from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
				from ...feature_extraction_utils import FeatureExtractionMixin
				from ...tokenization_utils_base import PreTrainedTokenizerBase
				from ...utils import TensorType

A_:   Optional[Any]   =      logging.get_logger(__name__)

A_:   Any   =      {
    '''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}

# fmt: off
A_:   Dict   =      [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
A_:   Optional[Any]   =      [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]


class _lowercase       (							a_    ):





							"""simple docstring"""




							lowerCAmelCase__							=       'whisper'
							lowerCAmelCase__							=       ['past_key_values']
							lowerCAmelCase__							=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



							def __init__(     self  , UpperCAmelCase=51865  , UpperCAmelCase=80  , UpperCAmelCase=6  , UpperCAmelCase=4  , UpperCAmelCase=6  , UpperCAmelCase=4  , UpperCAmelCase=1536  , UpperCAmelCase=1536  , UpperCAmelCase=0.0  , UpperCAmelCase=0.0  , UpperCAmelCase=50257  , UpperCAmelCase=True  , UpperCAmelCase=True  , UpperCAmelCase="gelu"  , UpperCAmelCase=256  , UpperCAmelCase=0.0  , UpperCAmelCase=0.0  , UpperCAmelCase=0.0  , UpperCAmelCase=0.02  , UpperCAmelCase=False  , UpperCAmelCase=1500  , UpperCAmelCase=448  , UpperCAmelCase=50256  , UpperCAmelCase=50256  , UpperCAmelCase=50256  , UpperCAmelCase=None  , UpperCAmelCase=[220, 50256]  , UpperCAmelCase=False  , UpperCAmelCase=256  , UpperCAmelCase=False  , UpperCAmelCase=0.05  , UpperCAmelCase=10  , UpperCAmelCase=2  , UpperCAmelCase=0.0  , UpperCAmelCase=10  , UpperCAmelCase=0  , UpperCAmelCase=7  , **UpperCAmelCase  , ):







														'''simple docstring'''






														_lowercase					=						vocab_size
														_lowercase					=						num_mel_bins
														_lowercase					=						d_model
														_lowercase					=						encoder_layers
														_lowercase					=						encoder_attention_heads
														_lowercase					=						decoder_layers
														_lowercase					=						decoder_attention_heads
														_lowercase					=						decoder_ffn_dim
														_lowercase					=						encoder_ffn_dim
														_lowercase					=						dropout
														_lowercase					=						attention_dropout
														_lowercase					=						activation_dropout
														_lowercase					=						activation_function
														_lowercase					=						init_std
														_lowercase					=						encoder_layerdrop
														_lowercase					=						decoder_layerdrop
														_lowercase					=						use_cache
														_lowercase					=						encoder_layers
														_lowercase					=						scale_embedding  # scale factor will be sqrt(d_model) if True
														_lowercase					=						max_source_positions
														_lowercase					=						max_target_positions

														# Audio Classification-specific parameters. Feel free to ignore for other classes.
														_lowercase					=						classifier_proj_size
														_lowercase					=						use_weighted_layer_sum

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														_lowercase					=						apply_spec_augment
														_lowercase					=						mask_time_prob
														_lowercase					=						mask_time_length
														_lowercase					=						mask_time_min_masks
														_lowercase					=						mask_feature_prob
														_lowercase					=						mask_feature_length
														_lowercase					=						mask_feature_min_masks

														_lowercase					=						median_filter_width

														super().__init__(
														    pad_token_id=lowercase_  , bos_token_id=lowercase_  , eos_token_id=lowercase_  , is_encoder_decoder=lowercase_  , decoder_start_token_id=lowercase_  , suppress_tokens=lowercase_  , begin_suppress_tokens=lowercase_  , **lowercase_  , )






class _lowercase       (							a_    ):





							"""simple docstring"""




							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						OrderedDict(
														    [
														        ("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
														    ]	)
														if self.use_past:
																					_lowercase					=						{0: '''batch'''}
														else:
																					_lowercase					=						{0: '''batch''', 1: '''decoder_sequence'''}

														if self.use_past:
																					self.fill_with_past_key_values_(lowercase_  , direction="""inputs"""	)

														return common_inputs



							def      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase = -1  , UpperCAmelCase = -1  , UpperCAmelCase = False  , UpperCAmelCase = None  , UpperCAmelCase = 22050  , UpperCAmelCase = 5.0  , UpperCAmelCase = 220  , ):







														'''simple docstring'''






														_lowercase					=						OrderedDict()
														_lowercase					=						OnnxConfig.generate_dummy_inputs(
														    self  , preprocessor=preprocessor.feature_extractor  , batch_size=lowercase_  , framework=lowercase_  , sampling_rate=lowercase_  , time_duration=lowercase_  , frequency=lowercase_  , )
														_lowercase					=						encoder_inputs['''input_features'''].shape[2]
														_lowercase					=						encoder_sequence_length // 2 if self.use_past else seq_length

														_lowercase					=						super().generate_dummy_inputs(
														    preprocessor.tokenizer  , lowercase_  , lowercase_  , lowercase_  , lowercase_	)

