


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation






def 					UpperCamelCase   (      __magic_name__	:				List[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						384
		if "tiny" in model_name:
				lowercase__	     =						[3, 3, 9, 3]
				lowercase__	     =						[96, 192, 384, 768]
		if "small" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[96, 192, 384, 768]
		if "base" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[128, 256, 512, 1024]
				lowercase__	     =						512
		if "large" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[192, 384, 768, 1536]
				lowercase__	     =						768
		if "xlarge" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[256, 512, 1024, 2048]
				lowercase__	     =						1024

		# set label information
		lowercase__	     =						150
		lowercase__	     =						"""huggingface/label-files"""
		lowercase__	     =						"""ade20k-id2label.json"""
		lowercase__	     =						json.load(open(hf_hub_download(__magic_name__       ,       __magic_name__       ,       repo_type="""dataset"""	)       ,       """r"""	)	)
		lowercase__	     =						{int(__magic_name__	): v for k, v in idalabel.items()}
		lowercase__	     =						{v: k for k, v in idalabel.items()}

		lowercase__	     =						ConvNextConfig(
		    depths=__magic_name__       ,       hidden_sizes=__magic_name__       ,       out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	)
		lowercase__	     =						UperNetConfig(
		    backbone_config=__magic_name__       ,       auxiliary_in_channels=__magic_name__       ,       num_labels=__magic_name__       ,       idalabel=__magic_name__       ,       labelaid=__magic_name__       ,       )

		return config






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						[]

		# fmt: off
		# stem
		rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")	)
		rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")	)
		rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")	)
		rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")	)
		# stages
		for i in range(len(config.backbone_config.depths	)	):
				for j in range(config.backbone_config.depths[i]	):
						rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')	)
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')	)
				if i > 0:
						rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')	)
						rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')	)
						rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')	)
						rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')	)

				rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')	)
				rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')	)

		# decode head
		rename_keys.extend(
		    [
		        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
		        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
		        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
		        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
		    ]	)
		# fmt: on

		return rename_keys






def 					UpperCamelCase   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				str       ,       __magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						dct.pop(__magic_name__	)
		lowercase__	     =						val






def 					UpperCamelCase   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Union[str, Any]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						{
		    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
		    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
		    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
		    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
		    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
		}
		lowercase__	     =						model_name_to_url[model_name]
		lowercase__	     =						torch.hub.load_state_dict_from_url(__magic_name__       ,       map_location="""cpu"""	)["""state_dict"""]

		lowercase__	     =						get_upernet_config(__magic_name__	)
		lowercase__	     =						UperNetForSemanticSegmentation(__magic_name__	)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				lowercase__	     =						state_dict.pop(__magic_name__	)
				if "bn" in key:
						lowercase__	     =						key.replace("""bn"""       ,       """batch_norm"""	)
				lowercase__	     =						val

		# rename keys
		lowercase__	     =						create_rename_keys(__magic_name__	)
		for src, dest in rename_keys:
				rename_key(__magic_name__       ,       __magic_name__       ,       __magic_name__	)

		model.load_state_dict(__magic_name__	)

		# verify on image
		lowercase__	     =						"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
		lowercase__	     =						Image.open(requests.get(__magic_name__       ,       stream=__magic_name__	).raw	).convert("""RGB"""	)

		lowercase__	     =						SegformerImageProcessor()
		lowercase__	     =						processor(__magic_name__       ,       return_tensors="""pt"""	).pixel_values

		with torch.no_grad():
				lowercase__	     =						model(__magic_name__	)

		if model_name == "upernet-convnext-tiny":
				lowercase__	     =						torch.tensor(
				    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]	)
		elif model_name == "upernet-convnext-small":
				lowercase__	     =						torch.tensor(
				    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]	)
		elif model_name == "upernet-convnext-base":
				lowercase__	     =						torch.tensor(
				    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]	)
		elif model_name == "upernet-convnext-large":
				lowercase__	     =						torch.tensor(
				    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]	)
		elif model_name == "upernet-convnext-xlarge":
				lowercase__	     =						torch.tensor(
				    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]	)
		print("""Logits:"""       ,       outputs.logits[0, 0, :3, :3]	)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,       __magic_name__       ,       atol=1E-4	)
		print("""Looks ok!"""	)

