



'''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_     :       int								=						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   :      Any				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[Any]=16				,    lowerCAmelCase__   :      List[Any]=13				,    lowerCAmelCase__   :      Union[str, Any]=7				,    lowerCAmelCase__   :      int=14				,    lowerCAmelCase__   :      List[str]=10				,    lowerCAmelCase__   :      str=19				,    lowerCAmelCase__   :      Dict=5				,    lowerCAmelCase__   :      List[str]=4				,    lowerCAmelCase__   :      int=True				,    lowerCAmelCase__   :      Optional[int]=16				,    lowerCAmelCase__   :      int=2				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Tuple=4				,    lowerCAmelCase__   :      int="gelu"				,    lowerCAmelCase__   :      Dict=0.1				,    lowerCAmelCase__   :      Union[str, Any]=0.1				,    lowerCAmelCase__   :      Any=[1, 2, 3, 4, 5]				,    lowerCAmelCase__   :      Dict=25				,    lowerCAmelCase__   :      Optional[Any]=5				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			d_model
													SCREAMING_SNAKE_CASE					:      Tuple     					=			parent
													SCREAMING_SNAKE_CASE					:      List[str]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      Any     					=			prediction_length
													SCREAMING_SNAKE_CASE					:      Any     					=			context_length
													SCREAMING_SNAKE_CASE					:      Any     					=			cardinality
													SCREAMING_SNAKE_CASE					:      Dict     					=			num_time_features
													SCREAMING_SNAKE_CASE					:      int     					=			lags_sequence
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			embedding_dimension
													SCREAMING_SNAKE_CASE					:      str     					=			is_training
													SCREAMING_SNAKE_CASE					:      List[str]     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      int     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      List[str]     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      int     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      int     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			attention_probs_dropout_prob

													SCREAMING_SNAKE_CASE					:      str     					=			context_length
													SCREAMING_SNAKE_CASE					:      Any     					=			prediction_length + label_length
													SCREAMING_SNAKE_CASE					:      int     					=			label_length

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







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													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 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			config.context_length + max(config.lags_sequence				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ids_tensor([self.batch_size, 1]				,    config.cardinality[0]				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			floats_tensor([self.batch_size, _past_length, config.num_time_features]				)
													SCREAMING_SNAKE_CASE					:      int     					=			floats_tensor([self.batch_size, _past_length]				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			floats_tensor([self.batch_size, _past_length]				) > 0.5

													# decoder inputs
													SCREAMING_SNAKE_CASE					:      str     					=			floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			floats_tensor([self.batch_size, config.prediction_length]				)

													SCREAMING_SNAKE_CASE					:      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 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_config()
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.prepare_autoformer_inputs_dict(lowerCAmelCase__				)
													return config, inputs_dict







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			self.prepare_config_and_inputs()
													return config, inputs_dict







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			AutoformerModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).eval()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			outputs.encoder_last_hidden_state
													SCREAMING_SNAKE_CASE					:      Dict     					=			outputs.last_hidden_state

													with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.get_encoder()
																				encoder.save_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			AutoformerEncoder.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.create_network_inputs(**lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]				)

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

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

													SCREAMING_SNAKE_CASE					:      Any     					=			torch.cat(
													    (
													        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)				,    dim=1				),
													        feature[:, config.context_length - config.label_length :, ...],
													    )				,    dim=-1				,    )
													SCREAMING_SNAKE_CASE					:      Any     					=			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:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model.get_decoder()
																				decoder.save_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			AutoformerDecoder.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			decoder(
													    trend=lowerCAmelCase__				,    inputs_embeds=lowerCAmelCase__				,    encoder_hidden_states=lowerCAmelCase__				,    )[0]

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


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







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			AutoformerModelTester(self				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    has_text_modality=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			model_class(lowerCAmelCase__				)

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model_class.from_pretrained(lowerCAmelCase__				,    output_loading_info=lowerCAmelCase__				)
																				self.assertEqual(info['''missing_keys''']				,    []				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__				)







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

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			inspect.signature(getattr(lowerCAmelCase__				,    '''forward'''				)				)
													# The main input is the name of the argument after `self`
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			list(model_signature.parameters.keys()				)[1]
													self.assertEqual(AutoformerModel.main_input_name				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[
																				    '''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(lowerCAmelCase__				)]				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      int     					=			True

