
"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowerCamelCase	:    Optional[Any]          =      False




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				return 12



	@property
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				return 12



	@property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return 32



	@property
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      VQModel(
				    block_out_channels=[32, 64]      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,  latent_channels=3      ,  num_vq_embeddings=self.num_embed      ,  vq_embed_dim=3      ,  )
				return model



	@property
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
				return tokenizer



	@property
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      CLIPTextConfig(
				    bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=self.text_embedder_hidden_size      ,  intermediate_size=37      ,  layer_norm_eps=1E-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1_000      ,  )
				return CLIPTextModel(lowercase     )



	@property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				torch.manual_seed(0     )

				_snake_case          =      12
				_snake_case          =      12

				_snake_case          =      {
				    'attention_bias': True,
				    'cross_attention_dim': 32,
				    'attention_head_dim': height * width,
				    'num_attention_heads': 1,
				    'num_vector_embeds': self.num_embed,
				    'num_embeds_ada_norm': self.num_embeds_ada_norm,
				    'norm_num_groups': 32,
				    'sample_size': width,
				    'activation_fn': 'geglu-approximate',
				}

				_snake_case          =      TransformeraDModel(**lowercase     )
				return model



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      'cpu'

				_snake_case          =      self.dummy_vqvae
				_snake_case          =      self.dummy_text_encoder
				_snake_case          =      self.dummy_tokenizer
				_snake_case          =      self.dummy_transformer
				_snake_case          =      VQDiffusionScheduler(self.num_embed     )
				_snake_case          =      LearnedClassifierFreeSamplingEmbeddings(learnable=lowercase     )

				_snake_case          =      VQDiffusionPipeline(
				    vqvae=lowercase      ,  text_encoder=lowercase      ,  tokenizer=lowercase      ,  transformer=lowercase      ,  scheduler=lowercase      ,  learned_classifier_free_sampling_embeddings=lowercase      ,  )
				_snake_case          =      pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      'teddy bear playing in the pool'

				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      pipe([prompt]      ,  generator=lowercase      ,  num_inference_steps=2      ,  output_type='np'     )
				_snake_case          =      output.images

				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      pipe(
				    [prompt]      ,  generator=lowercase      ,  output_type='np'      ,  return_dict=lowercase      ,  num_inference_steps=2     )[0]

				_snake_case          =      image[0, -3:, -3:, -1]
				_snake_case          =      image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				_snake_case          =      np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2





	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      'cpu'

				_snake_case          =      self.dummy_vqvae
				_snake_case          =      self.dummy_text_encoder
				_snake_case          =      self.dummy_tokenizer
				_snake_case          =      self.dummy_transformer
				_snake_case          =      VQDiffusionScheduler(self.num_embed     )
				_snake_case          =      LearnedClassifierFreeSamplingEmbeddings(
				    learnable=lowercase      ,  hidden_size=self.text_embedder_hidden_size      ,  length=tokenizer.model_max_length     )

				_snake_case          =      VQDiffusionPipeline(
				    vqvae=lowercase      ,  text_encoder=lowercase      ,  tokenizer=lowercase      ,  transformer=lowercase      ,  scheduler=lowercase      ,  learned_classifier_free_sampling_embeddings=lowercase      ,  )
				_snake_case          =      pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      'teddy bear playing in the pool'

				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      pipe([prompt]      ,  generator=lowercase      ,  num_inference_steps=2      ,  output_type='np'     )
				_snake_case          =      output.images

				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      pipe(
				    [prompt]      ,  generator=lowercase      ,  output_type='np'      ,  return_dict=lowercase      ,  num_inference_steps=2     )[0]

				_snake_case          =      image[0, -3:, -3:, -1]
				_snake_case          =      image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				_snake_case          =      np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2.0
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2




@slow
@require_torch_gpu
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'     )

				_snake_case          =      VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'     )
				_snake_case          =      pipeline.to(lowercase     )
				pipeline.set_progress_bar_config(disable=lowercase     )

				# requires GPU generator for gumbel softmax
				# don't use GPU generator in tests though
				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      pipeline(
				    'teddy bear playing in the pool'      ,  num_images_per_prompt=1      ,  generator=lowercase      ,  output_type='np'      ,  )

				_snake_case          =      output.images[0]

				assert image.shape == (256, 256, 3)
				assert np.abs(expected_image - image     ).max() < 2.0

import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def 	a_		(	__lowercase       :     Optional[Any]	, __lowercase       :     Optional[int]	, __lowercase       :     str	, __lowercase       :     Optional[int]	, __lowercase       :     str			)			->  Union[str, Any]:
			# 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'			)['module']

			# Load the entity vocab file
			_snake_case          =      load_original_entity_vocab(__lowercase			)
			# add an entry for [MASK2]
			_snake_case          =      max(entity_vocab.values()			) + 1
			config.entity_vocab_size += 1

