
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,
)
_snake_case			:       Optional[int]      =						logging.getLogger(__name__)

_snake_case			:       int      =						int(1_00_00)  # Hardcoded max length to avoid infinite loop

_snake_case			:       List[str]      =						{
    """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
_snake_case			:       List[Any]      =						"""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    __snake_case       (  __magic_name__	):







				'''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    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if args.temperature > 0.7:
								logger.info("CTRL typically works better with lower temperatures (and lower top_k)."	)

				lowercase						    =			tokenizer.encode(__magic_name__		,						add_special_tokens=__magic_name__	)
				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    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			hasattr(model.config		,						"use_lang_emb"	) and model.config.use_lang_emb
				if hasattr(model.config		,						"lang2id"	) and use_lang_emb:
								lowercase						    =			model.config.langaid.keys()
								if args.xlm_language in available_languages:
												lowercase						    =			args.xlm_language
								else:
												lowercase						    =			None
												while language not in available_languages:
																lowercase						    =			input("Using XLM. Select language in " + str(list(__magic_name__	)	) + " >>> "	)

								lowercase						    =			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    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				lowercase						    =			prefix + prompt_text
				return prompt_text

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				lowercase						    =			prefix + prompt_text
				return prompt_text


_snake_case			:       List[Any]      =						{
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if length < 0 and max_sequence_length > 0:
								lowercase						    =			max_sequence_length
				elif 0 < max_sequence_length < length:
								lowercase						    =			max_sequence_length  # No generation bigger than model size
				elif length < 0:
								lowercase						    =			MAX_LENGTH  # avoid infinite loop
				return length

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


				lowercase						    =			None
				if hasattr(__magic_name__		,						"hidden_size"	):
								lowercase						    =			model_config.hidden_size
				elif hasattr(__magic_name__		,						"n_embed"	):
								lowercase						    =			model_config.n_embed
				elif hasattr(__magic_name__		,						"n_embd"	):
								lowercase						    =			model_config.n_embd

				lowercase						    =			None
				if hasattr(__magic_name__		,						"num_attention_heads"	):
								lowercase						    =			model_config.num_attention_heads
				elif hasattr(__magic_name__		,						"n_head"	):
								lowercase						    =			model_config.n_head

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

				lowercase						    =			int(embedding_size / num_head	)
				if hasattr(__magic_name__		,						"n_layer"	):
								lowercase						    =			model_config.n_layer
				elif hasattr(__magic_name__		,						"num_hidden_layers"	):
								lowercase						    =			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    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


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

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			len(__magic_name__	)
				lowercase						    =			tokenizer.batch_encode_plus(__magic_name__		,						return_tensors="pt"	)
				lowercase						    =			dummy_input.to(model.device	)
				if model.config.use_cache:
								lowercase						    =			generate_past_key_values(__magic_name__		,						__magic_name__		,						1	)
				lowercase						    =			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      UpperCamelCase_			(			lowerCAmelCase_		):







						'''simple docstring'''



						UpperCamelCase			:     Optional[Any]							      =				("""_optimized""", """_default""")





						def __init__( self       :Any		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :Optional[Any]    )     ->Optional[int]:
										lowercase						    =			optimized
										lowercase						    =			default





						def __call__( self       :List[str]		,						*lowerCAmelCase__       :Tuple		,						**lowerCAmelCase__       :Tuple    )     ->List[str]:

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





						def __getattr__( self       :str		,						lowerCAmelCase__       :List[str]    )     ->Any:
										return getattr(self._default		,						__SCREAMING_SNAKE_CASE    )





						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :int=None		,						lowerCAmelCase__       :Dict=None		,						lowerCAmelCase__       :str=None		,						**lowerCAmelCase__       :Optional[int]    )     ->List[str]:
										return self._default.prepare_inputs_for_generation(
										    __SCREAMING_SNAKE_CASE		,						past_key_values=__SCREAMING_SNAKE_CASE		,						inputs_embeds=__SCREAMING_SNAKE_CASE		,						use_cache=__SCREAMING_SNAKE_CASE		,						**__SCREAMING_SNAKE_CASE    )





						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :int    )     ->List[Any]:
										return self._default._reorder_cache(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE    )




def    __snake_case       (  ):







				'''simple docstring'''


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

				parser.add_argument("--prompt"		,						type=__magic_name__		,						default=""	)
				parser.add_argument("--length"		,						type=__magic_name__		,						default=20	)
				parser.add_argument("--stop_token"		,						type=__magic_name__		,						default=__magic_name__		,						help="Token at which text generation is stopped"	)

				parser.add_argument(
				    "--temperature"		,						type=__magic_name__		,						default=1.0		,						help="temperature of 1.0 has no effect, lower tend toward greedy sampling"		,						)
				parser.add_argument(
				    "--repetition_penalty"		,						type=__magic_name__		,						default=1.0		,						help="primarily useful for CTRL model; in that case, use 1.2"	)
				parser.add_argument("--k"		,						type=__magic_name__		,						default=0	)
				parser.add_argument("--p"		,						type=__magic_name__		,						default=0.9	)

