



'''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,
)
_lowercase       :						List[str]						=logging.getLogger(__name__)

_lowercase       :						int						=int(1_0000)  # Hardcoded max length to avoid infinite loop

_lowercase       :						Optional[int]						={
    "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
_lowercase       :						List[Any]						="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 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Optional[Any]:
				np.random.seed(args.seed			)
				torch.manual_seed(args.seed			)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed			)


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

				snake_case__      :   str      =    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 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Any   ,				UpperCamelCase__				:Any   ,				UpperCamelCase__				:int			)		->						List[Any]:
				# kwargs = {"language": None, "mask_token_id": None}

				# Set the language
				snake_case__      :   Union[str, Any]      =    hasattr(model.config   ,				'''use_lang_emb'''			) and model.config.use_lang_emb
				if hasattr(model.config   ,				'''lang2id'''			) and use_lang_emb:
								snake_case__      :   List[str]      =    model.config.langaid.keys()
								if args.xlm_language in available_languages:
												snake_case__      :   Tuple      =    args.xlm_language
								else:
												snake_case__      :   int      =    None
												while language not in available_languages:
																snake_case__      :   Dict      =    input('''Using XLM. Select language in ''' + str(list(UpperCamelCase__			)			) + ''' >>> '''			)

								snake_case__      :   Tuple      =    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 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Optional[int]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				snake_case__      :   str      =    prefix + prompt_text
				return prompt_text


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]   ,				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Any   ,				UpperCamelCase__				:Dict			)		->						str:
				snake_case__      :   Dict      =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				snake_case__      :   int      =    prefix + prompt_text
				return prompt_text


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


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Union[str, Any]			)		->						str:
				if length < 0 and max_sequence_length > 0:
								snake_case__      :   int      =    max_sequence_length
				elif 0 < max_sequence_length < length:
								snake_case__      :   str      =    max_sequence_length  # No generation bigger than model size
				elif length < 0:
								snake_case__      :   Tuple      =    MAX_LENGTH  # avoid infinite loop
				return length


def 							__UpperCAmelCase				(				UpperCamelCase__				:Any			)		->						List[str]:
				snake_case__      :   Dict      =    None
				if hasattr(UpperCamelCase__   ,				'''hidden_size'''			):
								snake_case__      :   Optional[Any]      =    model_config.hidden_size
				elif hasattr(UpperCamelCase__   ,				'''n_embed'''			):
								snake_case__      :   int      =    model_config.n_embed
				elif hasattr(UpperCamelCase__   ,				'''n_embd'''			):
								snake_case__      :   str      =    model_config.n_embd

				snake_case__      :   List[str]      =    None
				if hasattr(UpperCamelCase__   ,				'''num_attention_heads'''			):
								snake_case__      :   int      =    model_config.num_attention_heads
				elif hasattr(UpperCamelCase__   ,				'''n_head'''			):
								snake_case__      :   List[Any]      =    model_config.n_head

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

				snake_case__      :   Union[str, Any]      =    int(embedding_size / num_head			)
				if hasattr(UpperCamelCase__   ,				'''n_layer'''			):
								snake_case__      :   Any      =    model_config.n_layer
				elif hasattr(UpperCamelCase__   ,				'''num_hidden_layers'''			):
								snake_case__      :   Optional[int]      =    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 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]   ,				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple			)		->						int:
				snake_case__  ,      snake_case__  ,      snake_case__      :   Dict      =    sparse_model_config(model.config			)
				if model.config.model_type == "bloom":
								snake_case__      :   Union[str, Any]      =    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:
								snake_case__      :   Tuple      =    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 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:int			)		->						Optional[Any]:
				snake_case__      :   int      =    len(UpperCamelCase__			)
				snake_case__      :   int      =    tokenizer.batch_encode_plus(UpperCamelCase__   ,				return_tensors='''pt'''			)
				snake_case__      :   Optional[Any]      =    dummy_input.to(model.device			)
				if model.config.use_cache:
								snake_case__      :   Tuple      =    generate_past_key_values(UpperCamelCase__   ,				UpperCamelCase__   ,				1			)
				snake_case__      :   Optional[int]      =    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 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ('_optimized', '_default')



