




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					(PNDMScheduler,)
						UpperCamelCase     						=					(("""num_inference_steps""", 50),)

						def     A__						(		self   :Union[str, Any]    , **__snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								={
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**__snake_case )
										return config

						def     A__						(		self   :List[str]    , __snake_case   :int=0    , **__snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=dict(self.forward_default_kwargs )
										__magic_name__	:	str								=kwargs.pop("""num_inference_steps"""    , __snake_case )
										__magic_name__	:	Dict								=self.dummy_sample
										__magic_name__	:	int								=0.1 * sample
										__magic_name__	:	List[Any]								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Tuple								=self.get_scheduler_config(**__snake_case )
														__magic_name__	:	List[Any]								=scheduler_class(**__snake_case )
														scheduler.set_timesteps(__snake_case )
														# copy over dummy past residuals
														__magic_name__	:	Optional[int]								=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case )
																		__magic_name__	:	List[str]								=scheduler_class.from_pretrained(__snake_case )
																		new_scheduler.set_timesteps(__snake_case )
																		# copy over dummy past residuals
																		__magic_name__	:	Tuple								=dummy_past_residuals[:]

														__magic_name__	:	Union[str, Any]								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Union[str, Any]								=new_scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

														__magic_name__	:	str								=scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	List[Any]								=new_scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										pass

						def     A__						(		self   :List[Any]    , __snake_case   :Optional[Any]=0    , **__snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								=dict(self.forward_default_kwargs )
										__magic_name__	:	List[str]								=kwargs.pop("""num_inference_steps"""    , __snake_case )
										__magic_name__	:	List[Any]								=self.dummy_sample
										__magic_name__	:	Optional[Any]								=0.1 * sample
										__magic_name__	:	Optional[Any]								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Tuple								=self.get_scheduler_config()
														__magic_name__	:	Optional[Any]								=scheduler_class(**__snake_case )
														scheduler.set_timesteps(__snake_case )

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__	:	str								=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case )
																		__magic_name__	:	List[Any]								=scheduler_class.from_pretrained(__snake_case )
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(__snake_case )

																		# copy over dummy past residual (must be after setting timesteps)
																		__magic_name__	:	Optional[Any]								=dummy_past_residuals[:]

														__magic_name__	:	str								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Tuple								=new_scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

														__magic_name__	:	Union[str, Any]								=scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Union[str, Any]								=new_scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

						def     A__						(		self   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.scheduler_classes[0]
										__magic_name__	:	Union[str, Any]								=self.get_scheduler_config(**__snake_case )
										__magic_name__	:	Dict								=scheduler_class(**__snake_case )

										__magic_name__	:	Tuple								=10
										__magic_name__	:	List[Any]								=self.dummy_model()
										__magic_name__	:	Dict								=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case )

										for i, t in enumerate(scheduler.prk_timesteps ):
														__magic_name__	:	List[str]								=model(__snake_case    , __snake_case )
														__magic_name__	:	List[Any]								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case ).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps ):
														__magic_name__	:	Tuple								=model(__snake_case    , __snake_case )
														__magic_name__	:	List[Any]								=scheduler.step_plms(__snake_case    , __snake_case    , __snake_case ).prev_sample

										return sample

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=dict(self.forward_default_kwargs )

										__magic_name__	:	Dict								=kwargs.pop("""num_inference_steps"""    , __snake_case )

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Optional[int]								=self.get_scheduler_config()
														__magic_name__	:	int								=scheduler_class(**__snake_case )

														__magic_name__	:	Optional[Any]								=self.dummy_sample
														__magic_name__	:	List[str]								=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case    , """set_timesteps""" ):
																		scheduler.set_timesteps(__snake_case )
														elif num_inference_steps is not None and not hasattr(__snake_case    , """set_timesteps""" ):
																		__magic_name__	:	str								=num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														__magic_name__	:	Any								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														__magic_name__	:	Tuple								=dummy_past_residuals[:]

														__magic_name__	:	Optional[int]								=scheduler.step_prk(__snake_case    , 0    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Optional[Any]								=scheduler.step_prk(__snake_case    , 1    , __snake_case    , **__snake_case ).prev_sample

														self.assertEqual(output_a.shape    , sample.shape )
														self.assertEqual(output_a.shape    , output_a.shape )

														__magic_name__	:	Optional[int]								=scheduler.step_plms(__snake_case    , 0    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Tuple								=scheduler.step_plms(__snake_case    , 1    , __snake_case    , **__snake_case ).prev_sample

														self.assertEqual(output_a.shape    , sample.shape )
														self.assertEqual(output_a.shape    , output_a.shape )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__snake_case )

										__magic_name__	:	Any								=self.scheduler_classes[0]
										__magic_name__	:	str								=self.get_scheduler_config(steps_offset=1 )
										__magic_name__	:	Optional[int]								=scheduler_class(**__snake_case )
										scheduler.set_timesteps(10 )
										assert torch.equal(
										    scheduler.timesteps    , torch.LongTensor(
										        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )    , )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.0001, 0.001]    , [0.002, 0.02] ):
														self.check_over_configs(beta_start=__snake_case    , beta_end=__snake_case )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=__snake_case )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__snake_case )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										for t in [1, 5, 10]:
														self.check_over_forward(time_step=__snake_case )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]    , [10, 50, 1_00] ):
														self.check_over_forward(num_inference_steps=__snake_case )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=27

