


'''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    				=							logging.getLogger(__name__)

__UpperCAmelCase    				=							int(10_000)  # Hardcoded max length to avoid infinite loop

__UpperCAmelCase    				=							{
    '''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    				=							'''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   (     A       )						->    Optional[int]:
							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   (     A ,    A ,    A ,    A       )						->    str:
							if args.temperature > 0.7:
														logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''       )

							lowerCAmelCase__          =	tokenizer.encode(A ,    add_special_tokens=A       )
							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   (     A ,    A ,    A ,    A       )						->    List[str]:
							# 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(A       )       ) + ''' >>> '''       )

														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 					_snake_case   (     A ,    A ,    A ,    A       )						->    List[Any]:
							lowerCAmelCase__          =	args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
							lowerCAmelCase__          =	prefix + prompt_text
							return prompt_text


def 					_snake_case   (     A ,    A ,    A ,    A       )						->    Optional[int]:
							lowerCAmelCase__          =	args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
							lowerCAmelCase__          =	prefix + prompt_text
							return prompt_text


__UpperCAmelCase    				=							{
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}


def 					_snake_case   (     A ,    A       )						->    Optional[int]:
							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 					_snake_case   (     A       )						->    int:
							lowerCAmelCase__          =	None
							if hasattr(A ,    '''hidden_size'''       ):
														lowerCAmelCase__          =	model_config.hidden_size
							elif hasattr(A ,    '''n_embed'''       ):
														lowerCAmelCase__          =	model_config.n_embed
							elif hasattr(A ,    '''n_embd'''       ):
														lowerCAmelCase__          =	model_config.n_embd

							lowerCAmelCase__          =	None
							if hasattr(A ,    '''num_attention_heads'''       ):
														lowerCAmelCase__          =	model_config.num_attention_heads
							elif hasattr(A ,    '''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(A ,    '''n_layer'''       ):
														lowerCAmelCase__          =	model_config.n_layer
							elif hasattr(A ,    '''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 					_snake_case   (     A ,    A ,    A       )						->    Union[str, Any]:
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	sparse_model_config(model.config       )
							if model.config.model_type == "bloom":
														lowerCAmelCase__          =	tuple(
														    (
														        torch.empty(int(num_attention_heads * batch_size       ) ,    A ,    A       )
														        .to(model.dtype       )
														        .to(model.device       ),
														        torch.empty(int(num_attention_heads * batch_size       ) ,    A ,    A       )
														        .to(model.dtype       )
														        .to(model.device       ),
														    )
														    for _ in range(A       )       )
							else:
														lowerCAmelCase__          =	tuple(
														    (
														        torch.empty(A ,    A ,    A ,    A       )
														        .to(model.dtype       )
														        .to(model.device       ),
														        torch.empty(A ,    A ,    A ,    A       )
														        .to(model.dtype       )
														        .to(model.device       ),
														    )
														    for _ in range(A       )       )
							return past_key_values


def 					_snake_case   (     A ,    A ,    A       )						->    str:
							lowerCAmelCase__          =	len(A       )
							lowerCAmelCase__          =	tokenizer.batch_encode_plus(A ,    return_tensors='''pt'''       )
							lowerCAmelCase__          =	dummy_input.to(model.device       )
							if model.config.use_cache:
														lowerCAmelCase__          =	generate_past_key_values(A ,    A ,    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'''






							lowercase__			:   Optional[int]							=      ("_optimized", "_default")







							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[int]:
														lowerCAmelCase__          =	optimized
														lowerCAmelCase__          =	default







							def __call__(		self					,    *lowerCamelCase_					,    **lowerCamelCase_   )       ->							Any:
														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'''					,    lowerCamelCase_   )
														for k in list(kwargs.keys()   ):
																					if kwargs[k] is None or isinstance(kwargs[k]					,    lowerCamelCase_   ):
																												kwargs.pop(lowerCamelCase_   )
														lowerCAmelCase__          =	self._optimized(**lowerCamelCase_   )
														lowerCAmelCase__          =	outputs[0]
														lowerCAmelCase__          =	outputs[1]
														lowerCAmelCase__          =	CausalLMOutputWithPast(
														    loss=lowerCamelCase_					,    logits=lowerCamelCase_					,    past_key_values=lowerCamelCase_					,    hidden_states=lowerCamelCase_					,    attentions=lowerCamelCase_					,    )
														return fixed_output







							def __getattr__(		self					,    lowerCamelCase_   )       ->							int:
														return getattr(self._default					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_=None					,    lowerCamelCase_=None					,    lowerCamelCase_=None					,    **lowerCamelCase_   )       ->							Dict:
														return self._default.prepare_inputs_for_generation(
														    lowerCamelCase_					,    past_key_values=lowerCamelCase_					,    inputs_embeds=lowerCamelCase_					,    use_cache=lowerCamelCase_					,    **lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Tuple[Tuple[torch.Tensor]]:
														return self._default._reorder_cache(lowerCamelCase_					,    lowerCamelCase_   )


def 					_snake_case   (     )						->    List[Any]:
							lowerCAmelCase__          =	argparse.ArgumentParser()
							parser.add_argument(
							    '''--model_type''' ,    default=A ,    type=A ,    required=A ,    help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()       ) ,    )
							parser.add_argument(
							    '''--model_name_or_path''' ,    default=A ,    type=A ,    required=A ,    help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()       ) ,    )

							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('''--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('''--num_return_sequences''' ,    type=A ,    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(A       )

							# 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=A       )
							logger.info(A       )

							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(A ,    A ,    A ,    A       )

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

														lowerCAmelCase__          =	tokenizer.encode(
														    A ,    add_special_tokens=A ,    return_tensors='''pt''' ,    **A       )
							else:
														lowerCAmelCase__          =	args.prefix if args.prefix else args.padding_text
														lowerCAmelCase__          =	tokenizer.encode(prefix + prompt_text ,    add_special_tokens=A ,    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(A ,    A ,    A       )
														torch._C._jit_set_texpr_fuser_enabled(A       )
														lowerCAmelCase__          =	False
														if hasattr(A ,    '''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(A ,    A       ) is not None       )
														lowerCAmelCase__          =	torch.jit.trace(A ,    A ,    strict=A       )
														lowerCAmelCase__          =	torch.jit.freeze(traced_model.eval()       )
														traced_model(*A       )
														traced_model(*A       )

														lowerCAmelCase__          =	_ModelFallbackWrapper(A ,    A       )

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

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

														# 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=A       )       ) :]
														)

														generated_sequences.append(A       )
														print(A       )

							return generated_sequences


if __name__ == "__main__":
		main()



'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__UpperCAmelCase    				=							logging.get_logger(__name__)




class 		a__				(							a__  ):





							'''simple docstring'''






							def __init__(		self					,    *lowerCamelCase_					,    **lowerCamelCase_   )       ->							None:
														warnings.warn(
														    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
														    ''' Please use MobileNetV2ImageProcessor instead.'''					,    lowerCamelCase_					,    )
														super().__init__(*lowerCamelCase_					,    **lowerCamelCase_   )