
"""simple docstring"""







import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

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


lowerCamelCase__           =	1E-4

if is_torch_available():
							import torch

							from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
							from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder




@require_torch
class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=13 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=14 ,	_SCREAMING_SNAKE_CASE=10 ,	_SCREAMING_SNAKE_CASE=19 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=[1, 2, 3, 4, 5] ,	_SCREAMING_SNAKE_CASE=25 ,	_SCREAMING_SNAKE_CASE=5 ,	):
			__lowerCAmelCase    :		Optional[Any]		    =   d_model
			__lowerCAmelCase    :		List[str]		    =   parent
			__lowerCAmelCase    :		Tuple		    =   batch_size
			__lowerCAmelCase    :		Any		    =   prediction_length
			__lowerCAmelCase    :		List[Any]		    =   context_length
			__lowerCAmelCase    :		Optional[Any]		    =   cardinality
			__lowerCAmelCase    :		Union[str, Any]		    =   num_time_features
			__lowerCAmelCase    :		int		    =   lags_sequence
			__lowerCAmelCase    :		Any		    =   embedding_dimension
			__lowerCAmelCase    :		Dict		    =   is_training
			__lowerCAmelCase    :		List[str]		    =   hidden_size
			__lowerCAmelCase    :		Any		    =   num_hidden_layers
			__lowerCAmelCase    :		int		    =   num_attention_heads
			__lowerCAmelCase    :		Optional[Any]		    =   intermediate_size
			__lowerCAmelCase    :		List[Any]		    =   hidden_act
			__lowerCAmelCase    :		Any		    =   hidden_dropout_prob
			__lowerCAmelCase    :		int		    =   attention_probs_dropout_prob

			__lowerCAmelCase    :		Union[str, Any]		    =   context_length
			__lowerCAmelCase    :		List[str]		    =   prediction_length + label_length
			__lowerCAmelCase    :		List[Any]		    =   label_length

			__lowerCAmelCase    :		List[Any]		    =   moving_average
			__lowerCAmelCase    :		int		    =   autocorrelation_factor

		def 							__lowerCamelCase							(   self				):
			return AutoformerConfig(
			    d_model=self.d_model ,	encoder_layers=self.num_hidden_layers ,	decoder_layers=self.num_hidden_layers ,	encoder_attention_heads=self.num_attention_heads ,	decoder_attention_heads=self.num_attention_heads ,	encoder_ffn_dim=self.intermediate_size ,	decoder_ffn_dim=self.intermediate_size ,	dropout=self.hidden_dropout_prob ,	attention_dropout=self.attention_probs_dropout_prob ,	prediction_length=self.prediction_length ,	context_length=self.context_length ,	label_length=self.label_length ,	lags_sequence=self.lags_sequence ,	num_time_features=self.num_time_features ,	num_static_categorical_features=1 ,	cardinality=[self.cardinality] ,	embedding_dimension=[self.embedding_dimension] ,	moving_average=self.moving_average ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   config.context_length + max(config.lags_sequence				)

			__lowerCAmelCase    :		int		    =   ids_tensor([self.batch_size, 1] ,	config.cardinality[0]				)
			__lowerCAmelCase    :		Optional[Any]		    =   floats_tensor([self.batch_size, _past_length, config.num_time_features]				)
			__lowerCAmelCase    :		Optional[int]		    =   floats_tensor([self.batch_size, _past_length]				)
			__lowerCAmelCase    :		Optional[Any]		    =   floats_tensor([self.batch_size, _past_length]				) > 0.5

			# decoder inputs
			__lowerCAmelCase    :		List[str]		    =   floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]				)
			__lowerCAmelCase    :		List[str]		    =   floats_tensor([self.batch_size, config.prediction_length]				)

			__lowerCAmelCase    :		Optional[Any]		    =   {
			    'past_values': past_values,
			    'static_categorical_features': static_categorical_features,
			    'past_time_features': past_time_features,
			    'past_observed_mask': past_observed_mask,
			    'future_time_features': future_time_features,
			    'future_values': future_values,
			}
			return inputs_dict

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.get_config()
			__lowerCAmelCase    :		int		    =   self.prepare_autoformer_inputs_dict(_SCREAMING_SNAKE_CASE				)
			return config, inputs_dict

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   self.prepare_config_and_inputs()
			return config, inputs_dict