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Optional[Any]								=self.dummy_sample
														__magic_name__	:	Optional[int]								=0.1 * sample

														__magic_name__	:	List[str]								=self.get_scheduler_config()
														__magic_name__	:	Any								=scheduler_class(**__snake_case )

														scheduler.set_timesteps(__snake_case )

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2] ):
																		__magic_name__	:	Union[str, Any]								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case ).prev_sample

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										with self.assertRaises(__snake_case ):
														__magic_name__	:	Any								=self.scheduler_classes[0]
														__magic_name__	:	Optional[Any]								=self.get_scheduler_config()
														__magic_name__	:	Any								=scheduler_class(**__snake_case )

														scheduler.step_plms(self.dummy_sample    , 1    , self.dummy_sample ).prev_sample

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.full_loop()
										__magic_name__	:	List[str]								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Union[str, Any]								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 198.1318 ) < 1E-2
										assert abs(result_mean.item() - 0.2580 ) < 1E-3

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.full_loop(prediction_type="""v_prediction""" )
										__magic_name__	:	int								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Tuple								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 67.3986 ) < 1E-2
										assert abs(result_mean.item() - 0.0878 ) < 1E-3

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.full_loop(set_alpha_to_one=__snake_case    , beta_start=0.01 )
										__magic_name__	:	Dict								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Dict								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 230.0399 ) < 1E-2
										assert abs(result_mean.item() - 0.2995 ) < 1E-3


						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.full_loop(set_alpha_to_one=__snake_case    , beta_start=0.01 )
										__magic_name__	:	Union[str, Any]								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Any								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 186.9482 ) < 1E-2
										assert abs(result_mean.item() - 0.2434 ) < 1E-3










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_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				# Load configuration defined in the metadata file
				with open(lowerCamelCase   ) as metadata_file:
								__magic_name__	:	List[Any]								=json.load(lowerCamelCase   )
				__magic_name__	:	Optional[int]								=LukeConfig(use_entity_aware_attention=lowerCamelCase	,     **metadata["""model_config"""]   )

				# Load in the weights from the checkpoint_path
				__magic_name__	:	List[str]								=torch.load(lowerCamelCase	,     map_location="""cpu"""   )

				# Load the entity vocab file
				__magic_name__	:	Dict								=load_entity_vocab(lowerCamelCase   )

				__magic_name__	:	Union[str, Any]								=RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]   )

				# Add special tokens to the token vocabulary for downstream tasks
				__magic_name__	:	List[str]								=AddedToken("""<ent>"""	,     lstrip=lowerCamelCase	,     rstrip=lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								=AddedToken("""<ent2>"""	,     lstrip=lowerCamelCase	,     rstrip=lowerCamelCase   )
				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(lowerCamelCase   )
				with open(os.path.join(lowerCamelCase	,     LukeTokenizer.vocab_files_names["""entity_vocab_file"""]   )	,     """w"""   ) as f:
								json.dump(lowerCamelCase	,     lowerCamelCase   )

				__magic_name__	:	Optional[Any]								=LukeTokenizer.from_pretrained(lowerCamelCase   )

				# Initialize the embeddings of the special tokens
				__magic_name__	:	List[str]								=state_dict["""embeddings.word_embeddings.weight"""]
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""@"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""#"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Tuple								=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"]:
												__magic_name__	:	Optional[Any]								=F"encoder.layer.{layer_index}.attention.self."
												__magic_name__	:	Optional[Any]								=state_dict[prefix + matrix_name]
												__magic_name__	:	Any								=state_dict[prefix + matrix_name]
												__magic_name__	:	Dict								=state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				__magic_name__	:	str								=state_dict["""entity_embeddings.entity_embeddings.weight"""]
				__magic_name__	:	Union[str, Any]								=entity_emb[entity_vocab["""[MASK]"""]]

				__magic_name__	:	Optional[int]								=LukeModel(config=lowerCamelCase   ).eval()

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

				__magic_name__	:	int								=(
				    """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 ."""
				)
				__magic_name__	:	Optional[Any]								=(39, 42)
				__magic_name__	:	Dict								=tokenizer(lowerCamelCase	,     entity_spans=[span]	,     add_prefix_space=lowerCamelCase	,     return_tensors="""pt"""   )

				__magic_name__	:	str								=model(**lowerCamelCase   )

				# Verify word hidden states
				if model_size == "large":
								__magic_name__	:	Tuple								=torch.Size((1, 42, 1024)   )
								__magic_name__	:	int								=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
								__magic_name__	:	Dict								=torch.Size((1, 42, 768)   )
								__magic_name__	:	List[Any]								=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]	,     lowerCamelCase	,     atol=1E-4   ):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								__magic_name__	:	Optional[Any]								=torch.Size((1, 1, 1024)   )
								__magic_name__	:	Tuple								=torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]   )
				else:  # base
								__magic_name__	:	List[Any]								=torch.Size((1, 1, 768)   )
								__magic_name__	:	Dict								=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]	,     lowerCamelCase	,     atol=1E-4   ):
								raise ValueError

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




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Tuple								={}
				with open(lowerCamelCase	,     """r"""	,     encoding="""utf-8"""   ) as f:
								for index, line in enumerate(lowerCamelCase   ):
												__magic_name__				, __magic_name__	:	Optional[Any]								=line.rstrip().split("""\t"""   )
												__magic_name__	:	Union[str, Any]								=index

				return entity_vocab


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[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."
							)
							UpperCAmelCase_		:				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,
							)