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    optimized
							snake_case__      :   Optional[Any]      =    default



			def __call__(     self		:						Dict    ,			*__UpperCamelCase		:						Optional[int]    ,			**__UpperCamelCase		:						List[str]			)      ->      Optional[int]:
							"""simple docstring"""





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



			def __getattr__(     self		:						Tuple    ,			__UpperCamelCase		:						str			)      ->      Dict:
							"""simple docstring"""





							return getattr(self._default    ,			__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						str=None    ,			__UpperCamelCase		:						int=None    ,			**__UpperCamelCase		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





							return self._default.prepare_inputs_for_generation(
							    __UpperCamelCase    ,			past_key_values=__UpperCamelCase    ,			inputs_embeds=__UpperCamelCase    ,			use_cache=__UpperCamelCase    ,			**__UpperCamelCase			)







			def 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						Tuple[Tuple[torch.Tensor]]    ,			__UpperCamelCase		:						torch.Tensor			)      ->      Tuple[Tuple[torch.Tensor]]:
							"""simple docstring"""





							return self._default._reorder_cache(__UpperCamelCase    ,			__UpperCamelCase			)



def 							__UpperCAmelCase				(				)		->						str:
				snake_case__      :   Union[str, Any]      =    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=20			)
				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=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=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'''			)
				snake_case__      :   Union[str, Any]      =    parser.parse_args()

				snake_case__      :   Any      =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
				snake_case__      :   Union[str, Any]      =    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:
								snake_case__      :   List[str]      =    args.model_type.lower()
								snake_case__  ,      snake_case__      :   List[Any]      =    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 :)'''			)

				snake_case__      :   int      =    tokenizer_class.from_pretrained(args.model_name_or_path			)
				if tokenizer.pad_token is None:
								snake_case__      :   Union[str, Any]      =    tokenizer.eos_token
				snake_case__      :   str      =    model_class.from_pretrained(args.model_name_or_path			)
				model.to(args.device			)

				if args.fpaa:
								model.half()
				snake_case__      :   Any      =    getattr(model.config   ,				'''max_position_embeddings'''   ,				0			)
				snake_case__      :   List[Any]      =    adjust_length_to_model(args.length   ,				max_sequence_length=UpperCamelCase__			)
				logger.info(UpperCamelCase__			)

				snake_case__      :   Optional[Any]      =    args.prompt if args.prompt else input('''Model prompt >>> '''			)

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

								if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
												snake_case__      :   List[str]      =    {'''add_space_before_punct_symbol''': True}
								else:
												snake_case__      :   Dict      =    {}

								snake_case__      :   Dict      =    tokenizer.encode(
								    UpperCamelCase__   ,				add_special_tokens=UpperCamelCase__   ,				return_tensors='''pt'''   ,				**UpperCamelCase__			)
				else:
								snake_case__      :   Dict      =    args.prefix if args.prefix else args.padding_text
								snake_case__      :   Union[str, Any]      =    tokenizer.encode(prefix + prompt_text   ,				add_special_tokens=UpperCamelCase__   ,				return_tensors='''pt'''			)
				snake_case__      :   List[Any]      =    encoded_prompt.to(args.device			)

				if encoded_prompt.size()[-1] == 0:
								snake_case__      :   int      =    None
				else:
								snake_case__      :   List[Any]      =    encoded_prompt

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

								snake_case__      :   Optional[Any]      =    _ModelFallbackWrapper(UpperCamelCase__   ,				UpperCamelCase__			)

				snake_case__      :   Union[str, Any]      =    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_()

				snake_case__      :   List[str]      =    []

				for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__			):
								print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''			)
								snake_case__      :   List[Any]      =    generated_sequence.tolist()