													SCREAMING_SNAKE_CASE					:      Dict     					=			getattr(self.model_tester				,    '''seq_length'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			getattr(self.model_tester				,    '''decoder_seq_length'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			getattr(self.model_tester				,    '''encoder_seq_length'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			getattr(self.model_tester				,    '''d_model'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			getattr(self.model_tester				,    '''num_attention_heads'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			d_model // num_attention_heads

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

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

																				self.assertListEqual(
																				    list(attentions[0].shape[-3:]				)				,    [self.model_tester.num_attention_heads, encoder_seq_length, dim]				,    )
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			len(lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			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(lowerCAmelCase__				,    lowerCAmelCase__				)

																				# decoder attentions
																				SCREAMING_SNAKE_CASE					:      Dict     					=			outputs.decoder_attentions
																				self.assertIsInstance(lowerCAmelCase__				,    (list, tuple)				)
																				self.assertEqual(len(lowerCAmelCase__				)				,    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
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			outputs.cross_attentions
																				self.assertIsInstance(lowerCAmelCase__				,    (list, tuple)				)
																				self.assertEqual(len(lowerCAmelCase__				)				,    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
													SCREAMING_SNAKE_CASE					:      Dict     					=			True
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			True
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model_class(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE					:      Dict     					=			model(**self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				)

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

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

													self.assertEqual(len(lowerCAmelCase__				)				,    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 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													super().test_retain_grad_hidden_states_attentions()


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






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







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			prepare_batch()

													with torch.no_grad():
																				SCREAMING_SNAKE_CASE					:      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]

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.Size(
													    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)				)
													self.assertEqual(output.shape				,    lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			prepare_batch('''val-batch.pt'''				)
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE					:      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
													SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.Size((64, model.config.context_length, model.config.d_model)				)
													self.assertEqual(output.shape				,    lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			prepare_batch('''val-batch.pt'''				)
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE					:      str     					=			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''']				,    )
													SCREAMING_SNAKE_CASE					:      str     					=			torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)				)
													self.assertEqual(outputs.sequences.shape				,    lowerCAmelCase__				)

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









'''simple docstring'''



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Optional[int]					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1.5
							SCREAMING_SNAKE_CASE					:      str     					=			int(factor * num_class_images				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ClipClient(
							    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1				)

							os.makedirs(F"""{class_data_dir}/images"""					,		exist_ok=A				)
							if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
														return

							while True:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			client.query(text=A				)
														if len(A				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(factor * num_images				)
																					SCREAMING_SNAKE_CASE					:      str     					=			ClipClient(
																					    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1					,		)

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
							SCREAMING_SNAKE_CASE					:      Tuple     					=			tqdm(desc='''downloading real regularization images'''					,		total=A				)

							with open(F"""{class_data_dir}/caption.txt"""					,		'''w'''				) as fa, open(F"""{class_data_dir}/urls.txt"""					,		'''w'''				) as fa, open(
							    F"""{class_data_dir}/images.txt"""					,		'''w'''				) as fa:
														while total < num_class_images:
																					SCREAMING_SNAKE_CASE					:      int     					=			class_images[count]
																					count += 1
																					try:
																												SCREAMING_SNAKE_CASE					:      int     					=			requests.get(images['''url''']				)
																												if img.status_code == 200:
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			Image.open(BytesIO(img.content				)				)
																																			with open(F"""{class_data_dir}/images/{total}.jpg"""					,		'''wb'''				) as f:
																																										f.write(img.content				)
																																			fa.write(images['''caption'''] + '''\n'''				)
																																			fa.write(images['''url'''] + '''\n'''				)
																																			fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '''\n'''				)
																																			total += 1
																																			pbar.update(1				)
																												else:
																																			continue
																					except Exception:
																												continue
							return
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			argparse.ArgumentParser(''''''					,		add_help=A				)
							parser.add_argument('''--class_prompt'''					,		help='''text prompt to retrieve images'''					,		required=A					,		type=A				)
							parser.add_argument('''--class_data_dir'''					,		help='''path to save images'''					,		required=A					,		type=A				)
							parser.add_argument('''--num_class_images'''					,		help='''number of images to download'''					,		default=200					,		type=A				)
							return parser.parse_args()


if __name__ == "__main__":
							lowerCAmelCase_     :       List[str]								=						parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




