import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
		import transformers






def        lowerCamelCase_					(     UpperCamelCase__       :						Optional[int]			):







				'''simple docstring'''







				random.seed(__a			)
				np.random.seed(__a			)
				torch.manual_seed(__a			)
				torch.cuda.manual_seed_all(__a			)
				# ^^ safe to call this function even if cuda is not available







class 	__lowercase    :




				'''simple docstring'''


				def __init__(		self    :		Union[str, Any]      ,							_a    :		Iterable[torch.nn.Parameter]      ,							_a    :		float = 0.9999      ,							_a    :		float = 0.0      ,							_a    :		int = 0      ,							_a    :		bool = False      ,							_a    :		Union[float, int] = 1.0      ,							_a    :		Union[float, int] = 2 / 3      ,							_a    :		Optional[Any] = None      ,							_a    :		Dict[str, Any] = None      ,							**_a    :		str      ,							):

								if isinstance(_snake_case      ,							torch.nn.Module				):
												UpperCamelCase__					=				(
												    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
												    '''Please pass the parameters of the module instead.'''
												)
												deprecate(
												    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''      ,							'''1.0.0'''      ,							_snake_case      ,							standard_warn=_snake_case      ,							)
												UpperCamelCase__					=				parameters.parameters()

												# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
												UpperCamelCase__					=				True

								if kwargs.get('''max_value'''      ,							_snake_case				) is not None:
												UpperCamelCase__					=				'''The `max_value` argument is deprecated. Please use `decay` instead.'''
												deprecate('''max_value'''      ,							'''1.0.0'''      ,							_snake_case      ,							standard_warn=_snake_case				)
												UpperCamelCase__					=				kwargs['''max_value''']

								if kwargs.get('''min_value'''      ,							_snake_case				) is not None:
												UpperCamelCase__					=				'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
												deprecate('''min_value'''      ,							'''1.0.0'''      ,							_snake_case      ,							standard_warn=_snake_case				)
												UpperCamelCase__					=				kwargs['''min_value''']

								UpperCamelCase__					=				list(_snake_case				)
								UpperCamelCase__					=				[p.clone().detach() for p in parameters]

								if kwargs.get('''device'''      ,							_snake_case				) is not None:
												UpperCamelCase__					=				'''The `device` argument is deprecated. Please use `to` instead.'''
												deprecate('''device'''      ,							'''1.0.0'''      ,							_snake_case      ,							standard_warn=_snake_case				)
												self.to(device=kwargs['''device''']				)

								UpperCamelCase__					=				None

								UpperCamelCase__					=				decay
								UpperCamelCase__					=				min_decay
								UpperCamelCase__					=				update_after_step
								UpperCamelCase__					=				use_ema_warmup
								UpperCamelCase__					=				inv_gamma
								UpperCamelCase__					=				power
								UpperCamelCase__					=				0
								UpperCamelCase__					=				None  # set in `step()`

								UpperCamelCase__					=				model_cls
								UpperCamelCase__					=				model_config





				@classmethod
				def      A_							(		cls    :		Union[str, Any]      ,							_a    :		Dict      ,							_a    :		str				):
								UpperCamelCase__		, UpperCamelCase__					=				model_cls.load_config(_snake_case      ,							return_unused_kwargs=_snake_case				)
								UpperCamelCase__					=				model_cls.from_pretrained(_snake_case				)

								UpperCamelCase__					=				cls(model.parameters()      ,							model_cls=_snake_case      ,							model_config=model.config				)

								ema_model.load_state_dict(_snake_case				)
								return ema_model





				def      A_							(		self    :		Dict      ,							_a    :		Tuple				):
								if self.model_cls is None:
												raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''				)

								if self.model_config is None:
												raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''				)

								UpperCamelCase__					=				self.model_cls.from_config(self.model_config				)
								UpperCamelCase__					=				self.state_dict()
								state_dict.pop('''shadow_params'''      ,							_snake_case				)

								model.register_to_config(**_snake_case				)
								self.copy_to(model.parameters()				)
								model.save_pretrained(_snake_case				)





				def      A_							(		self    :		List[str]      ,							_a    :		int				):
								UpperCamelCase__					=				max(0      ,							optimization_step - self.update_after_step - 1				)

								if step <= 0:
												return 0.0

								if self.use_ema_warmup:
												UpperCamelCase__					=				1 - (1 + step / self.inv_gamma) ** -self.power
								else:
												UpperCamelCase__					=				(1 + step) / (10 + step)

								UpperCamelCase__					=				min(_snake_case      ,							self.decay				)
								# make sure decay is not smaller than min_decay
								UpperCamelCase__					=				max(_snake_case      ,							self.min_decay				)
								return cur_decay_value





