


'''simple docstring'''

import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
_UpperCAmelCase					:			List[Any]				     =					logging.getLogger(__name__)

_UpperCAmelCase					:			Dict				     =					int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

_UpperCAmelCase					:			Union[str, Any]				     =					{
    """gpt2""": (GPTaLMHeadModel, GPTaTokenizer),
    """ctrl""": (CTRLLMHeadModel, CTRLTokenizer),
    """openai-gpt""": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    """xlnet""": (XLNetLMHeadModel, XLNetTokenizer),
    """transfo-xl""": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    """xlm""": (XLMWithLMHeadModel, XLMTokenizer),
    """gptj""": (GPTJForCausalLM, AutoTokenizer),
    """bloom""": (BloomForCausalLM, BloomTokenizerFast),
    """llama""": (LlamaForCausalLM, LlamaTokenizer),
    """opt""": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_UpperCAmelCase					:			Dict				     =					"""In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						if args.temperature > 0.7:
												logger.info('''CTRL typically works better with lower temperatures (and lower top_k).''')

						__lowerCAmelCase											=					tokenizer.encode(lowerCamelCase,					add_special_tokens=lowerCamelCase)
						if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
												logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results''')
						return prompt_text

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						# kwargs = {"language": None, "mask_token_id": None}

						# Set the language
						__lowerCAmelCase											=					hasattr(model.config,					'''use_lang_emb''') and model.config.use_lang_emb
						if hasattr(model.config,					'''lang2id''') and use_lang_emb:
												__lowerCAmelCase											=					model.config.langaid.keys()
												if args.xlm_language in available_languages:
																		__lowerCAmelCase											=					args.xlm_language
												else:
																		__lowerCAmelCase											=					None
																		while language not in available_languages:
																								__lowerCAmelCase											=					input('''Using XLM. Select language in ''' + str(list(lowerCamelCase)) + ''' >>> ''')

												__lowerCAmelCase											=					model.config.langaid[language]
												# kwargs["language"] = tokenizer.lang2id[language]

						# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
						# XLM masked-language modeling (MLM) models need masked token
						# is_xlm_mlm = "mlm" in args.model_name_or_path
						# if is_xlm_mlm:
						#     kwargs["mask_token_id"] = tokenizer.mask_token_id

						return prompt_text

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						__lowerCAmelCase											=					prefix + prompt_text
						return prompt_text

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						__lowerCAmelCase											=					prefix + prompt_text
						return prompt_text


_UpperCAmelCase					:			Dict				     =					{
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						if length < 0 and max_sequence_length > 0:
												__lowerCAmelCase											=					max_sequence_length
						elif 0 < max_sequence_length < length:
												__lowerCAmelCase											=					max_sequence_length  # No generation bigger than model size
						elif length < 0:
												__lowerCAmelCase											=					MAX_LENGTH  # avoid infinite loop
						return length

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					None
						if hasattr(lowerCamelCase,					'''hidden_size'''):
												__lowerCAmelCase											=					model_config.hidden_size
						elif hasattr(lowerCamelCase,					'''n_embed'''):
												__lowerCAmelCase											=					model_config.n_embed
						elif hasattr(lowerCamelCase,					'''n_embd'''):
												__lowerCAmelCase											=					model_config.n_embd

						__lowerCAmelCase											=					None
						if hasattr(lowerCamelCase,					'''num_attention_heads'''):
												__lowerCAmelCase											=					model_config.num_attention_heads
						elif hasattr(lowerCamelCase,					'''n_head'''):
												__lowerCAmelCase											=					model_config.n_head

						if embedding_size is None or num_head is None or num_head == 0:
												raise ValueError('''Check the model config''')

						__lowerCAmelCase											=					int(embedding_size / num_head)
						if hasattr(lowerCamelCase,					'''n_layer'''):
												__lowerCAmelCase											=					model_config.n_layer
						elif hasattr(lowerCamelCase,					'''num_hidden_layers'''):
												__lowerCAmelCase											=					model_config.num_hidden_layers
						else:
												raise ValueError('''Number of hidden layers couldn\'t be determined from the model config''')

						return num_layer, num_head, num_embedding_size_per_head

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					sparse_model_config(model.config)
						if model.config.model_type == "bloom":
												__lowerCAmelCase											=					tuple(
												    (
												        torch.empty(int(num_attention_heads * batch_size),					lowerCamelCase,					lowerCamelCase)
												        .to(model.dtype)
												        .to(model.device),
												        torch.empty(int(num_attention_heads * batch_size),					lowerCamelCase,					lowerCamelCase)
												        .to(model.dtype)
												        .to(model.device),
												    )
												    for _ in range(lowerCamelCase))
						else:
												__lowerCAmelCase											=					tuple(
												    (
												        torch.empty(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase)
												        .to(model.dtype)
												        .to(model.device),
												        torch.empty(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase)
												        .to(model.dtype)
												        .to(model.device),
												    )
												    for _ in range(lowerCamelCase))
						return past_key_values