			_snake_case          =      XLMRobertaTokenizer.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	, 'tokenizer_config.json'			)	, 'r'			) as f:
						_snake_case          =      json.load(__lowercase			)
			_snake_case          =      'MLukeTokenizer'
			with open(os.path.join(__lowercase	, 'tokenizer_config.json'			)	, 'w'			) as f:
						json.dump(__lowercase	, __lowercase			)

			with open(os.path.join(__lowercase	, MLukeTokenizer.vocab_files_names['entity_vocab_file']			)	, 'w'			) as f:
						json.dump(__lowercase	, __lowercase			)

			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase			)

			# Initialize the embeddings of the special tokens
			_snake_case          =      tokenizer.convert_tokens_to_ids(['@']			)[0]
			_snake_case          =      tokenizer.convert_tokens_to_ids(['#']			)[0]

			_snake_case          =      state_dict['embeddings.word_embeddings.weight']
			_snake_case          =      word_emb[ent_init_index].unsqueeze(0			)
			_snake_case          =      word_emb[enta_init_index].unsqueeze(0			)
			_snake_case          =      torch.cat([word_emb, ent_emb, enta_emb]			)
			# add special tokens for 'entity_predictions.bias'
			for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
						_snake_case          =      state_dict[bias_name]
						_snake_case          =      decoder_bias[ent_init_index].unsqueeze(0			)
						_snake_case          =      decoder_bias[enta_init_index].unsqueeze(0			)
						_snake_case          =      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

			# 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]']].unsqueeze(0			)
			_snake_case          =      torch.cat([entity_emb, entity_mask_emb]			)
			# add [MASK2] for 'entity_predictions.bias'
			_snake_case          =      state_dict['entity_predictions.bias']
			_snake_case          =      entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0			)
			_snake_case          =      torch.cat([entity_prediction_bias, entity_mask_bias]			)

			_snake_case          =      LukeForMaskedLM(config=__lowercase			).eval()

			state_dict.pop('entity_predictions.decoder.weight'			)
			state_dict.pop('lm_head.decoder.weight'			)
			state_dict.pop('lm_head.decoder.bias'			)
			_snake_case          =      OrderedDict()
			for key, value in state_dict.items():
						if not (key.startswith('lm_head'			) or key.startswith('entity_predictions'			)):
									_snake_case          =      state_dict[key]
						else:
									_snake_case          =      state_dict[key]

			_snake_case     , _snake_case          =      model.load_state_dict(__lowercase	, strict=__lowercase			)

			if set(__lowercase			) != {"luke.embeddings.position_ids"}:
						raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''			)
			if set(__lowercase			) != {
			    "lm_head.decoder.weight",
			    "lm_head.decoder.bias",
			    "entity_predictions.decoder.weight",
			}:
						raise ValueError(f'''Unexpected missing_keys: {missing_keys}'''			)

			model.tie_weights()
			assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
			assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

			# Check outputs
			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase	, task='entity_classification'			)

			_snake_case          =      'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
			_snake_case          =      (0, 9)
			_snake_case          =      tokenizer(__lowercase	, entity_spans=[span]	, return_tensors='pt'			)

			_snake_case          =      model(**__lowercase			)

			# Verify word hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						_snake_case          =      torch.Size((1, 33, 768)			)
						_snake_case          =      torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]			)

			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":
						raise NotImplementedError
			else:  # base
						_snake_case          =      torch.Size((1, 1, 768)			)
						_snake_case          =      torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]			)

			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

			# Verify masked word/entity prediction
			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase			)
			_snake_case          =      'Tokyo is the capital of <mask>.'
			_snake_case          =      (24, 30)
			_snake_case          =      tokenizer(__lowercase	, entity_spans=[span]	, return_tensors='pt'			)

			_snake_case          =      model(**__lowercase			)

			_snake_case          =      encoding['input_ids'][0].tolist()
			_snake_case          =      input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'			)			)
			_snake_case          =      outputs.logits[0][mask_position_id].argmax(dim=-1			)
			assert "Japan" == tokenizer.decode(__lowercase			)

			_snake_case          =      outputs.entity_logits[0][0].argmax().item()
			_snake_case          =      [
			    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
			]
			assert [e for e in multilingual_predicted_entities if e.startswith('en:'			)][0] == "en:Japan"

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






def 	a_		(	__lowercase       :     int			)			->  int:
			_snake_case          =      ['[MASK]', '[PAD]', '[UNK]']

			_snake_case          =      [json.loads(__lowercase			) for line in open(__lowercase			)]

			_snake_case          =      {}
			for entry in data:
						_snake_case          =      entry['id']
						for entity_name, language in entry["entities"]:
									if entity_name in SPECIAL_TOKENS:
												_snake_case          =      entity_id
												break
									_snake_case          =      f'''{language}:{entity_name}'''
									_snake_case          =      entity_id
			return new_mapping


if __name__ == "__main__":
			_lowerCamelCase	:    Union[str, Any]          =      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.'''
			)
			_lowerCamelCase	:    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,
			)