


"""simple docstring"""




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__	:   Any          =							logging.getLogger(__name__)





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





def 			a_   (  ):
			__lowerCamelCase        =			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""", )
			__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(lowercase__    )

			# 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(lowercase__    )
			__lowerCamelCase        =			args.prompt if args.prompt else input("""Model prompt >>> """    )

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

			__lowerCamelCase        =			model.generate(
			    **lowercase__, 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(lowercase__    ):
						print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='    )
						__lowerCamelCase        =			generated_sequence.tolist()

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

						# 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=lowercase__    )    ) :]
						)

						generated_sequences.append(lowercase__    )
						print(lowercase__    )

			return generated_sequences


if __name__ == "__main__":
					main()








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__	:   Optional[Any]          =							{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   List[Any]          =							[
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   Tuple          =							[
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




