

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(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# Load configuration defined in the metadata file
	with open(lowercase__						) as metadata_file:
		snake_case_   		=			json.load(lowercase__						)
	snake_case_   		=			LukeConfig(use_entity_aware_attention=lowercase__      ,			**metadata['model_config']						)

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

	# Load the entity vocab file
	snake_case_   		=			load_entity_vocab(lowercase__						)

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

	# Add special tokens to the token vocabulary for downstream tasks
	snake_case_   		=			AddedToken('<ent>'      ,			lstrip=lowercase__      ,			rstrip=lowercase__						)
	snake_case_   		=			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__						)

	snake_case_   		=			LukeTokenizer.from_pretrained(lowercase__						)

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

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

	snake_case_   		=			LukeModel(config=lowercase__						).eval()

	snake_case_		,							snake_case_   		=			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
	snake_case_   		=			LukeTokenizer.from_pretrained(lowercase__      ,			task='entity_classification'						)

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

	snake_case_   		=			model(**lowercase__						)

	# Verify word hidden states
	if model_size == "large":
		snake_case_   		=			torch.Size((1, 42, 1024)						)
		snake_case_   		=			torch.tensor(
		    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]						)
	else:  # base
		snake_case_   		=			torch.Size((1, 42, 768)						)
		snake_case_   		=			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]      ,			lowercase__      ,			atol=1e-4						):
		raise ValueError

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





	'''simple docstring'''
	snake_case_   		=			{}
	with open(lowercase__      ,			'r'      ,			encoding='utf-8'						) as f:
		for index, line in enumerate(lowercase__						):
			snake_case_		,							snake_case_   		=			line.rstrip().split('\t'						)
			snake_case_   		=			index

	return entity_vocab


if __name__ == "__main__":
		A									=  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.'
		)
		A									=  parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)



import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ASTForAudioClassification, ASTModel
		from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


if is_torchaudio_available():
		import torchaudio

		from transformers import ASTFeatureExtractor


class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=13			,					__UpperCamelCase=2			,					__UpperCamelCase=24			,					__UpperCamelCase=16			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase=32			,					__UpperCamelCase=5			,					__UpperCamelCase=4			,					__UpperCamelCase=37			,					__UpperCamelCase="gelu"			,					__UpperCamelCase=0.1			,					__UpperCamelCase=0.1			,					__UpperCamelCase=10			,					__UpperCamelCase=0.02			,					__UpperCamelCase=None			,					__UpperCamelCase=2			,					__UpperCamelCase=2			,					):

				"""simple docstring"""


				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			patch_size
				snake_case_   		=			max_length
				snake_case_   		=			num_mel_bins
				snake_case_   		=			is_training
				snake_case_   		=			use_labels
				snake_case_   		=			hidden_size
				snake_case_   		=			num_hidden_layers
				snake_case_   		=			num_attention_heads
				snake_case_   		=			intermediate_size
				snake_case_   		=			hidden_act
				snake_case_   		=			hidden_dropout_prob
				snake_case_   		=			attention_probs_dropout_prob
				snake_case_   		=			type_sequence_label_size
				snake_case_   		=			initializer_range
				snake_case_   		=			scope
				snake_case_   		=			frequency_stride
				snake_case_   		=			time_stride

				# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
				snake_case_   		=			(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
				snake_case_   		=			(self.max_length - self.patch_size) // self.time_stride + 1
				snake_case_   		=			frequency_out_dimension * time_out_dimension
				snake_case_   		=			num_patches + 2


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]				)

				snake_case_   		=			None
				if self.use_labels:
					snake_case_   		=			ids_tensor([self.batch_size]			,					self.type_sequence_label_size				)

				snake_case_   		=			self.get_config()

				return config, input_values, labels


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return ASTConfig(
				    patch_size=self.patch_size			,					max_length=self.max_length			,					num_mel_bins=self.num_mel_bins			,					hidden_size=self.hidden_size			,					num_hidden_layers=self.num_hidden_layers			,					num_attention_heads=self.num_attention_heads			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					is_decoder=__UpperCamelCase			,					initializer_range=self.initializer_range			,					frequency_stride=self.frequency_stride			,					time_stride=self.time_stride			,					)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			ASTModel(config=__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase				)
				self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size)				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.prepare_config_and_inputs()
				(
				    (
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							
				)   		=			config_and_inputs
				snake_case_   		=			{'input_values': input_values}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	(
			    (
			        ASTModel,
			        ASTForAudioClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__A    	=	(
			    {"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
			    if is_torch_available()
			    else {}
			)
			__A    	=	False
			__A    	=	False
			__A    	=	False
			__A    	=	False


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				if pipeline_test_casse_name == "AudioClassificationPipelineTests":
					return True

				return False


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			ASTModelTester(self				)
				snake_case_   		=			ConfigTester(self			,					config_class=__UpperCamelCase			,					has_text_modality=__UpperCamelCase			,					hidden_size=37				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			@unittest.skip(reason='AST does not use inputs_embeds'				)
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_   		=			model_class(__UpperCamelCase				)
					self.assertIsInstance(model.get_input_embeddings()			,					(nn.Module)				)
					snake_case_   		=			model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__UpperCamelCase			,					nn.Linear				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_   		=			model_class(__UpperCamelCase				)
					snake_case_   		=			inspect.signature(model.forward				)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					snake_case_   		=			[*signature.parameters.keys()]

					snake_case_   		=			['input_values']
					self.assertListEqual(arg_names[:1]			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_   		=			ASTModel.from_pretrained(__UpperCamelCase				)
					self.assertIsNotNone(__UpperCamelCase				)







def a():





	'''simple docstring'''
	snake_case_   		=			hf_hub_download(
	    repo_id='nielsr/audio-spectogram-transformer-checkpoint'      ,			filename='sample_audio.flac'      ,			repo_type='dataset'						)

	snake_case_		,							snake_case_   		=			torchaudio.load(lowercase__						)

	return audio, sampling_rate






@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			@cached_property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return (
				    ASTFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'				)
				    if is_torchaudio_available()
				    else None
				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.default_feature_extractor
				snake_case_   		=			ASTForAudioClassification.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'				).to(__UpperCamelCase				)

				snake_case_   		=			self.default_feature_extractor
				snake_case_		,							snake_case_   		=			prepare_audio()
				snake_case_   		=			audio.squeeze().numpy()
				snake_case_   		=			feature_extractor(__UpperCamelCase			,					sampling_rate=__UpperCamelCase			,					return_tensors='pt'				).to(__UpperCamelCase				)

				# forward pass
				with torch.no_grad():
					snake_case_   		=			model(**__UpperCamelCase				)

				# verify the logits
				snake_case_   		=			torch.Size((1, 5_27)				)
				self.assertEqual(outputs.logits.shape			,					__UpperCamelCase				)

				snake_case_   		=			torch.tensor([-0.8760, -7.0042, -8.6602]				).to(__UpperCamelCase				)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					__UpperCamelCase			,					atol=1E-4				)				)
