

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    				=     1E-4

if is_torch_available():
  import torch

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





@require_torch
class A__    :



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	Union[str, Any]     ,						A_ :	Dict=1_6     ,						A_ :	Optional[Any]=1_3     ,						A_ :	Any=7     ,						A_ :	Any=1_4     ,						A_ :	Optional[Any]=1_0     ,						A_ :	Tuple=1_9     ,						A_ :	str=5     ,						A_ :	List[str]=4     ,						A_ :	List[str]=True     ,						A_ :	Union[str, Any]=1_6     ,						A_ :	Optional[Any]=2     ,						A_ :	Any=4     ,						A_ :	Tuple=4     ,						A_ :	List[Any]="gelu"     ,						A_ :	str=0.1     ,						A_ :	Tuple=0.1     ,						A_ :	List[str]=[1, 2, 3, 4, 5]     ,						A_ :	Dict=2_5     ,						A_ :	int=5     ,						):






      '''simple docstring'''




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

      _lowerCAmelCase   :					str        =    context_length
      _lowerCAmelCase   :					Any        =    prediction_length + label_length
      _lowerCAmelCase   :					Any        =    label_length

      _lowerCAmelCase   :					Tuple        =    moving_average
      _lowerCAmelCase   :					Tuple        =    autocorrelation_factor

 def    __magic_name__       (		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    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    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   :					Tuple        =    floats_tensor([self.batch_size, _past_length]       )
      _lowerCAmelCase   :					str        =    floats_tensor([self.batch_size, _past_length]       ) > 0.5

      # decoder inputs
      _lowerCAmelCase   :					int        =    floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]       )
      _lowerCAmelCase   :					List[Any]        =    floats_tensor([self.batch_size, config.prediction_length]       )

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

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.get_config()
      _lowerCAmelCase   :					Dict        =    self.prepare_autoformer_inputs_dict(_A       )
      return config, inputs_dict

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.prepare_config_and_inputs()
      return config, inputs_dict


 def    __magic_name__       (		self :	Any     ,						A_ :	int     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    AutoformerModel(config=_A       ).to(_A       ).eval()
      _lowerCAmelCase   :					Tuple        =    model(**_A       )

      _lowerCAmelCase   :					Any        =    outputs.encoder_last_hidden_state
      _lowerCAmelCase   :					List[Any]        =    outputs.last_hidden_state

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

      _lowerCAmelCase   :					Dict        =    model.create_network_inputs(**_A       )
      _lowerCAmelCase   :					List[Any]        =    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]       )

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

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

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

      with tempfile.TemporaryDirectory() as tmpdirname:
           _lowerCAmelCase   :					Dict        =    model.get_decoder()
           decoder.save_pretrained(_A       )
           _lowerCAmelCase   :					Dict        =    AutoformerDecoder.from_pretrained(_A       ).to(_A       )

      _lowerCAmelCase   :					Tuple        =    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 A__    ( a__  ,		a__  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 _lowercase     :					List[str]      =						(AutoformerForPrediction,) if is_torch_available() else ()
 _lowercase     :					Any      =						{'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
 _lowercase     :					List[str]      =						False
 _lowercase     :					Any      =						False
 _lowercase     :					Dict      =						False
 _lowercase     :					Tuple      =						False
 _lowercase     :					int      =						False
 _lowercase     :					List[str]      =						False

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    AutoformerModelTester(self       )
      _lowerCAmelCase   :					List[Any]        =    ConfigTester(self     ,						config_class=_A     ,						has_text_modality=_A       )

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






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
           _lowerCAmelCase   :					Dict        =    model_class(_A       )

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

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    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    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      pass

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    inspect.signature(getattr(_A     ,						"forward"       )       )
      # The main input is the name of the argument after `self`
      _lowerCAmelCase   :					str        =    list(model_signature.parameters.keys()       )[1]
      self.assertEqual(AutoformerModel.main_input_name     ,						_A       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowerCAmelCase   :					Dict        =    [
               '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    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCAmelCase   :					Any        =    True

