				:     List[str]					      =       layer_norm_eps
       lowercase__				:     Dict					      =       qkv_bias

       # decode head attributes for semantic segmentation
       lowercase__				:     List[Any]					      =       aspp_out_channels
       lowercase__				:     List[Any]					      =       atrous_rates
       lowercase__				:     str					      =       aspp_dropout_prob
       lowercase__				:     int					      =       semantic_loss_ignore_index



class 						snake_case_ (	__A ):
   __A			:	int        =       version.parse("1.11" )






   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Mapping[str, Mapping[int, str]]:
       return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]						)






   @property
   def    __UpperCamelCase  (   self       :  str						)      ->		Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([("logits", {0: "batch"})]						)
       else:
           return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)







   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		float:
       return 1E-4