				@torch.no_grad()
				def      A_							(		self    :		Optional[Any]      ,							_a    :		Iterable[torch.nn.Parameter]				):
								if isinstance(_snake_case      ,							torch.nn.Module				):
												UpperCamelCase__					=				(
												    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
												    '''Please pass the parameters of the module instead.'''
												)
												deprecate(
												    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''      ,							'''1.0.0'''      ,							_snake_case      ,							standard_warn=_snake_case      ,							)
												UpperCamelCase__					=				parameters.parameters()

								UpperCamelCase__					=				list(_snake_case				)

								self.optimization_step += 1

								# Compute the decay factor for the exponential moving average.
								UpperCamelCase__					=				self.get_decay(self.optimization_step				)
								UpperCamelCase__					=				decay
								UpperCamelCase__					=				1 - decay

								UpperCamelCase__					=				contextlib.nullcontext
								if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
												import deepspeed

								for s_param, param in zip(self.shadow_params      ,							_snake_case				):
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																UpperCamelCase__					=				deepspeed.zero.GatheredParameters(_snake_case      ,							modifier_rank=_snake_case				)

												with context_manager():
																if param.requires_grad:
																				s_param.sub_(one_minus_decay * (s_param - param)				)
																else:
																				s_param.copy_(_snake_case				)





				def      A_							(		self    :		List[Any]      ,							_a    :		Iterable[torch.nn.Parameter]				):
								UpperCamelCase__					=				list(_snake_case				)
								for s_param, param in zip(self.shadow_params      ,							_snake_case				):
												param.data.copy_(s_param.to(param.device				).data				)





				def      A_							(		self    :		List[Any]      ,							_a    :		int=None      ,							_a    :		str=None				):
								UpperCamelCase__					=				[
								    p.to(device=_snake_case      ,							dtype=_snake_case				) if p.is_floating_point() else p.to(device=_snake_case				)
								    for p in self.shadow_params
								]





				def      A_							(		self    :		Dict				):
								return {
								    "decay": self.decay,
								    "min_decay": self.min_decay,
								    "optimization_step": self.optimization_step,
								    "update_after_step": self.update_after_step,
								    "use_ema_warmup": self.use_ema_warmup,
								    "inv_gamma": self.inv_gamma,
								    "power": self.power,
								    "shadow_params": self.shadow_params,
								}





				def      A_							(		self    :		Union[str, Any]      ,							_a    :		Iterable[torch.nn.Parameter]				):
								UpperCamelCase__					=				[param.detach().cpu().clone() for param in parameters]





				def      A_							(		self    :		List[Any]      ,							_a    :		Iterable[torch.nn.Parameter]				):
								if self.temp_stored_params is None:
												raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''				)
								for c_param, param in zip(self.temp_stored_params      ,							_snake_case				):
												param.data.copy_(c_param.data				)

								# Better memory-wise.
								UpperCamelCase__					=				None





				def      A_							(		self    :		str      ,							_a    :		dict				):
								UpperCamelCase__					=				copy.deepcopy(_snake_case				)

								UpperCamelCase__					=				state_dict.get('''decay'''      ,							self.decay				)
								if self.decay < 0.0 or self.decay > 1.0:
												raise ValueError('''Decay must be between 0 and 1'''				)

								UpperCamelCase__					=				state_dict.get('''min_decay'''      ,							self.min_decay				)
								if not isinstance(self.min_decay      ,							_snake_case				):
												raise ValueError('''Invalid min_decay'''				)

								UpperCamelCase__					=				state_dict.get('''optimization_step'''      ,							self.optimization_step				)
								if not isinstance(self.optimization_step      ,							_snake_case				):
												raise ValueError('''Invalid optimization_step'''				)

								UpperCamelCase__					=				state_dict.get('''update_after_step'''      ,							self.update_after_step				)
								if not isinstance(self.update_after_step      ,							_snake_case				):
												raise ValueError('''Invalid update_after_step'''				)

								UpperCamelCase__					=				state_dict.get('''use_ema_warmup'''      ,							self.use_ema_warmup				)
								if not isinstance(self.use_ema_warmup      ,							_snake_case				):
												raise ValueError('''Invalid use_ema_warmup'''				)

								UpperCamelCase__					=				state_dict.get('''inv_gamma'''      ,							self.inv_gamma				)
								if not isinstance(self.inv_gamma      ,							(float, int)				):
												raise ValueError('''Invalid inv_gamma'''				)

								UpperCamelCase__					=				state_dict.get('''power'''      ,							self.power				)
								if not isinstance(self.power      ,							(float, int)				):
												raise ValueError('''Invalid power'''				)

								UpperCamelCase__					=				state_dict.get('''shadow_params'''      ,							_snake_case				)
								if shadow_params is not None:
												UpperCamelCase__					=				shadow_params
												if not isinstance(self.shadow_params      ,							_snake_case				):
																raise ValueError('''shadow_params must be a list'''				)
												if not all(isinstance(_snake_case      ,							torch.Tensor				) for p in self.shadow_params				):
																raise ValueError('''shadow_params must all be Tensors'''				)





