






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,
)
snake_case_   :	List[Any]							      =logging.getLogger(__name__)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							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"					,     )
		__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(lowerCAmelCase__	)

		# 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(lowerCAmelCase__	)
		__A    =							args.prompt if args.prompt else input("Model prompt >>> "	)

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

		__A    =							model.generate(
		    **lowerCAmelCase__					,     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(lowerCAmelCase__	):
				print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""	)
				__A    =							generated_sequence.tolist()

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

				# 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=lowerCAmelCase__	)	) :]
				)

				generated_sequences.append(lowerCAmelCase__	)
				print(lowerCAmelCase__	)

		return generated_sequences


if __name__ == "__main__":
					main()






from typing import TYPE_CHECKING

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


UpperCAmelCase :				List[Any]							={
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""RetriBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				str							=[
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase :				List[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