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
				model.save_pretrained(__magic_name__	)
				print(f'''Saving processor to {pytorch_dump_folder_path}'''	)
				processor.save_pretrained(__magic_name__	)

		if push_to_hub:
				print(f'''Pushing model and processor for {model_name} to hub'''	)
				model.push_to_hub(f'''openmmlab/{model_name}'''	)
				processor.push_to_hub(f'''openmmlab/{model_name}'''	)


if __name__ == "__main__":
	A			:						Any        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='upernet-convnext-tiny',
	    type=str,
	    choices=[F'upernet-convnext-{size}' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
	    help='Name of the ConvNext UperNet model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	A			:						Union[str, Any]        =  parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''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


lowercase_									=    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					,  A					,  A=16					,  A=13					,  A=7					,  A=14					,  A=10					,  A=19					,  A=5					,  A=4					,  A=True					,  A=16					,  A=2					,  A=4					,  A=4					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=[1, 2, 3, 4, 5]					,  A=25					,  A=5					,  )       ->	List[str]:


      """simple docstring"""



      _a           =    d_model
      _a           =    parent
      _a           =    batch_size
      _a           =    prediction_length
      _a           =    context_length
      _a           =    cardinality
      _a           =    num_time_features
      _a           =    lags_sequence
      _a           =    embedding_dimension
      _a           =    is_training
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob

      _a           =    context_length
      _a           =    prediction_length + label_length
      _a           =    label_length

      _a           =    moving_average
      _a           =    autocorrelation_factor



 def    a__    (self			)       ->	Any:


      """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    a__    (self					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    config.context_length + max(config.lags_sequence			)

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

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

      _a           =    {
          '''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			)       ->	Any:


      """simple docstring"""



      _a           =    self.get_config()
      _a           =    self.prepare_autoformer_inputs_dict(A			)
      return config, inputs_dict



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a    ,       _a           =    self.prepare_config_and_inputs()
      return config, inputs_dict



 def    a__    (self					,  A					,  A			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    AutoformerModel(config=A			).to(A			).eval()
      _a           =    model(**A			)

      _a           =    outputs.encoder_last_hidden_state
      _a           =    outputs.last_hidden_state

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

      _a    ,       _a    ,       _a    ,       _a    ,       _a           =    model.create_network_inputs(**A			)
      _a    ,       _a           =    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]			)

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

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

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

      _a           =    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'''







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



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    AutoformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  has_text_modality=A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
           _a           =    model_class(A			)

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



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    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			)       ->	Tuple:


      """simple docstring"""



      pass



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



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



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

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

           _a           =    [
               '''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			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    True

      _a           =    getattr(self.model_tester					,  '''seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''decoder_seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''encoder_seq_length'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''d_model'''					,  A			)
      _a           =    getattr(self.model_tester					,  '''num_attention_heads'''					,  A			)
      _a           =    d_model // num_attention_heads

      for model_class in self.all_model_classes:
           _a           =    True
           _a           =    False
           _a           =    True
           _a           =    model_class(A			)
           model.to(A			)
           model.eval()
           with torch.no_grad():
                _a           =    model(**self._prepare_for_class(A					,  A			)			)
           _a           =    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"]
           _a           =    True
           _a           =    model_class(A			)
           model.to(A			)
           model.eval()
           with torch.no_grad():
                _a           =    model(**self._prepare_for_class(A					,  A			)			)
           _a           =    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]					,  )
           _a           =    len(A			)

           _a           =    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
           _a           =    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
           _a           =    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
      _a           =    True
      _a           =    True
      _a           =    model_class(A			)
      model.to(A			)
      model.eval()
      with torch.no_grad():
           _a           =    model(**self._prepare_for_class(A					,  A			)			)

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

      _a           =    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			)       ->	Optional[Any]:


      """simple docstring"""



      super().test_retain_grad_hidden_states_attentions()





def lowerCAmelCase    (__A="train-batch.pt"):



     """simple docstring"""



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






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



 '''simple docstring'''







 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''			).to(A			)
      _a           =    prepare_batch()

      with torch.no_grad():
           _a           =    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]

      _a           =    torch.Size(
          (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)			)
      self.assertEqual(output.shape					,  A			)

      _a           =    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			)       ->	Any:


      """simple docstring"""



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

      _a           =    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			)       ->	Tuple:


      """simple docstring"""



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

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