      _lowerCAmelCase   :					str        =    getattr(self.model_tester     ,						"seq_length"     ,						_A       )
      _lowerCAmelCase   :					List[str]        =    getattr(self.model_tester     ,						"decoder_seq_length"     ,						_A       )
      _lowerCAmelCase   :					int        =    getattr(self.model_tester     ,						"encoder_seq_length"     ,						_A       )
      _lowerCAmelCase   :					List[Any]        =    getattr(self.model_tester     ,						"d_model"     ,						_A       )
      _lowerCAmelCase   :					str        =    getattr(self.model_tester     ,						"num_attention_heads"     ,						_A       )
      _lowerCAmelCase   :					Optional[Any]        =    d_model // num_attention_heads

      for model_class in self.all_model_classes:
           _lowerCAmelCase   :					Any        =    True
           _lowerCAmelCase   :					Any        =    False
           _lowerCAmelCase   :					int        =    True
           _lowerCAmelCase   :					int        =    model_class(_A       )
           model.to(_A       )
           model.eval()
           with torch.no_grad():
                _lowerCAmelCase   :					Union[str, Any]        =    model(**self._prepare_for_class(_A     ,						_A       )       )
           _lowerCAmelCase   :					Any        =    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   :					int        =    True
           _lowerCAmelCase   :					List[str]        =    model_class(_A       )
           model.to(_A       )
           model.eval()
           with torch.no_grad():
                _lowerCAmelCase   :					Optional[Any]        =    model(**self._prepare_for_class(_A     ,						_A       )       )
           _lowerCAmelCase   :					List[Any]        =    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   :					Union[str, Any]        =    len(_A       )

           _lowerCAmelCase   :					Union[str, 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(_A     ,						_A       )

           # decoder attentions
           _lowerCAmelCase   :					Dict        =    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   :					Any        =    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   :					str        =    True
      _lowerCAmelCase   :					List[Any]        =    True
      _lowerCAmelCase   :					Union[str, Any]        =    model_class(_A       )
      model.to(_A       )
      model.eval()
      with torch.no_grad():
           _lowerCAmelCase   :					Dict        =    model(**self._prepare_for_class(_A     ,						_A       )       )

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

      _lowerCAmelCase   :					str        =    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    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      super().test_retain_grad_hidden_states_attentions()







def 	_snake_case		(    SCREAMING_SNAKE_CASE="train-batch.pt"				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch"		,							filename=_lowerCamelCase		,							repo_type="dataset"				)
     _lowerCAmelCase   :					Optional[Any]        =    torch.load(_lowerCamelCase		,							map_location=_lowerCamelCase				)
     return batch





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



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly"       ).to(_A       )
      _lowerCAmelCase   :					Dict        =    prepare_batch()

      with torch.no_grad():
           _lowerCAmelCase   :					Union[str, 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   :					Any        =    torch.Size(
          (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)       )
      self.assertEqual(output.shape     ,						_A       )

      _lowerCAmelCase   :					Dict        =    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    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"       ).to(_A       )
      _lowerCAmelCase   :					Any        =    prepare_batch("val-batch.pt"       )
      with torch.no_grad():
           _lowerCAmelCase   :					List[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   :					Dict        =    torch.Size((6_4, model.config.context_length, model.config.d_model)       )
      self.assertEqual(output.shape     ,						_A       )

      _lowerCAmelCase   :					Tuple        =    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    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"       ).to(_A       )
      _lowerCAmelCase   :					int        =    prepare_batch("val-batch.pt"       )
      with torch.no_grad():
           _lowerCAmelCase   :					Any        =    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[str]        =    torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)       )
      self.assertEqual(outputs.sequences.shape     ,						_A       )

      _lowerCAmelCase   :					Dict        =    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=_A       )
      _lowerCAmelCase   :					Optional[int]        =    outputs.sequences.mean(dim=1       )
      self.assertTrue(torch.allclose(mean_prediction[0, -3:]     ,						_A     ,						rtol=1E-1       )       )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase    				=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['YolosFeatureExtractor']
  __UpperCAmelCase    				=     ['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