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   AutoformerModel(config=_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				).eval()
			__lowerCAmelCase    :		Dict		    =   model(**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   outputs.encoder_last_hidden_state
			__lowerCAmelCase    :		Dict		    =   outputs.last_hidden_state

			with tempfile.TemporaryDirectory() as tmpdirname:
				__lowerCAmelCase    :		Optional[int]		    =   model.get_encoder()
				encoder.save_pretrained(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		int		    =   AutoformerEncoder.from_pretrained(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[int]		    =   model.create_network_inputs(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]				)

			__lowerCAmelCase    :		Any		    =   torch.cat(
			    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) ,	dim=-1 ,	)
			__lowerCAmelCase    :		List[str]		    =   encoder(inputs_embeds=_SCREAMING_SNAKE_CASE				)[0]
			self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3				)

			__lowerCAmelCase    :		List[Any]		    =   (
			    torch.mean(transformer_inputs[:, : config.context_length, ...] ,	dim=1				)
			    .unsqueeze(1				)
			    .repeat(1 ,	config.prediction_length ,	1				)
			)
			__lowerCAmelCase    :		Union[str, Any]		    =   torch.zeros(
			    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] ,	device=enc_input.device ,	)

			__lowerCAmelCase    :		int		    =   torch.cat(
			    (
			        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) ,	dim=1				),
			        feature[:, config.context_length - config.label_length :, ...],
			    ) ,	dim=-1 ,	)
			__lowerCAmelCase    :		List[str]		    =   torch.cat(
			    (
			        torch.cat((trend_input[:, -config.label_length :, ...], mean) ,	dim=1				),
			        feature[:, config.context_length - config.label_length :, ...],
			    ) ,	dim=-1 ,	)

			with tempfile.TemporaryDirectory() as tmpdirname:
				__lowerCAmelCase    :		Any		    =   model.get_decoder()
				decoder.save_pretrained(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		List[str]		    =   AutoformerDecoder.from_pretrained(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   decoder(
			    trend=_SCREAMING_SNAKE_CASE ,	inputs_embeds=_SCREAMING_SNAKE_CASE ,	encoder_hidden_states=_SCREAMING_SNAKE_CASE ,	)[0]

			self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3				)




@require_torch
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: str            =			(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
		A_		: int            =			(AutoformerForPrediction,) if is_torch_available() else ()
		A_		: Optional[Any]            =			{'feature-extraction': AutoformerModel} if is_torch_available() else {}
		A_		: Any            =			False
		A_		: Any            =			False
		A_		: List[str]            =			False
		A_		: Any            =			False
		A_		: Tuple            =			False
		A_		: Tuple            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   AutoformerModelTester(self				)
			__lowerCAmelCase    :		Dict		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	has_text_modality=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self.config_tester.run_common_tests()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Tuple		    =   model_class(_SCREAMING_SNAKE_CASE				)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   model_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	output_loading_info=_SCREAMING_SNAKE_CASE				)
				self.assertEqual(info['missing_keys'] ,	[]				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE				)

		@unittest.skip(reason='Model has no tokens embeddings'				)
		def 							__lowerCamelCase							(   self				):
			pass

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   inspect.signature(getattr(_SCREAMING_SNAKE_CASE ,	'forward'				)				)
			# The main input is the name of the argument after `self`
			__lowerCAmelCase    :		int		    =   list(model_signature.parameters.keys()				)[1]
			self.assertEqual(AutoformerModel.main_input_name ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase    :		List[str]		    =   [
				    'past_values',
				    'past_time_features',
				    'past_observed_mask',
				    'static_categorical_features',
				    'static_real_features',
				    'future_values',
				    'future_time_features',
				]

				if model.__class__.__name__ in ["AutoformerForPrediction"]:
					expected_arg_names.append('future_observed_mask'				)

				expected_arg_names.extend(
				    [
				        'decoder_attention_mask',
				        'head_mask',
				        'decoder_head_mask',
				        'cross_attn_head_mask',
				        'encoder_outputs',
				        'past_key_values',
				        'output_hidden_states',
				        'output_attentions',
				        'use_cache',
				        'return_dict',
				    ]				)

				self.assertListEqual(arg_names[: len(_SCREAMING_SNAKE_CASE				)] ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase    :		Union[str, Any]		    =   True