								# Decode text
								snake_case__      :   Tuple      =    tokenizer.decode(UpperCamelCase__   ,				clean_up_tokenization_spaces=UpperCamelCase__			)

								# Remove all text after the stop token
								snake_case__      :   Optional[Any]      =    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
								snake_case__      :   str      =    (
								    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()









'''simple docstring'''



import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowercase       :						int						=[nn.LayerNorm]

_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						List[Any]						=version.parse(version.parse(torch.__version__).base_version)

_lowercase       :						int						=parsed_torch_version_base >= version.parse("2.0")
_lowercase       :						Tuple						=parsed_torch_version_base >= version.parse("1.12")
_lowercase       :						int						=parsed_torch_version_base >= version.parse("1.11")
_lowercase       :						Dict						=parsed_torch_version_base < version.parse("1.11")


def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:Any   ,				self				:Optional[int]			)		->						Union[str, Any]:
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(UpperCamelCase__   ,				UpperCamelCase__   ,				parent.dim   ,				self			)
				else:
								return _softmax_backward_data(UpperCamelCase__   ,				UpperCamelCase__   ,				parent.dim   ,				self.dtype			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:nn.Linear   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:int = 0			)		->						nn.Linear:
				snake_case__      :   Optional[Any]      =    index.to(layer.weight.device			)
				snake_case__      :   int      =    layer.weight.index_select(UpperCamelCase__   ,				UpperCamelCase__			).clone().detach()
				if layer.bias is not None:
								if dim == 1:
												snake_case__      :   Dict      =    layer.bias.clone().detach()
								else:
												snake_case__      :   Optional[Any]      =    layer.bias[index].clone().detach()
				snake_case__      :   Union[str, Any]      =    list(layer.weight.size()			)
				snake_case__      :   int      =    len(UpperCamelCase__			)
				snake_case__      :   int      =    nn.Linear(new_size[1]   ,				new_size[0]   ,				bias=layer.bias is not None			).to(layer.weight.device			)
				snake_case__      :   Union[str, Any]      =    False
				new_layer.weight.copy_(W.contiguous()			)
				snake_case__      :   List[Any]      =    True
				if layer.bias is not None:
								snake_case__      :   Union[str, Any]      =    False
								new_layer.bias.copy_(b.contiguous()			)
								snake_case__      :   Dict      =    True
				return new_layer






class 			_SCREAMING_SNAKE_CASE				(nn.Module      ):



			def __init__(     self		:						int    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Optional[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__()
							snake_case__      :   str      =    nf
							snake_case__      :   Tuple      =    nn.Parameter(torch.empty(__UpperCamelCase    ,			__UpperCamelCase			)			)
							snake_case__      :   Optional[Any]      =    nn.Parameter(torch.zeros(__UpperCamelCase			)			)
							nn.init.normal_(self.weight    ,			std=0.02			)







			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Optional[int]			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Dict      =    x.size()[:-1] + (self.nf,)
							snake_case__      :   List[str]      =    torch.addmm(self.bias    ,			x.view(-1    ,			x.size(-1			)			)    ,			self.weight			)
							snake_case__      :   Dict      =    x.view(__UpperCamelCase			)
							return x



def 							__UpperCAmelCase				(				UpperCamelCase__				:ConvaD   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:int = 1			)		->						ConvaD:
				snake_case__      :   Optional[Any]      =    index.to(layer.weight.device			)
				snake_case__      :   List[Any]      =    layer.weight.index_select(UpperCamelCase__   ,				UpperCamelCase__			).clone().detach()
				if dim == 0:
								snake_case__      :   Any      =    layer.bias.clone().detach()
				else:
								snake_case__      :   Optional[Any]      =    layer.bias[index].clone().detach()
				snake_case__      :   Union[str, Any]      =    list(layer.weight.size()			)
				snake_case__      :   List[str]      =    len(UpperCamelCase__			)
				snake_case__      :   Optional[Any]      =    ConvaD(new_size[1]   ,				new_size[0]			).to(layer.weight.device			)
				snake_case__      :   List[str]      =    False
				new_layer.weight.copy_(W.contiguous()			)
				snake_case__      :   List[Any]      =    True
				snake_case__      :   List[str]      =    False
				new_layer.bias.copy_(b.contiguous()			)
				snake_case__      :   str      =    True
				return new_layer


