


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_  :	str							  =							logging.getLogger(__name__)







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







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

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

				parser.add_argument('''--seed'''		,type=a__		,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'''		,)
				_SCREAMING_SNAKE_CASE	     =parser.parse_args()

				_SCREAMING_SNAKE_CASE	     =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
				_SCREAMING_SNAKE_CASE	     =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(a__)

				# Initialize the model and tokenizer
				_SCREAMING_SNAKE_CASE	     =AutoTokenizer.from_pretrained(args.model_name_or_path)
				_SCREAMING_SNAKE_CASE	     =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(a__)
				_SCREAMING_SNAKE_CASE	     =args.prompt if args.prompt else input('''Model prompt >>> ''')

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

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

				_SCREAMING_SNAKE_CASE	     =[]
				for generated_sequence_idx, generated_sequence in enumerate(a__):
								print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
								_SCREAMING_SNAKE_CASE	     =generated_sequence.tolist()

								# Decode text
								_SCREAMING_SNAKE_CASE	     =tokenizer.decode(a__		,clean_up_tokenization_spaces=a__		,add_special_tokens=a__)

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

								generated_sequences.append(a__)
								print(a__)

				return generated_sequences


if __name__ == "__main__":
							main()



from __future__ import annotations

from collections.abc import Callable







def   lowerCamelCase(      a__		,a__		,a__		,a__ = 100		,):
				_SCREAMING_SNAKE_CASE	     =x_start
				_SCREAMING_SNAKE_CASE	     =fnc(a__)
				_SCREAMING_SNAKE_CASE	     =0.0

				for _ in range(a__):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_SCREAMING_SNAKE_CASE	     =(x_end - x_start) / steps + xa
								_SCREAMING_SNAKE_CASE	     =fnc(a__)
								area += abs(fxa + fxa) * (xa - xa) / 2

								# Increment step
								_SCREAMING_SNAKE_CASE	     =xa
								_SCREAMING_SNAKE_CASE	     =fxa
				return area


if __name__ == "__main__":







							def   lowerCamelCase(      a__):
											return x**3

							print('''f(x) = x^3''')
							print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
							snake_case_  :	Any							  =							10
							while i <= 10_00_00:
														snake_case_  :	Tuple							  =							trapezoidal_area(f, -5, 5, i)
														print(f"""with {i} steps: {area}""")
														i *= 10