"""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_				:				Any							      =						logging.getLogger(__name__)

UpperCamelCase_				:				Union[str, Any]							      =						int(1_0000)  # Hardcoded max length to avoid infinite loop

UpperCamelCase_				:				Dict							      =						{
    """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_				:				Optional[int]							      =						"""In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"""


def      a__					(						UpperCAmelCase__				:	List[Any]	)->		str:
						np.random.seed(args.seed	)
						torch.manual_seed(args.seed	)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed	)


def      a__					(						UpperCAmelCase__				:	Dict						,	UpperCAmelCase__				:	Any						,	UpperCAmelCase__				:	Tuple						,	UpperCAmelCase__				:	Optional[Any]	)->		int:
						if args.temperature > 0.7:
												logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""	)

						UpperCAmelCase__    		=						tokenizer.encode(UpperCamelCase__						,	add_special_tokens=UpperCamelCase__	)
						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      a__					(						UpperCAmelCase__				:	Any						,	UpperCAmelCase__				:	Optional[int]						,	UpperCAmelCase__				:	Union[str, Any]						,	UpperCAmelCase__				:	List[Any]	)->		str:
						# kwargs = {"language": None, "mask_token_id": None}

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

												UpperCAmelCase__    		=						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      a__					(						UpperCAmelCase__				:	Optional[Any]						,	UpperCAmelCase__				:	Optional[int]						,	UpperCAmelCase__				:	Union[str, Any]						,	UpperCAmelCase__				:	Union[str, Any]	)->		Dict:
						UpperCAmelCase__    		=						args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						UpperCAmelCase__    		=						prefix + prompt_text
						return prompt_text


def      a__					(						UpperCAmelCase__				:	str						,	UpperCAmelCase__				:	List[str]						,	UpperCAmelCase__				:	str						,	UpperCAmelCase__				:	List[str]	)->		Any:
						UpperCAmelCase__    		=						args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						UpperCAmelCase__    		=						prefix + prompt_text
						return prompt_text


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


def      a__					(						UpperCAmelCase__				:	List[Any]						,	UpperCAmelCase__				:	List[Any]	)->		Any:
						if length < 0 and max_sequence_length > 0:
												UpperCAmelCase__    		=						max_sequence_length
						elif 0 < max_sequence_length < length:
												UpperCAmelCase__    		=						max_sequence_length  # No generation bigger than model size
						elif length < 0:
												UpperCAmelCase__    		=						MAX_LENGTH  # avoid infinite loop
						return length


def      a__					(						UpperCAmelCase__				:	Optional[int]	)->		Any:
						UpperCAmelCase__    		=						None
						if hasattr(UpperCamelCase__						,	"""hidden_size"""	):
												UpperCAmelCase__    		=						model_config.hidden_size
						elif hasattr(UpperCamelCase__						,	"""n_embed"""	):
												UpperCAmelCase__    		=						model_config.n_embed
						elif hasattr(UpperCamelCase__						,	"""n_embd"""	):
												UpperCAmelCase__    		=						model_config.n_embd

						UpperCAmelCase__    		=						None
						if hasattr(UpperCamelCase__						,	"""num_attention_heads"""	):
												UpperCAmelCase__    		=						model_config.num_attention_heads
						elif hasattr(UpperCamelCase__						,	"""n_head"""	):
												UpperCAmelCase__    		=						model_config.n_head

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

						UpperCAmelCase__    		=						int(embedding_size / num_head	)
						if hasattr(UpperCamelCase__						,	"""n_layer"""	):
												UpperCAmelCase__    		=						model_config.n_layer
						elif hasattr(UpperCamelCase__						,	"""num_hidden_layers"""	):
												UpperCAmelCase__    		=						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      a__					(						UpperCAmelCase__				:	Tuple						,	UpperCAmelCase__				:	str						,	UpperCAmelCase__				:	Optional[int]	)->		List[str]:
						UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__    		=						sparse_model_config(model.config	)
						if model.config.model_type == "bloom":
												UpperCAmelCase__    		=						tuple(
												    (
												        torch.empty(int(num_attention_heads * batch_size	)						,	UpperCamelCase__						,	UpperCamelCase__	)
												        .to(model.dtype	)
												        .to(model.device	),
												        torch.empty(int(num_attention_heads * batch_size	)						,	UpperCamelCase__						,	UpperCamelCase__	)
												        .to(model.dtype	)
												        .to(model.device	),
												    )
												    for _ in range(UpperCamelCase__	)	)
						else:
												UpperCAmelCase__    		=						tuple(
												    (
												        torch.empty(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__	)
												        .to(model.dtype	)
												        .to(model.device	),
												        torch.empty(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__	)
												        .to(model.dtype	)
												        .to(model.device	),
												    )
												    for _ in range(UpperCamelCase__	)	)
						return past_key_values