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[nn.Linear, ConvaD]   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:Optional[int] = None			)		->						Union[nn.Linear, ConvaD]:
				if isinstance(UpperCamelCase__   ,				nn.Linear			):
								return prune_linear_layer(UpperCamelCase__   ,				UpperCamelCase__   ,				dim=0 if dim is None else dim			)
				elif isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								return prune_convad_layer(UpperCamelCase__   ,				UpperCamelCase__   ,				dim=1 if dim is None else dim			)
				else:
								raise ValueError(F'''Can\'t prune layer of class {layer.__class__}'''			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[..., torch.Tensor]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:int   ,				*UpperCamelCase__				:List[str]			)		->						torch.Tensor:
				assert len(UpperCamelCase__			) > 0, F'''{input_tensors} has to be a tuple/list of tensors'''

				# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
				snake_case__      :   str      =    len(inspect.signature(UpperCamelCase__			).parameters			)
				if num_args_in_forward_chunk_fn != len(UpperCamelCase__			):
								raise ValueError(
								    F'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase__			)} input '''
								    '''tensors are given'''			)

				if chunk_size > 0:
								snake_case__      :   Tuple      =    input_tensors[0].shape[chunk_dim]
								for input_tensor in input_tensors:
												if input_tensor.shape[chunk_dim] != tensor_shape:
																raise ValueError(
																    F'''All input tenors have to be of the same shape: {tensor_shape}, '''
																    F'''found shape {input_tensor.shape[chunk_dim]}'''			)

								if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
												raise ValueError(
												    F'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
												    F'''size {chunk_size}'''			)

								snake_case__      :   Optional[Any]      =    input_tensors[0].shape[chunk_dim] // chunk_size

								# chunk input tensor into tuples
								snake_case__      :   List[Any]      =    tuple(input_tensor.chunk(UpperCamelCase__   ,				dim=UpperCamelCase__			) for input_tensor in input_tensors			)
								# apply forward fn to every tuple
								snake_case__      :   List[Any]      =    tuple(forward_fn(*UpperCamelCase__			) for input_tensors_chunk in zip(*UpperCamelCase__			)			)
								# concatenate output at same dimension
								return torch.cat(UpperCamelCase__   ,				dim=UpperCamelCase__			)

				return forward_fn(*UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[int]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Set[int]			)		->						Tuple[Set[int], torch.LongTensor]:
				snake_case__      :   str      =    torch.ones(UpperCamelCase__   ,				UpperCamelCase__			)
				snake_case__      :   int      =    set(UpperCamelCase__			) - already_pruned_heads  # Convert to set and remove already pruned heads
				for head in heads:
								# Compute how many pruned heads are before the head and move the index accordingly
								snake_case__      :   str      =    head - sum(1 if h < head else 0 for h in already_pruned_heads			)
								snake_case__      :   Optional[int]      =    0
				snake_case__      :   Optional[Any]      =    mask.view(-1			).contiguous().eq(1			)
				snake_case__      :   torch.LongTensor      =    torch.arange(len(UpperCamelCase__			)			)[mask].long()
				return heads, index


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Union[torch.Tensor, List[torch.Tensor]]   ,				UpperCamelCase__				:Optional[str] = None			)		->						Tuple[torch.Tensor, ...]:
				return torch.meshgrid(*UpperCamelCase__   ,				indexing=UpperCamelCase__			)



def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor			)		->						Tuple[torch.device, int, int]:
				return tensor.device, storage_ptr(UpperCamelCase__			), storage_size(UpperCamelCase__			)