			__lowerCAmelCase    :		Dict		    =   getattr(self.model_tester ,	'seq_length' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   getattr(self.model_tester ,	'decoder_seq_length' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   getattr(self.model_tester ,	'encoder_seq_length' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   getattr(self.model_tester ,	'd_model' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   getattr(self.model_tester ,	'num_attention_heads' ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   d_model // num_attention_heads

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Optional[Any]		    =   True
				__lowerCAmelCase    :		Optional[int]		    =   False
				__lowerCAmelCase    :		Tuple		    =   True
				__lowerCAmelCase    :		Tuple		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()
				with torch.no_grad():
					__lowerCAmelCase    :		Union[str, Any]		    =   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)
				__lowerCAmelCase    :		Optional[Any]		    =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	self.model_tester.num_hidden_layers				)

				# check that output_attentions also work using config
				del inputs_dict["output_attentions"]
				__lowerCAmelCase    :		Optional[int]		    =   True
				__lowerCAmelCase    :		Dict		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()
				with torch.no_grad():
					__lowerCAmelCase    :		Optional[int]		    =   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)
				__lowerCAmelCase    :		Any		    =   outputs.encoder_attentions
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	self.model_tester.num_hidden_layers				)

				self.assertListEqual(
				    list(attentions[0].shape[-3:]				) ,	[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,	)
				__lowerCAmelCase    :		int		    =   len(_SCREAMING_SNAKE_CASE				)

				__lowerCAmelCase    :		int		    =   7

				if "last_hidden_state" in outputs:
					correct_outlen += 1

				if "trend" in outputs:
					correct_outlen += 1

				if "past_key_values" in outputs:
					correct_outlen += 1  # past_key_values have been returned

				if "loss" in outputs:
					correct_outlen += 1

				if "params" in outputs:
					correct_outlen += 1

				self.assertEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

				# decoder attentions
				__lowerCAmelCase    :		List[Any]		    =   outputs.decoder_attentions
				self.assertIsInstance(_SCREAMING_SNAKE_CASE ,	(list, tuple)				)
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	self.model_tester.num_hidden_layers				)
				self.assertListEqual(
				    list(decoder_attentions[0].shape[-3:]				) ,	[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,	)

				# cross attentions
				__lowerCAmelCase    :		Optional[int]		    =   outputs.cross_attentions
				self.assertIsInstance(_SCREAMING_SNAKE_CASE ,	(list, tuple)				)
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	self.model_tester.num_hidden_layers				)
				self.assertListEqual(
				    list(cross_attentions[0].shape[-3:]				) ,	[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,	)

			# Check attention is always last and order is fine
			__lowerCAmelCase    :		Dict		    =   True
			__lowerCAmelCase    :		Dict		    =   True
			__lowerCAmelCase    :		Union[str, Any]		    =   model_class(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			with torch.no_grad():
				__lowerCAmelCase    :		Optional[int]		    =   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

			self.assertEqual(out_len + 2 ,	len(_SCREAMING_SNAKE_CASE				)				)

			__lowerCAmelCase    :		List[Any]		    =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

			self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	self.model_tester.num_hidden_layers				)
			self.assertListEqual(
			    list(self_attentions[0].shape[-3:]				) ,	[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,	)







		@is_flaky()
		def 							__lowerCamelCase							(   self				):
			super().test_retain_grad_hidden_states_attentions()



def 					__lowerCAmelCase       (_UpperCamelCase="train-batch.pt"	):
	__lowerCAmelCase    :		List[str]		    =   hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch' ,						filename=_UpperCamelCase ,						repo_type='dataset'	)
	__lowerCAmelCase    :		Any		    =   torch.load(_UpperCamelCase ,						map_location=_UpperCamelCase	)
	return batch




@require_torch
@slow
class 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'				).to(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   prepare_batch()

			with torch.no_grad():
				__lowerCAmelCase    :		List[Any]		    =   model(
				    past_values=batch['past_values'] ,	past_time_features=batch['past_time_features'] ,	past_observed_mask=batch['past_observed_mask'] ,	static_categorical_features=batch['static_categorical_features'] ,	future_values=batch['future_values'] ,	future_time_features=batch['future_time_features'] ,	)[0]

