
"""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


A_    							=					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  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=16							,						UpperCAmelCase=13							,						UpperCAmelCase=7							,						UpperCAmelCase=14							,						UpperCAmelCase=10							,						UpperCAmelCase=19							,						UpperCAmelCase=5							,						UpperCAmelCase=4							,						UpperCAmelCase=True							,						UpperCAmelCase=16							,						UpperCAmelCase=2							,						UpperCAmelCase=4							,						UpperCAmelCase=4							,						UpperCAmelCase="gelu"							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=[1, 2, 3, 4, 5]							,						UpperCAmelCase=25							,						UpperCAmelCase=5							,						):
							lowerCamelCase_					=  d_model
							lowerCamelCase_					=  parent
							lowerCamelCase_					=  batch_size
							lowerCamelCase_					=  prediction_length
							lowerCamelCase_					=  context_length
							lowerCamelCase_					=  cardinality
							lowerCamelCase_					=  num_time_features
							lowerCamelCase_					=  lags_sequence
							lowerCamelCase_					=  embedding_dimension
							lowerCamelCase_					=  is_training
							lowerCamelCase_					=  hidden_size
							lowerCamelCase_					=  num_hidden_layers
							lowerCamelCase_					=  num_attention_heads
							lowerCamelCase_					=  intermediate_size
							lowerCamelCase_					=  hidden_act
							lowerCamelCase_					=  hidden_dropout_prob
							lowerCamelCase_					=  attention_probs_dropout_prob

							lowerCamelCase_					=  context_length
							lowerCamelCase_					=  prediction_length + label_length
							lowerCamelCase_					=  label_length

							lowerCamelCase_					=  moving_average
							lowerCamelCase_					=  autocorrelation_factor




				def        UpperCAmelCase__						(				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        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  config.context_length + max(config.lags_sequence   )

							lowerCamelCase_					=  ids_tensor([self.batch_size, 1]							,						config.cardinality[0]   )
							lowerCamelCase_					=  floats_tensor([self.batch_size, _past_length, config.num_time_features]   )
							lowerCamelCase_					=  floats_tensor([self.batch_size, _past_length]   )
							lowerCamelCase_					=  floats_tensor([self.batch_size, _past_length]   ) > 0.5

							# decoder inputs
							lowerCamelCase_					=  floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]   )
							lowerCamelCase_					=  floats_tensor([self.batch_size, config.prediction_length]   )

							lowerCamelCase_					=  {
							    '''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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_config()
							lowerCamelCase_					=  self.prepare_autoformer_inputs_dict(UpperCAmelCase   )
							return config, inputs_dict




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_		, lowerCamelCase_					=  self.prepare_config_and_inputs()
							return config, inputs_dict





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  AutoformerModel(config=UpperCAmelCase   ).to(UpperCAmelCase   ).eval()
							lowerCamelCase_					=  model(**UpperCAmelCase   )

							lowerCamelCase_					=  outputs.encoder_last_hidden_state
							lowerCamelCase_					=  outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
										lowerCamelCase_					=  model.get_encoder()
										encoder.save_pretrained(UpperCAmelCase   )
										lowerCamelCase_					=  AutoformerEncoder.from_pretrained(UpperCAmelCase   ).to(UpperCAmelCase   )

							lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  model.create_network_inputs(**UpperCAmelCase   )
							lowerCamelCase_		, lowerCamelCase_					=  model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]   )

							lowerCamelCase_					=  torch.cat(
							    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])							,						dim=-1							,						)
							lowerCamelCase_					=  encoder(inputs_embeds=UpperCAmelCase   )[0]
							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3   )

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

							lowerCamelCase_					=  torch.cat(
							    (
							        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)							,						dim=1   ),
							        feature[:, config.context_length - config.label_length :, ...],
							    )							,						dim=-1							,						)
							lowerCamelCase_					=  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_					=  model.get_decoder()
										decoder.save_pretrained(UpperCAmelCase   )
										lowerCamelCase_					=  AutoformerDecoder.from_pretrained(UpperCAmelCase   ).to(UpperCAmelCase   )

							lowerCamelCase_					=  decoder(
							    trend=UpperCAmelCase							,						inputs_embeds=UpperCAmelCase							,						encoder_hidden_states=UpperCAmelCase							,						)[0]

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




@require_torch
class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      Tuple          =    (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
				a__:      Union[str, Any]          =    (AutoformerForPrediction,) if is_torch_available() else ()
				a__:      List[Any]          =    {'feature-extraction': AutoformerModel} if is_torch_available() else {}
				a__:      str          =    False
				a__:      int          =    False
				a__:      Dict          =    False
				a__:      List[str]          =    False
				a__:      Optional[int]          =    False
				a__:      Optional[Any]          =    False




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoformerModelTester(self   )
							lowerCamelCase_					=  ConfigTester(self							,						config_class=UpperCAmelCase							,						has_text_modality=UpperCAmelCase   )