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

				parser.add_argument("--seed"		,						type=__magic_name__		,						default=42		,						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=__magic_name__		,						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"	)
				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(__magic_name__	)

				# Initialize the model and tokenizer
				try:
								lowercase						    =			args.model_type.lower()
								lowercase    ,  lowercase						    =			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 :)"	)

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

				if args.fpaa:
								model.half()
				lowercase						    =			getattr(model.config		,						"max_position_embeddings"		,						0	)
				lowercase						    =			adjust_length_to_model(args.length		,						max_sequence_length=__magic_name__	)
				logger.info(__magic_name__	)

				lowercase						    =			args.prompt if args.prompt else input("Model prompt >>> "	)

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

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

								lowercase						    =			tokenizer.encode(
								    __magic_name__		,						add_special_tokens=__magic_name__		,						return_tensors="pt"		,						**__magic_name__	)
				else:
								lowercase						    =			args.prefix if args.prefix else args.padding_text
								lowercase						    =			tokenizer.encode(prefix + prompt_text		,						add_special_tokens=__magic_name__		,						return_tensors="pt"	)
				lowercase						    =			encoded_prompt.to(args.device	)

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

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

								lowercase						    =			_ModelFallbackWrapper(__magic_name__		,						__magic_name__	)

				lowercase						    =			model.generate(
				    input_ids=__magic_name__		,						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=__magic_name__		,						num_return_sequences=args.num_return_sequences		,						)

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

				lowercase						    =			[]

				for generated_sequence_idx, generated_sequence in enumerate(__magic_name__	):
								print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''	)
								lowercase						    =			generated_sequence.tolist()

								# Decode text
								lowercase						    =			tokenizer.decode(__magic_name__		,						clean_up_tokenization_spaces=__magic_name__	)

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

								generated_sequences.append(__magic_name__	)
								print(__magic_name__	)

				return generated_sequences


if __name__ == "__main__":
						main()




from __future__ import annotations

import random
import string





class      UpperCamelCase_			:







						'''simple docstring'''



						def __init__( self       :Dict		,						lowerCAmelCase__       :str | None = None    )     ->None:
										lowercase						    =			passcode or self.__passcode_creator()
										lowercase						    =			self.__make_key_list()
										lowercase						    =			self.__make_shift_key()





						def __str__( self       :Dict    )     ->str:
										return "".join(self.__passcode    )





						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :list[int]    )     ->list[int]:
										for i in range(1		,						len(lowerCAmelCase__    )		,						2    ):
														iterlist[i] *= -1
										return iterlist





						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->list[str]:
										lowercase						    =			string.ascii_letters + string.digits
										lowercase						    =			[random.choice(lowerCAmelCase__    ) for _ in range(random.randint(10		,						20    )    )]
										return password





						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]    )     ->list[str]:
										lowercase						    =			(
										    string.ascii_letters + string.digits + string.punctuation + " \t\n"
										)

										lowercase						    =			[]

										# creates points known as breakpoints to break the key_list_options at those
										# points and pivot each substring
										lowercase						    =			sorted(set(self.__passcode    )    )
										lowercase						    =			[]

										# algorithm for creating a new shuffled list, keys_l, out of key_list_options
										for i in key_list_options:
														temp_list.extend(lowerCAmelCase__    )

														# checking breakpoints at which to pivot temporary sublist and add it into
														# keys_l
														if i in breakpoints or i == key_list_options[-1]:
																		keys_l.extend(temp_list[::-1]    )
																		temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
										return keys_l





						def 					SCREAMING_SNAKE_CASE( self       :Optional[int]    )     ->int:
										lowercase						    =			sum(self.__neg_pos([ord(lowerCAmelCase__    ) for x in self.__passcode]    )    )
										return num if num > 0 else len(self.__passcode    )





						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :str    )     ->str:
										lowercase						    =			""

										# decoding shift like Caesar cipher algorithm implementing negative shift or
										# reverse shift or left shift
										for i in encoded_message:
														lowercase						    =			self.__key_list.index(lowerCAmelCase__    )
														decoded_message += self.__key_list[
														    (position - self.__shift_key) % -len(self.__key_list    )
														]

										return decoded_message





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :str    )     ->str:
										lowercase						    =			""

										# encoding shift like Caesar cipher algorithm implementing positive shift or
										# forward shift or right shift
										for i in plaintext:
														lowercase						    =			self.__key_list.index(lowerCAmelCase__    )
														encoded_message += self.__key_list[
														    (position + self.__shift_key) % len(self.__key_list    )
														]

										return encoded_message




def    __snake_case       (  __magic_name__ = "Hello, this is a modified Caesar cipher"	):







				'''simple docstring'''


				lowercase						    =			ShuffledShiftCipher()
				return cipa.decrypt(cipa.encrypt(__magic_name__	)	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