			__lowerCAmelCase    :		List[str]		    =   torch.Size(
			    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)				)
			self.assertEqual(output.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   torch.tensor(
			    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] ,	device=_SCREAMING_SNAKE_CASE				)
			self.assertTrue(torch.allclose(output[0, :3, :3] ,	_SCREAMING_SNAKE_CASE ,	atol=_SCREAMING_SNAKE_CASE				)				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'				).to(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   prepare_batch('val-batch.pt'				)
			with torch.no_grad():
				__lowerCAmelCase    :		List[Any]		    =   model(
				    past_values=batch['past_values'] ,	past_time_features=batch['past_time_features'] ,	past_observed_mask=batch['past_observed_mask'] ,	static_categorical_features=batch['static_categorical_features'] ,	).encoder_last_hidden_state
			__lowerCAmelCase    :		Dict		    =   torch.Size((64, model.config.context_length, model.config.d_model)				)
			self.assertEqual(output.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   torch.tensor(
			    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] ,	device=_SCREAMING_SNAKE_CASE				)
			self.assertTrue(torch.allclose(output[0, :3, :3] ,	_SCREAMING_SNAKE_CASE ,	atol=_SCREAMING_SNAKE_CASE				)				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'				).to(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   prepare_batch('val-batch.pt'				)
			with torch.no_grad():
				__lowerCAmelCase    :		Tuple		    =   model.generate(
				    static_categorical_features=batch['static_categorical_features'] ,	past_time_features=batch['past_time_features'] ,	past_values=batch['past_values'] ,	future_time_features=batch['future_time_features'] ,	past_observed_mask=batch['past_observed_mask'] ,	)
			__lowerCAmelCase    :		Dict		    =   torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)				)
			self.assertEqual(outputs.sequences.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   torch.tensor([3130.6763, 4056.5293, 7053.0786] ,	device=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   outputs.sequences.mean(dim=1				)
			self.assertTrue(torch.allclose(mean_prediction[0, -3:] ,	_SCREAMING_SNAKE_CASE ,	rtol=1E-1				)				)

"""simple docstring"""







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase__           =	data_utils.TransfoXLTokenizer
lowerCamelCase__           =	data_utils.TransfoXLCorpus
lowerCamelCase__           =	data_utils
lowerCamelCase__           =	data_utils
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(_UpperCamelCase ,						'rb'	) as fp:
			__lowerCAmelCase    :		int		    =   pickle.load(_UpperCamelCase ,						encoding='latin1'	)
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		__lowerCAmelCase    :		Any		    =   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
		print(F"Save vocabulary to {pytorch_vocab_dump_path}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   corpus.vocab.__dict__
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   corpus.__dict__
		corpus_dict_no_vocab.pop('vocab' ,						_UpperCamelCase	)
		__lowerCAmelCase    :		List[str]		    =   pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F"Save dataset to {pytorch_dataset_dump_path}"	)
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		__lowerCAmelCase    :		Tuple		    =   os.path.abspath(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   os.path.abspath(_UpperCamelCase	)

		print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."	)
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			__lowerCAmelCase    :		Any		    =   TransfoXLConfig()
		else:
			__lowerCAmelCase    :		str		    =   TransfoXLConfig.from_json_file(_UpperCamelCase	)
		print(F"Building PyTorch model from configuration: {config}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   TransfoXLLMHeadModel(_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   load_tf_weights_in_transfo_xl(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
		# Save pytorch-model
		__lowerCAmelCase    :		str		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		__lowerCAmelCase    :		Tuple		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		print(F"Save PyTorch model to {os.path.abspath(_UpperCamelCase	)}"	)
		torch.save(model.state_dict() ,						_UpperCamelCase	)
		print(F"Save configuration file to {os.path.abspath(_UpperCamelCase	)}"	)
		with open(_UpperCamelCase ,						'w' ,						encoding='utf-8'	) as f:
			f.write(config.to_json_string()	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
							)
							parser.add_argument(
							    """--tf_checkpoint_path""",
							    default="""""",
							    type=str,
							    help="""An optional path to a TensorFlow checkpoint path to be converted.""",
							)
							parser.add_argument(
							    """--transfo_xl_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained BERT model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--transfo_xl_dataset_file""",
							    default="""""",
							    type=str,
							    help="""An optional dataset file to be converted in a vocabulary.""",
							)
							lowerCamelCase__           =	parser.parse_args()
							convert_transfo_xl_checkpoint_to_pytorch(
							    args.tf_checkpoint_path,
							    args.transfo_xl_config_file,
							    args.pytorch_dump_folder_path,
							    args.transfo_xl_dataset_file,
							)