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


SCREAMING_SNAKE_CASE :	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    A_ :

  def __init__(		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=16     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=13     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=7     ,							__SCREAMING_SNAKE_CASE :      Dict=14     ,							__SCREAMING_SNAKE_CASE :      Tuple=10     ,							__SCREAMING_SNAKE_CASE :      Any=19     ,							__SCREAMING_SNAKE_CASE :      int=5     ,							__SCREAMING_SNAKE_CASE :      str=4     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=16     ,							__SCREAMING_SNAKE_CASE :      str=2     ,							__SCREAMING_SNAKE_CASE :      List[str]=4     ,							__SCREAMING_SNAKE_CASE :      int=4     ,							__SCREAMING_SNAKE_CASE :      Tuple="gelu"     ,							__SCREAMING_SNAKE_CASE :      Dict=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=[1, 2, 3, 4, 5]     ,							__SCREAMING_SNAKE_CASE :      Tuple=25     ,							__SCREAMING_SNAKE_CASE :      List[Any]=5     ,							):
   __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 		_UpperCAmelCase   (		self :      Optional[int] ):
   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 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Dict ):
   __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 		_UpperCAmelCase   (		self :      Any ):
   __a       =  self.get_config()
   __a       =  self.prepare_autoformer_inputs_dict(__UpperCamelCase )
   return config, inputs_dict

  def 		_UpperCAmelCase   (		self :      str ):
   __a       ,     __a       =  self.prepare_config_and_inputs()
   return config, inputs_dict

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Tuple ):
   __a       =  AutoformerModel(config=__UpperCamelCase ).to(__UpperCamelCase ).eval()
   __a       =  model(**__UpperCamelCase )

   __a       =  outputs.encoder_last_hidden_state
   __a       =  outputs.last_hidden_state

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

   __a       ,     __a       ,     __a       ,     __a       ,     __a       =  model.create_network_inputs(**__UpperCamelCase )
   __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=__UpperCamelCase )[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(__UpperCamelCase )
    __a       =  AutoformerDecoder.from_pretrained(__UpperCamelCase ).to(__UpperCamelCase )

   __a       =  decoder(
       trend=__UpperCamelCase     ,							inputs_embeds=__UpperCamelCase     ,							encoder_hidden_states=__UpperCamelCase     ,							)[0]

   self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class    A_ (       _UpperCAmelCase			,       _UpperCAmelCase			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE    				=			(AutoformerForPrediction,) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE    				=			{'feature-extraction': AutoformerModel} if is_torch_available() else {}
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      List[str] ):
   __a       =  AutoformerModelTester(self )
   __a       =  ConfigTester(self     ,							config_class=__UpperCamelCase     ,							has_text_modality=__UpperCamelCase )

  def 		_UpperCAmelCase   (		self :      str ):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase   (		self :      Any ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
    __a       =  model_class(__UpperCamelCase )

    with tempfile.TemporaryDirectory() as tmpdirname:
     model.save_pretrained(__UpperCamelCase )
     __a       ,     __a       =  model_class.from_pretrained(__UpperCamelCase     ,							output_loading_info=__UpperCamelCase )
    self.assertEqual(info["missing_keys"]     ,							[] )

  def 		_UpperCAmelCase   (		self :      str ):
   __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_encoder_decoder_model_standalone(*__UpperCamelCase )

  @unittest.skip(reason="Model has no tokens embeddings" )
  def 		_UpperCAmelCase   (		self :      Union[str, Any] ):
   pass

  def 		_UpperCAmelCase   (		self :      Tuple ):
   __a       =  inspect.signature(getattr(__UpperCamelCase     ,							"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     ,							__UpperCamelCase )

  def 		_UpperCAmelCase   (		self :      Any ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  model_class(__UpperCamelCase )
    __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(__UpperCamelCase )]     ,							__UpperCamelCase )

  def 		_UpperCAmelCase   (		self :      int ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   __a       =  True

   __a       =  getattr(self.model_tester     ,							"seq_length"     ,							__UpperCamelCase )
   __a       =  getattr(self.model_tester     ,							"decoder_seq_length"     ,							__UpperCamelCase )
   __a       =  getattr(self.model_tester     ,							"encoder_seq_length"     ,							__UpperCamelCase )
   __a       =  getattr(self.model_tester     ,							"d_model"     ,							__UpperCamelCase )
   __a       =  getattr(self.model_tester     ,							"num_attention_heads"     ,							__UpperCamelCase )
   __a       =  d_model // num_attention_heads

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

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

    __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(__UpperCamelCase     ,							__UpperCamelCase )

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

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

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

   self.assertEqual(len(__UpperCamelCase )     ,							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 		_UpperCAmelCase   (		self :      Union[str, Any] ):
   super().test_retain_grad_hidden_states_attentions()



def   __A		(			_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 ):

  def 		_UpperCAmelCase   (		self :      Optional[Any] ):
   __a       =  AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__UpperCamelCase )
   __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     ,							__UpperCamelCase )

   __a       =  torch.tensor(
       [[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]]     ,							device=__UpperCamelCase )
   self.assertTrue(torch.allclose(output[0, :3, :3]     ,							__UpperCamelCase     ,							atol=__UpperCamelCase ) )

  def 		_UpperCAmelCase   (		self :      int ):
   __a       =  AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__UpperCamelCase )
   __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     ,							__UpperCamelCase )

   __a       =  torch.tensor(
       [[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]]     ,							device=__UpperCamelCase )
   self.assertTrue(torch.allclose(output[0, :3, :3]     ,							__UpperCamelCase     ,							atol=__UpperCamelCase ) )

  def 		_UpperCAmelCase   (		self :      Dict ):
   __a       =  AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__UpperCamelCase )
   __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     ,							__UpperCamelCase )

   __a       =  torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86]     ,							device=__UpperCamelCase )
   __a       =  outputs.sequences.mean(dim=1 )
   self.assertTrue(torch.allclose(mean_prediction[0, -3:]     ,							__UpperCamelCase     ,							rtol=1E-1 ) )



# Function to print upper half of diamond (pyramid)



def   __A		(			_A					):



 """simple docstring"""


 for i in range(0							,				_A					):
  for _ in range(0							,				n - i - 1					):  # printing spaces
   print(" "							,				end=""					)
  for _ in range(0							,				i + 1					):  # printing stars
   print("* "							,				end=""					)
  print()



def   __A		(			_A					):



 """simple docstring"""


 for i in range(_A							,				0							,				-1					):
  for _ in range(_A							,				0							,				-1					):  # printing stars
   print("* "							,				end=""					)
  print()
  for _ in range(n - i + 1							,				0							,				-1					):  # printing spaces
   print(" "							,				end=""					)



def   __A		(			_A					):



 """simple docstring"""


 if n <= 0:
  print("       ...       ....        nothing printing :("					)
  return
 floyd(_A					)  # upper half
 reverse_floyd(_A					)  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE :	Tuple							     =		1
  while K:
    SCREAMING_SNAKE_CASE :	Tuple							     =		int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE :	Optional[int]							     =		int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")