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					len(lowerCamelCase)
						__lowerCAmelCase											=					tokenizer.batch_encode_plus(lowerCamelCase,					return_tensors='''pt''')
						__lowerCAmelCase											=					dummy_input.to(model.device)
						if model.config.use_cache:
												__lowerCAmelCase											=					generate_past_key_values(lowerCamelCase,					lowerCamelCase,					1)
						__lowerCAmelCase											=					torch.cat(
						    [
						        torch.zeros(dummy_input['''attention_mask'''].shape[0],					1)
						        .to(dummy_input['''attention_mask'''].dtype)
						        .to(model.device),
						        dummy_input['''attention_mask'''],
						    ],					-1,					)
						return dummy_input


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[str, Any]						=						('_optimized', '_default')




				def __init__(self							,      __lowercase							,      __lowercase		):
										__lowerCAmelCase											=					optimized
										__lowerCAmelCase											=					default




				def __call__(self							,      *__lowercase							,      **__lowercase		):
										if kwargs["past_key_values"] is None and self._default.config.use_cache:
																__lowerCAmelCase											=					generate_past_key_values(self._default							,      kwargs['''input_ids'''].shape[0]							,      0		)
										kwargs.pop('''position_ids'''							,      __lowercase		)
										for k in list(kwargs.keys()		):
																if kwargs[k] is None or isinstance(kwargs[k]							,      __lowercase		):
																						kwargs.pop(__lowercase		)
										__lowerCAmelCase											=					self._optimized(**__lowercase		)
										__lowerCAmelCase											=					outputs[0]
										__lowerCAmelCase											=					outputs[1]
										__lowerCAmelCase											=					CausalLMOutputWithPast(
										    loss=__lowercase							,      logits=__lowercase							,      past_key_values=__lowercase							,      hidden_states=__lowercase							,      attentions=__lowercase							,      )
										return fixed_output




				def __getattr__(self							,      __lowercase		):
										return getattr(self._default							,      __lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      **__lowercase		):
										return self._default.prepare_inputs_for_generation(
										    __lowercase							,      past_key_values=__lowercase							,      inputs_embeds=__lowercase							,      use_cache=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase		):
										return self._default._reorder_cache(__lowercase							,      __lowercase		)

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser()
						parser.add_argument(
						    '''--model_type''',					default=lowerCamelCase,					type=lowerCamelCase,					required=lowerCamelCase,					help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()),					)
						parser.add_argument(
						    '''--model_name_or_path''',					default=lowerCamelCase,					type=lowerCamelCase,					required=lowerCamelCase,					help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()),					)

						parser.add_argument('''--prompt''',					type=lowerCamelCase,					default='''''')
						parser.add_argument('''--length''',					type=lowerCamelCase,					default=2_0)
						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('''--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=4_2,					help='''random seed for initialization''')
						parser.add_argument('''--no_cuda''',					action='''store_true''',					help='''Avoid using CUDA when available''')
						parser.add_argument('''--num_return_sequences''',					type=lowerCamelCase,					default=1,					help='''The number of samples to generate.''')
						parser.add_argument(
						    '''--fp16''',					action='''store_true''',					help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',					)
						parser.add_argument('''--jit''',					action='''store_true''',					help='''Whether or not to use jit trace to accelerate inference''')
						__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(lowerCamelCase)

						# Initialize the model and tokenizer
						try:
												__lowerCAmelCase											=					args.model_type.lower()
												__lowerCAmelCase							,       __lowerCAmelCase											=					MODEL_CLASSES[args.model_type]
						except KeyError:
												raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)''')

						__lowerCAmelCase											=					tokenizer_class.from_pretrained(args.model_name_or_path)
						if tokenizer.pad_token is None:
												__lowerCAmelCase											=					tokenizer.eos_token
						__lowerCAmelCase											=					model_class.from_pretrained(args.model_name_or_path)
						model.to(args.device)

						if args.fpaa:
												model.half()
						__lowerCAmelCase											=					getattr(model.config,					'''max_position_embeddings''',					0)
						__lowerCAmelCase											=					adjust_length_to_model(args.length,					max_sequence_length=lowerCamelCase)
						logger.info(lowerCamelCase)