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_		, lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
										lowerCamelCase_					=  model_class(UpperCAmelCase   )

										with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(UpperCAmelCase   )
													lowerCamelCase_		, lowerCamelCase_					=  model_class.from_pretrained(UpperCAmelCase							,						output_loading_info=UpperCAmelCase   )
										self.assertEqual(info['''missing_keys''']							,						[]   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase   )




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  inspect.signature(getattr(UpperCAmelCase							,						'''forward'''   )   )
							# The main input is the name of the argument after `self`
							lowerCamelCase_					=  list(model_signature.parameters.keys()   )[1]
							self.assertEqual(AutoformerModel.main_input_name							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_		, lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCamelCase_					=  [
										    '''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(UpperCAmelCase   )]							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_		, lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase_					=  True

							lowerCamelCase_					=  getattr(self.model_tester							,						'''seq_length'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  getattr(self.model_tester							,						'''decoder_seq_length'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  getattr(self.model_tester							,						'''encoder_seq_length'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  getattr(self.model_tester							,						'''d_model'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  getattr(self.model_tester							,						'''num_attention_heads'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  d_model // num_attention_heads

							for model_class in self.all_model_classes:
										lowerCamelCase_					=  True
										lowerCamelCase_					=  False
										lowerCamelCase_					=  True
										lowerCamelCase_					=  model_class(UpperCAmelCase   )
										model.to(UpperCAmelCase   )
										model.eval()
										with torch.no_grad():
													lowerCamelCase_					=  model(**self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase   )   )
										lowerCamelCase_					=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
										self.assertEqual(len(UpperCAmelCase   )							,						self.model_tester.num_hidden_layers   )

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

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

										lowerCamelCase_					=  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(UpperCAmelCase							,						UpperCAmelCase   )

										# decoder attentions
										lowerCamelCase_					=  outputs.decoder_attentions
										self.assertIsInstance(UpperCAmelCase							,						(list, tuple)   )
										self.assertEqual(len(UpperCAmelCase   )							,						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_					=  outputs.cross_attentions
										self.assertIsInstance(UpperCAmelCase							,						(list, tuple)   )
										self.assertEqual(len(UpperCAmelCase   )							,						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_					=  True
							lowerCamelCase_					=  True
							lowerCamelCase_					=  model_class(UpperCAmelCase   )
							model.to(UpperCAmelCase   )
							model.eval()
							with torch.no_grad():
										lowerCamelCase_					=  model(**self._prepare_for_class(UpperCAmelCase							,						UpperCAmelCase   )   )

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

							lowerCamelCase_					=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

							self.assertEqual(len(UpperCAmelCase   )							,						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        UpperCAmelCase__						(				self   ):
							super().test_retain_grad_hidden_states_attentions()






def  lowercase						(	lowerCAmelCase__="train-batch.pt"						):
			lowerCamelCase_					=  hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''	,filename=lowerCAmelCase__	,repo_type='''dataset'''						)
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location=lowerCAmelCase__						)
			return batch




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  prepare_batch()

							with torch.no_grad():
										lowerCamelCase_					=  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_					=  torch.Size(
							    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)   )
							self.assertEqual(output.shape							,						UpperCAmelCase   )

							lowerCamelCase_					=  torch.tensor(
							    [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]							,						device=UpperCAmelCase   )
							self.assertTrue(torch.allclose(output[0, :3, :3]							,						UpperCAmelCase							,						atol=UpperCAmelCase   )   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  prepare_batch('''val-batch.pt'''   )
							with torch.no_grad():
										lowerCamelCase_					=  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_					=  torch.Size((64, model.config.context_length, model.config.d_model)   )
							self.assertEqual(output.shape							,						UpperCAmelCase   )

							lowerCamelCase_					=  torch.tensor(
							    [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]							,						device=UpperCAmelCase   )
							self.assertTrue(torch.allclose(output[0, :3, :3]							,						UpperCAmelCase							,						atol=UpperCAmelCase   )   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  prepare_batch('''val-batch.pt'''   )
							with torch.no_grad():
										lowerCamelCase_					=  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_					=  torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)   )
							self.assertEqual(outputs.sequences.shape							,						UpperCAmelCase   )

							lowerCamelCase_					=  torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6]							,						device=UpperCAmelCase   )
							lowerCamelCase_					=  outputs.sequences.mean(dim=1   )
							self.assertTrue(torch.allclose(mean_prediction[0, -3:]							,						UpperCAmelCase							,						rtol=1e-1   )   )





"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

			if finetuned:
						lowerCamelCase_					=  make_linear_from_emb(model.model.shared						)

			return model


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
						)
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument(
						    """--hf_config""",
						    default="""facebook/mbart-large-cc25""",
						    type=str,
						    help="""Which huggingface architecture to use: mbart-large""",
						)
						parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
						parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
						A_    							=					parser.parse_args()
						A_    							=					convert_fairseq_mbart_checkpoint_from_disk(
						    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
						)
						model.save_pretrained(args.pytorch_dump_folder_path)



