


"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE  :						int    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       Union[str, Any]										= """perceiver"""

					def __init__(    self				,       lowerCamelCase__=2_5_6				,       lowerCamelCase__=1_2_8_0				,       lowerCamelCase__=7_6_8				,       lowerCamelCase__=1				,       lowerCamelCase__=2_6				,       lowerCamelCase__=8				,       lowerCamelCase__=8				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__="kv"				,       lowerCamelCase__=1				,       lowerCamelCase__=1				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=1e-12				,       lowerCamelCase__=True				,       lowerCamelCase__=2_6_2				,       lowerCamelCase__=2_0_4_8				,       lowerCamelCase__=5_6				,       lowerCamelCase__=[3_6_8, 4_9_6]				,       lowerCamelCase__=1_6				,       lowerCamelCase__=1_9_2_0				,       lowerCamelCase__=1_6				,       lowerCamelCase__=[1, 1_6, 2_2_4, 2_2_4]				,       **lowerCamelCase__				,       ):
									super().__init__(**lowerCamelCase__   )

									_lowerCamelCase        =				num_latents
									_lowerCamelCase        =				d_latents
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				num_blocks
									_lowerCamelCase        =				num_self_attends_per_block
									_lowerCamelCase        =				num_self_attention_heads
									_lowerCamelCase        =				num_cross_attention_heads
									_lowerCamelCase        =				qk_channels
									_lowerCamelCase        =				v_channels
									_lowerCamelCase        =				cross_attention_shape_for_attention
									_lowerCamelCase        =				self_attention_widening_factor
									_lowerCamelCase        =				cross_attention_widening_factor
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				layer_norm_eps
									_lowerCamelCase        =				use_query_residual
									# masked language modeling attributes
									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				max_position_embeddings
									# image classification attributes
									_lowerCamelCase        =				image_size
									# flow attributes
									_lowerCamelCase        =				train_size
									# multimodal autoencoding attributes
									_lowerCamelCase        =				num_frames
									_lowerCamelCase        =				audio_samples_per_frame
									_lowerCamelCase        =				samples_per_patch
									_lowerCamelCase        =				output_shape


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					@property
					def 			snake_case__      (    self   ):

									if self.task == "multiple-choice":
													_lowerCamelCase        =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
													_lowerCamelCase        =				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''inputs''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]   )

					@property
					def 			snake_case__      (    self   ):
									return 1e-4





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = -1				,       lowerCamelCase__ = -1				,       lowerCamelCase__ = -1				,       lowerCamelCase__ = False				,       lowerCamelCase__ = None				,       lowerCamelCase__ = 3				,       lowerCamelCase__ = 4_0				,       lowerCamelCase__ = 4_0				,       ):

									if isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													_lowerCamelCase        =				compute_effective_axis_dimension(
													    lowerCamelCase__				,       fixed_dimension=OnnxConfig.default_fixed_batch				,       num_token_to_add=0   )
													# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
													_lowerCamelCase        =				preprocessor.num_special_tokens_to_add(lowerCamelCase__   )
													_lowerCamelCase        =				compute_effective_axis_dimension(
													    lowerCamelCase__				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=lowerCamelCase__   )
													# Generate dummy inputs according to compute batch and sequence
													_lowerCamelCase        =				[''' '''.join(['''a''']   ) * seq_length] * batch_size
													_lowerCamelCase        =				dict(preprocessor(lowerCamelCase__				,       return_tensors=lowerCamelCase__   )   )
													_lowerCamelCase        =				inputs.pop('''input_ids'''   )
													return inputs
									elif isinstance(lowerCamelCase__				,       lowerCamelCase__   ) and preprocessor.model_input_names[0] == "pixel_values":
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													_lowerCamelCase        =				compute_effective_axis_dimension(lowerCamelCase__				,       fixed_dimension=OnnxConfig.default_fixed_batch   )
													_lowerCamelCase        =				self._generate_dummy_images(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
													_lowerCamelCase        =				dict(preprocessor(images=lowerCamelCase__				,       return_tensors=lowerCamelCase__   )   )
													_lowerCamelCase        =				inputs.pop('''pixel_values'''   )
													return inputs
									else:
													raise ValueError(
													    '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''   )





"""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        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Tuple						,   lowercase_     :							List[str]							)	->       Dict:
				# Load configuration defined in the metadata file
				with open(lowercase_							) as metadata_file:
								_lowerCamelCase        =				json.load(lowercase_							)
				_lowerCamelCase        =				LukeConfig(use_entity_aware_attention=lowercase_						,   **metadata['''model_config''']							)

				# Load in the weights from the checkpoint_path
				_lowerCamelCase        =				torch.load(lowercase_						,   map_location='''cpu'''							)

				# Load the entity vocab file
				_lowerCamelCase        =				load_entity_vocab(lowercase_							)

				_lowerCamelCase        =				RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

				# Add special tokens to the token vocabulary for downstream tasks
				_lowerCamelCase        =				AddedToken('''<ent>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				_lowerCamelCase        =				AddedToken('''<ent2>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				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(lowercase_							)
				with open(os.path.join(lowercase_						,   LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)						,   '''w'''							) as f:
								json.dump(lowercase_						,   lowercase_							)

				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_							)

				# Initialize the embeddings of the special tokens
				_lowerCamelCase        =				state_dict['''embeddings.word_embeddings.weight''']
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				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"]:
												_lowerCamelCase        =				F"""encoder.layer.{layer_index}.attention.self."""
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]

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

				_lowerCamelCase        =				LukeModel(config=lowercase_							).eval()

				_lowerCamelCase  ,     _lowerCamelCase        =				model.load_state_dict(lowercase_						,   strict=lowercase_							)
				if not (len(lowercase_							) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"""Missing keys {", ".join(lowercase_							)}. 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
				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_						,   task='''entity_classification'''							)

				_lowerCamelCase        =				(
				    '''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 .'''
				)
				_lowerCamelCase        =				(39, 42)
				_lowerCamelCase        =				tokenizer(lowercase_						,   entity_spans=[span]						,   add_prefix_space=lowercase_						,   return_tensors='''pt'''							)

				_lowerCamelCase        =				model(**lowercase_							)

				# Verify word hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 42, 10_24)							)
								_lowerCamelCase        =				torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 42, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]							)

				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]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 1, 10_24)							)
								_lowerCamelCase        =				torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 1, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]							)

				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]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Finally, save our PyTorch model and tokenizer
				print('''Saving PyTorch model to {}'''.format(lowercase_							)							)
				model.save_pretrained(lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       Any:
				_lowerCamelCase        =				{}
				with open(lowercase_						,   '''r'''						,   encoding='''utf-8'''							) as f:
								for index, line in enumerate(lowercase_							):
												_lowerCamelCase  ,     _lowerCamelCase        =				line.rstrip().split('''\t'''							)
												_lowerCamelCase        =				index

				return entity_vocab


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      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.'''
			)
			__SCREAMING_SNAKE_CASE  :						int    							=      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)
