
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,
)
SCREAMING_SNAKE_CASE__							=    logging.getLogger(__name__)




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




def 							__SCREAMING_SNAKE_CASE       (		)					->						Dict:
				__lowercase								= 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								= parser.parse_args()

				__lowercase								= torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
				__lowercase								= 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								= AutoTokenizer.from_pretrained(args.model_name_or_path      )
				__lowercase								= 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								= args.prompt if args.prompt else input('Model prompt >>> '      )

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

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

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

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

								# Remove all text after the stop token
								__lowercase								= 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								= (
								    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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


											super().__init__(pad_token_id=_UpperCAmelCase					,			**_UpperCAmelCase    )

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





							@property
							def       a__			(			self  :       Any    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


											if self.task == "multiple-choice":
															__lowercase								= {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															__lowercase								= {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]    )