														_lowercase					=						encoder_inputs.pop("""input_features"""	)
														_lowercase					=						decoder_inputs.pop("""decoder_input_ids"""	)

														if "past_key_values" in decoder_inputs:
																					_lowercase					=						decoder_inputs.pop("""past_key_values"""	)

														return dummy_inputs



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														return 1e-3





"""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


__UpperCamelCase    : Optional[Any]				 =						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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							str  ,lowercase_     :							Optional[Any]  ,lowercase_     :							Optional[Any]=1_6  ,lowercase_     :							int=1_3  ,lowercase_     :							Optional[int]=7  ,lowercase_     :							int=1_4  ,lowercase_     :							str=1_0  ,lowercase_     :							List[Any]=1_9  ,lowercase_     :							Any=5  ,lowercase_     :							Any=4  ,lowercase_     :							List[str]=True  ,lowercase_     :							Union[str, Any]=1_6  ,lowercase_     :							Tuple=2  ,lowercase_     :							str=4  ,lowercase_     :							Tuple=4  ,lowercase_     :							int="gelu"  ,lowercase_     :							Any=0.1  ,lowercase_     :							int=0.1  ,lowercase_     :							Optional[int]=[1, 2, 3, 4, 5]  ,lowercase_     :							List[Any]=2_5  ,lowercase_     :							Union[str, Any]=5  ,):
						lowerCAmelCase__      :		List[str]    							=      d_model
						lowerCAmelCase__      :		Optional[Any]    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		Any    							=      prediction_length
						lowerCAmelCase__      :		str    							=      context_length
						lowerCAmelCase__      :		int    							=      cardinality
						lowerCAmelCase__      :		Dict    							=      num_time_features
						lowerCAmelCase__      :		str    							=      lags_sequence
						lowerCAmelCase__      :		int    							=      embedding_dimension
						lowerCAmelCase__      :		Dict    							=      is_training
						lowerCAmelCase__      :		Optional[Any]    							=      hidden_size
						lowerCAmelCase__      :		Optional[int]    							=      num_hidden_layers
						lowerCAmelCase__      :		List[str]    							=      num_attention_heads
						lowerCAmelCase__      :		int    							=      intermediate_size
						lowerCAmelCase__      :		List[str]    							=      hidden_act
						lowerCAmelCase__      :		Dict    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Optional[int]    							=      attention_probs_dropout_prob

						lowerCAmelCase__      :		Tuple    							=      context_length
						lowerCAmelCase__      :		Optional[Any]    							=      prediction_length + label_length
						lowerCAmelCase__      :		Union[str, Any]    							=      label_length

						lowerCAmelCase__      :		Optional[int]    							=      moving_average
						lowerCAmelCase__      :		Dict    							=      autocorrelation_factor







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						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     :							Union[str, Any]  ,lowercase_     :							str						):
						lowerCAmelCase__      :		str    							=      config.context_length + max(config.lags_sequence						)

						lowerCAmelCase__      :		Optional[int]    							=      ids_tensor([self.batch_size, 1]  ,config.cardinality[0]						)
						lowerCAmelCase__      :		int    							=      floats_tensor([self.batch_size, _past_length, config.num_time_features]						)
						lowerCAmelCase__      :		Any    							=      floats_tensor([self.batch_size, _past_length]						)
						lowerCAmelCase__      :		List[str]    							=      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__      :		Tuple    							=      floats_tensor([self.batch_size, config.prediction_length]						)

						lowerCAmelCase__      :		Optional[int]    							=      {
						    '''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     :							Optional[Any]						):
						lowerCAmelCase__      :		Dict    							=      self.get_config()
						lowerCAmelCase__      :		Dict    							=      self.prepare_autoformer_inputs_dict(lowercase_						)
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      self.prepare_config_and_inputs()
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Optional[int]  ,lowercase_     :							Union[str, Any]						):
						lowerCAmelCase__      :		Any    							=      AutoformerModel(config=lowercase_						).to(lowercase_						).eval()
						lowerCAmelCase__      :		Tuple    							=      model(**lowercase_						)

						lowerCAmelCase__      :		Any    							=      outputs.encoder_last_hidden_state
						lowerCAmelCase__      :		str    							=      outputs.last_hidden_state

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

						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      model.create_network_inputs(**lowercase_						)
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]						)

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

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

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

						lowerCAmelCase__      :		Union[str, Any]    							=      decoder(
						    trend=lowercase_  ,inputs_embeds=lowercase_  ,encoder_hidden_states=lowercase_  ,)[0]

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







@require_torch
class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



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







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		int    							=      AutoformerModelTester(self						)
						lowerCAmelCase__      :		Optional[int]    							=      ConfigTester(self  ,config_class=lowercase_  ,has_text_modality=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		Optional[Any]    							=      model_class(lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Tuple    							=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*lowercase_						)







					@unittest.skip(reason='''Model has no tokens embeddings'''						)
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						pass







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







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

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

							lowerCAmelCase__      :		Optional[int]    							=      [
							    '''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(lowercase_						)]  ,lowercase_						)







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

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

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

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

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

							lowerCAmelCase__      :		List[str]    							=      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(lowercase_  ,lowercase_						)

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

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

						lowerCAmelCase__      :		Optional[Any]    							=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

						self.assertEqual(len(lowercase_						)  ,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     :							str						):
						super().test_retain_grad_hidden_states_attentions()

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







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






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		List[Any]    							=      AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		int    							=      prepare_batch()

						with torch.no_grad():
							lowerCAmelCase__      :		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__      :		Dict    							=      torch.Size(
						    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)						)
						self.assertEqual(output.shape  ,lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      prepare_batch('''val-batch.pt'''						)
						with torch.no_grad():
							lowerCAmelCase__      :		str    							=      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__      :		Any    							=      torch.Size((6_4, model.config.context_length, model.config.d_model)						)
						self.assertEqual(output.shape  ,lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      prepare_batch('''val-batch.pt'''						)
						with torch.no_grad():
							lowerCAmelCase__      :		int    							=      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__      :		List[Any]    							=      torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)						)
						self.assertEqual(outputs.sequences.shape  ,lowercase_						)

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