						__lowerCAmelCase											=					args.prompt if args.prompt else input('''Model prompt >>> ''')

						# Different models need different input formatting and/or extra arguments
						__lowerCAmelCase											=					args.model_type in PREPROCESSING_FUNCTIONS.keys()
						if requires_preprocessing:
												__lowerCAmelCase											=					PREPROCESSING_FUNCTIONS.get(args.model_type)
												__lowerCAmelCase											=					prepare_input(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

												if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
																		__lowerCAmelCase											=					{'''add_space_before_punct_symbol''': True}
												else:
																		__lowerCAmelCase											=					{}

												__lowerCAmelCase											=					tokenizer.encode(
												    lowerCamelCase,					add_special_tokens=lowerCamelCase,					return_tensors='''pt''',					**lowerCamelCase)
						else:
												__lowerCAmelCase											=					args.prefix if args.prefix else args.padding_text
												__lowerCAmelCase											=					tokenizer.encode(prefix + prompt_text,					add_special_tokens=lowerCamelCase,					return_tensors='''pt''')
						__lowerCAmelCase											=					encoded_prompt.to(args.device)

						if encoded_prompt.size()[-1] == 0:
												__lowerCAmelCase											=					None
						else:
												__lowerCAmelCase											=					encoded_prompt

						if args.jit:
												__lowerCAmelCase											=					['''enable jit''']
												__lowerCAmelCase											=					prepare_jit_inputs(lowerCamelCase,					lowerCamelCase,					lowerCamelCase)
												torch._C._jit_set_texpr_fuser_enabled(lowerCamelCase)
												__lowerCAmelCase											=					False
												if hasattr(lowerCamelCase,					'''forward'''):
																		__lowerCAmelCase											=					inspect.signature(model.forward)
												else:
																		__lowerCAmelCase											=					inspect.signature(model.__call__)
												__lowerCAmelCase											=					tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(lowerCamelCase,					lowerCamelCase) is not None)
												__lowerCAmelCase											=					torch.jit.trace(lowerCamelCase,					lowerCamelCase,					strict=lowerCamelCase)
												__lowerCAmelCase											=					torch.jit.freeze(traced_model.eval())
												traced_model(*lowerCamelCase)
												traced_model(*lowerCamelCase)

												__lowerCAmelCase											=					_ModelFallbackWrapper(lowerCamelCase,					lowerCamelCase)

						__lowerCAmelCase											=					model.generate(
						    input_ids=lowerCamelCase,					max_length=args.length + len(encoded_prompt[0]),					temperature=args.temperature,					top_k=args.k,					top_p=args.p,					repetition_penalty=args.repetition_penalty,					do_sample=lowerCamelCase,					num_return_sequences=args.num_return_sequences,					)

						# Remove the batch dimension when returning multiple sequences
						if len(output_sequences.shape) > 2:
												output_sequences.squeeze_()

						__lowerCAmelCase											=					[]

						for generated_sequence_idx, generated_sequence in enumerate(lowerCamelCase):
												print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===""")
												__lowerCAmelCase											=					generated_sequence.tolist()

												# Decode text
												__lowerCAmelCase											=					tokenizer.decode(lowerCamelCase,					clean_up_tokenization_spaces=lowerCamelCase)

												# 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(encoded_prompt[0],					clean_up_tokenization_spaces=lowerCamelCase)) :]
												)

												generated_sequences.append(lowerCamelCase)
												print(lowerCamelCase)

						return generated_sequences


if __name__ == "__main__":
	main()





'''simple docstring'''

import math

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					math.log(2,					1_0) * number
						__lowerCAmelCase											=					round((log_number - int(lowerCamelCase)),					1_5)

						return difference

def 				__magic_name__(    lowerCamelCase = 6_7_8_9_1_0):
						__lowerCAmelCase											=					9_0
						__lowerCAmelCase											=					0

						__lowerCAmelCase											=					math.log(1.23,					1_0)
						__lowerCAmelCase											=					math.log(1.24,					1_0)
						__lowerCAmelCase											=					0

						while position < number:
												__lowerCAmelCase											=					log_difference(lowerCamelCase)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__lowerCAmelCase											=					power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f"""{solution() = }""")

