

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     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_6					,						_A=1_3					,						_A=7					,						_A=1_4					,						_A=1_0					,						_A=1_9					,						_A=5					,						_A=4					,						_A=True					,						_A=1_6					,						_A=2					,						_A=4					,						_A=4					,						_A="gelu"					,						_A=0.1					,						_A=0.1					,						_A=[1, 2, 3, 4, 5]					,						_A=2_5					,						_A=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        A__  ( 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        A__  ( self					,						_A							):
												__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        A__  ( self							):
												__lowerCAmelCase					=							self.get_config()
												__lowerCAmelCase					=							self.prepare_autoformer_inputs_dict(_A							)
												return config, inputs_dict




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




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							AutoformerModel(config=_A							).to(_A							).eval()
												__lowerCAmelCase					=							model(**_A							)

												__lowerCAmelCase					=							outputs.encoder_last_hidden_state
												__lowerCAmelCase					=							outputs.last_hidden_state

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

												__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							model.create_network_inputs(**_A							)
												__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=_A							)[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(_A							)
																	__lowerCAmelCase					=							AutoformerDecoder.from_pretrained(_A							).to(_A							)

												__lowerCAmelCase					=							decoder(
												    trend=_A					,						inputs_embeds=_A					,						encoder_hidden_states=_A					,						)[0]

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





@require_torch
class     snake_case_			(				_a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
							__UpperCAmelCase       =(AutoformerForPrediction,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




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




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_encoder_decoder_model_standalone(*_A							)




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




							def        A__  ( self							):
												__lowerCAmelCase					=							inspect.signature(getattr(_A					,						'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					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	__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(_A							)]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True

												__lowerCAmelCase					=							getattr(self.model_tester					,						'seq_length'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'decoder_seq_length'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'encoder_seq_length'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'd_model'					,						_A							)
												__lowerCAmelCase					=							getattr(self.model_tester					,						'num_attention_heads'					,						_A							)
												__lowerCAmelCase					=							d_model // num_attention_heads

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

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

																	__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(_A					,						_A							)

																	# decoder attentions
																	__lowerCAmelCase					=							outputs.decoder_attentions
																	self.assertIsInstance(_A					,						(list, tuple)							)
																	self.assertEqual(len(_A							)					,						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(_A					,						(list, tuple)							)
																	self.assertEqual(len(_A							)					,						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(_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(**self._prepare_for_class(_A					,						_A							)							)

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

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

												self.assertEqual(len(_A							)					,						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        A__  ( self							):
												super().test_retain_grad_hidden_states_attentions()


def        __lowercase    (  UpperCAmelCase__="train-batch.pt"      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'		,					filename=UpperCAmelCase__		,					repo_type='dataset'      )
					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location=UpperCAmelCase__      )
					return batch





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


							"""simple docstring"""





							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'							).to(_A							)
												__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(
												    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)							)
												self.assertEqual(output.shape					,						_A							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'							).to(_A							)
												__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((6_4, model.config.context_length, model.config.d_model)							)
												self.assertEqual(output.shape					,						_A							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'							).to(_A							)
												__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((6_4, model.config.num_parallel_samples, model.config.prediction_length)							)
												self.assertEqual(outputs.sequences.shape					,						_A							)

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









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""facebook/bart-large-mnli"""
							__UpperCAmelCase       =(
							    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
							    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
							    """It returns the most likely label in the list of provided `labels` for the input text."""
							)
							__UpperCAmelCase       ="""text_classifier"""
							__UpperCAmelCase       =AutoTokenizer
							__UpperCAmelCase       =AutoModelForSequenceClassification

							__UpperCAmelCase       =["""text""", ["""text"""]]
							__UpperCAmelCase       =["""text"""]




							def        A__  ( self							):
												super().setup()
												__lowerCAmelCase					=							self.model.config
												__lowerCAmelCase					=							-1
												for idx, label in config.idalabel.items():
																	if label.lower().startswith('entail'							):
																						__lowerCAmelCase					=							int(_A							)
												if self.entailment_id == -1:
																	raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'							)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