def      a__					(						UpperCAmelCase__				:	List[Any]						,	UpperCAmelCase__				:	Dict						,	UpperCAmelCase__				:	int	)->		Dict:
						UpperCAmelCase__    		=						len(UpperCamelCase__	)
						UpperCAmelCase__    		=						tokenizer.batch_encode_plus(UpperCamelCase__						,	return_tensors="""pt"""	)
						UpperCAmelCase__    		=						dummy_input.to(model.device	)
						if model.config.use_cache:
												UpperCAmelCase__    		=						generate_past_key_values(UpperCamelCase__						,	UpperCamelCase__						,	1	)
						UpperCAmelCase__    		=						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 	_lowercase						(      UpperCamelCase__     ):
				lowerCamelCase__ 						=				('_optimized', '_default')

				def __init__(   self				:			Optional[Any]				,			_lowercase				:			Dict				,			_lowercase				:			Optional[int]   ):


										'''simple docstring'''
										UpperCAmelCase__    		=						optimized
										UpperCAmelCase__    		=						default

				def __call__(   self				:			Any				,			*_lowercase				:			Dict				,			**_lowercase				:			Optional[int]   ):


										'''simple docstring'''



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

				def __getattr__(   self				:			Union[str, Any]				,			_lowercase				:			str   ):


										'''simple docstring'''
										return getattr(self._default				,			__A   )

				def 				A       (   self				:			Any				,			_lowercase				:			Tuple				,			_lowercase				:			Any=None				,			_lowercase				:			Optional[Any]=None				,			_lowercase				:			Union[str, Any]=None				,			**_lowercase				:			List[Any]   ):


										'''simple docstring'''
										return self._default.prepare_inputs_for_generation(
										    __A				,			past_key_values=__A				,			inputs_embeds=__A				,			use_cache=__A				,			**__A   )

				def 				A       (   self				:			List[str]				,			_lowercase				:			Tuple[Tuple[torch.Tensor]]				,			_lowercase				:			torch.Tensor   ):


										'''simple docstring'''
										return self._default._reorder_cache(__A				,			__A   )


def      a__					(						)->		List[str]:
						UpperCAmelCase__    		=						argparse.ArgumentParser()
						parser.add_argument(
						    """--model_type"""						,	default=UpperCamelCase__						,	type=UpperCamelCase__						,	required=UpperCamelCase__						,	help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()	)						,	)
						parser.add_argument(
						    """--model_name_or_path"""						,	default=UpperCamelCase__						,	type=UpperCamelCase__						,	required=UpperCamelCase__						,	help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()	)						,	)

						parser.add_argument("""--prompt"""						,	type=UpperCamelCase__						,	default=""""""	)
						parser.add_argument("""--length"""						,	type=UpperCamelCase__						,	default=2_0	)
						parser.add_argument("""--stop_token"""						,	type=UpperCamelCase__						,	default=UpperCamelCase__						,	help="""Token at which text generation is stopped"""	)

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

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

						parser.add_argument("""--seed"""						,	type=UpperCamelCase__						,	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=UpperCamelCase__						,	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"""	)
						UpperCAmelCase__    		=						parser.parse_args()

						UpperCAmelCase__    		=						torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""	)
						UpperCAmelCase__    		=						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(UpperCamelCase__	)

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

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

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

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

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

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

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

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

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

												UpperCAmelCase__    		=						_ModelFallbackWrapper(UpperCamelCase__						,	UpperCamelCase__	)

						UpperCAmelCase__    		=						model.generate(
						    input_ids=UpperCamelCase__						,	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=UpperCamelCase__						,	num_return_sequences=args.num_return_sequences						,	)

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

						UpperCAmelCase__    		=						[]

						for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__	):
												print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='	)
												UpperCAmelCase__    		=						generated_sequence.tolist()

												# Decode text
												UpperCAmelCase__    		=						tokenizer.decode(UpperCamelCase__						,	clean_up_tokenization_spaces=UpperCamelCase__	)

												# Remove all text after the stop token
												UpperCAmelCase__    		=						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
												UpperCAmelCase__    		=						(
												    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]						,	clean_up_tokenization_spaces=UpperCamelCase__	)	) :]
												)

												generated_sequences.append(UpperCamelCase__	)
												print(UpperCamelCase__	)

						return generated_sequences


if __name__ == "__main__":
						main()








import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
	import torch




class      UpperCamelCase_				(   unittest.TestCase ,     UpperCamelCase__			):


							def    _snake_case     (							self			:Dict						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					load_tool("""speech-to-text"""						)
									self.tool.setup()


							def    _snake_case     (							self			:str						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)


							def    _snake_case     (							self			:Union[str, Any]						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(audio=torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)