"""simple docstring"""





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				A_  (__a					, __a					, __a					, __a					, __a					):

					'''simple docstring'''





					with open(__a					) as metadata_file:
										A_								=    json.load(__a					)
					A_								=    LukeConfig(use_entity_aware_attention=__a					, **metadata["model_config"]					)

					# Load in the weights from the checkpoint_path
					A_								=    torch.load(__a					, map_location="cpu"					)

					# Load the entity vocab file
					A_								=    load_entity_vocab(__a					)

					A_								=    RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]					)

					# Add special tokens to the token vocabulary for downstream tasks
					A_								=    AddedToken("<ent>"					, lstrip=__a					, rstrip=__a					)
					A_								=    AddedToken("<ent2>"					, lstrip=__a					, rstrip=__a					)
					tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}					)
					config.vocab_size += 2

					print(f'Saving tokenizer to {pytorch_dump_folder_path}'					)
					tokenizer.save_pretrained(__a					)
					with open(os.path.join(__a					, LukeTokenizer.vocab_files_names["entity_vocab_file"]					)					, "w"					) as f:
										json.dump(__a					, __a					)

					A_								=    LukeTokenizer.from_pretrained(__a					)

					# Initialize the embeddings of the special tokens
					A_								=    state_dict["embeddings.word_embeddings.weight"]
					A_								=    word_emb[tokenizer.convert_tokens_to_ids(["@"]					)[0]].unsqueeze(0					)
					A_								=    word_emb[tokenizer.convert_tokens_to_ids(["#"]					)[0]].unsqueeze(0					)
					A_								=    torch.cat([word_emb, ent_emb, enta_emb]					)

					# Initialize the query layers of the entity-aware self-attention mechanism
					for layer_index in range(config.num_hidden_layers					):
										for matrix_name in ["query.weight", "query.bias"]:
															A_								=    f'encoder.layer.{layer_index}.attention.self.'
															A_								=    state_dict[prefix + matrix_name]
															A_								=    state_dict[prefix + matrix_name]
															A_								=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					A_								=    state_dict["entity_embeddings.entity_embeddings.weight"]
					A_								=    entity_emb[entity_vocab["[MASK]"]]

					A_								=    LukeModel(config=__a					).eval()

					A_			,     A_								=    model.load_state_dict(__a					, strict=__a					)
					if not (len(__a					) == 1 and missing_keys[0] == "embeddings.position_ids"):
										raise ValueError(f'Missing keys {", ".join(__a					)}. Expected only missing embeddings.position_ids'					)
					if not (all(key.startswith("entity_predictions"					) or key.startswith("lm_head"					) for key in unexpected_keys					)):
										raise ValueError(
										    "Unexpected keys"
										    f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"					) or key.startswith("lm_head"					))]					)}'					)

					# Check outputs
					A_								=    LukeTokenizer.from_pretrained(__a					, task="entity_classification"					)

					A_								=    (
					    "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
					    " new world number one avoid a humiliating second- round exit at Wimbledon ."
					)
					A_								=    (39, 42)
					A_								=    tokenizer(__a					, entity_spans=[span]					, add_prefix_space=__a					, return_tensors="pt"					)

					A_								=    model(**__a					)

					# Verify word hidden states
					if model_size == "large":
										A_								=    torch.Size((1, 42, 1024)					)
										A_								=    torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]					)
					else:  # base
										A_								=    torch.Size((1, 42, 768)					)
										A_								=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]					)

					if not (outputs.last_hidden_state.shape == expected_shape):
										raise ValueError(
										    f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'					)
					if not torch.allclose(outputs.last_hidden_state[0, :3, :3]					, __a					, atol=1e-4					):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										A_								=    torch.Size((1, 1, 1024)					)
										A_								=    torch.tensor([[0.0466, -0.0106, -0.0179]]					)
					else:  # base
										A_								=    torch.Size((1, 1, 768)					)
										A_								=    torch.tensor([[0.1457, 0.1044, 0.0174]]					)

					if not (outputs.entity_last_hidden_state.shape != expected_shape):
										raise ValueError(
										    f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
										    f' {expected_shape}'					)
					if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]					, __a					, atol=1e-4					):
										raise ValueError

					# Finally, save our PyTorch model and tokenizer
					print("Saving PyTorch model to {}".format(__a					)					)
					model.save_pretrained(__a					)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    {}
					with open(__a					, "r"					, encoding="utf-8"					) as f:
										for index, line in enumerate(__a					):
															A_			,     A_								=    line.rstrip().split("\t"					)
															A_								=    index

					return entity_vocab


if __name__ == "__main__":
		UpperCamelCase_       :    Dict						      =			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
		parser.add_argument(
		    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
		)
		parser.add_argument(
		    '''--entity_vocab_path''',
		    default=None,
		    type=str,
		    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
		)
		UpperCamelCase_       :    List[str]						      =			parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)




