"""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__  		=						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	:  Tuple   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  str=16   ,				__lowerCAmelCase	:  List[str]=13   ,				__lowerCAmelCase	:  Tuple=7   ,				__lowerCAmelCase	:  Tuple=14   ,				__lowerCAmelCase	:  int=10   ,				__lowerCAmelCase	:  Any=19   ,				__lowerCAmelCase	:  Tuple=5   ,				__lowerCAmelCase	:  str=4   ,				__lowerCAmelCase	:  Union[str, Any]=True   ,				__lowerCAmelCase	:  List[str]=16   ,				__lowerCAmelCase	:  int=2   ,				__lowerCAmelCase	:  Any=4   ,				__lowerCAmelCase	:  int=4   ,				__lowerCAmelCase	:  int="gelu"   ,				__lowerCAmelCase	:  Optional[int]=0.1   ,				__lowerCAmelCase	:  int=0.1   ,				__lowerCAmelCase	:  Optional[Any]=[1, 2, 3, 4, 5]   ,				__lowerCAmelCase	:  List[Any]=25   ,				__lowerCAmelCase	:  str=5   ,				):
      _UpperCAmelCase						 =      d_model
      _UpperCAmelCase						 =      parent
      _UpperCAmelCase						 =      batch_size
      _UpperCAmelCase						 =      prediction_length
      _UpperCAmelCase						 =      context_length
      _UpperCAmelCase						 =      cardinality
      _UpperCAmelCase						 =      num_time_features
      _UpperCAmelCase						 =      lags_sequence
      _UpperCAmelCase						 =      embedding_dimension
      _UpperCAmelCase						 =      is_training
      _UpperCAmelCase						 =      hidden_size
      _UpperCAmelCase						 =      num_hidden_layers
      _UpperCAmelCase						 =      num_attention_heads
      _UpperCAmelCase						 =      intermediate_size
      _UpperCAmelCase						 =      hidden_act
      _UpperCAmelCase						 =      hidden_dropout_prob
      _UpperCAmelCase						 =      attention_probs_dropout_prob

      _UpperCAmelCase						 =      context_length
      _UpperCAmelCase						 =      prediction_length + label_length
      _UpperCAmelCase						 =      label_length

      _UpperCAmelCase						 =      moving_average
      _UpperCAmelCase						 =      autocorrelation_factor


    def      lowerCAmelCase_  (					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      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  int					):
      _UpperCAmelCase						 =      config.context_length + max(config.lags_sequence					)

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

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

      _UpperCAmelCase						 =      {
          """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]					):
      _UpperCAmelCase						 =      self.get_config()
      _UpperCAmelCase						 =      self.prepare_autoformer_inputs_dict(__lowerCAmelCase					)
      return config, inputs_dict


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


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      AutoformerModel(config=__lowerCAmelCase					).to(__lowerCAmelCase					).eval()
      _UpperCAmelCase						 =      model(**__lowerCAmelCase					)

      _UpperCAmelCase						 =      outputs.encoder_last_hidden_state
      _UpperCAmelCase						 =      outputs.last_hidden_state

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

      _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      model.create_network_inputs(**__lowerCAmelCase					)
      _UpperCAmelCase ,				_UpperCAmelCase						 =      model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]					)

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

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

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

      _UpperCAmelCase						 =      decoder(
          trend=__lowerCAmelCase   ,				inputs_embeds=__lowerCAmelCase   ,				encoder_hidden_states=__lowerCAmelCase   ,				)[0]

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



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


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


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      self.config_tester.run_common_tests()


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
        _UpperCAmelCase						 =      model_class(__lowerCAmelCase					)

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


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*__lowerCAmelCase					)


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


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


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()

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

        _UpperCAmelCase						 =      [
            """past_values""",
            """past_time_features""",
            """past_observed_mask""",
            """static_categorical_features""",
            """static_real_features""",
            """future_values""",
            """future_time_features""",
        ]

        if model.__class__.__name__ in ["AutoformerForPrediction"]:
          expected_arg_names.append("""future_observed_mask"""					)

        expected_arg_names.extend(
            [
                """decoder_attention_mask""",
                """head_mask""",
                """decoder_head_mask""",
                """cross_attn_head_mask""",
                """encoder_outputs""",
                """past_key_values""",
                """output_hidden_states""",
                """output_attentions""",
                """use_cache""",
                """return_dict""",
            ]					)

        self.assertListEqual(arg_names[: len(__lowerCAmelCase					)]   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()
      _UpperCAmelCase						 =      True

      _UpperCAmelCase						 =      getattr(self.model_tester   ,				"""seq_length"""   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      getattr(self.model_tester   ,				"""decoder_seq_length"""   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      getattr(self.model_tester   ,				"""encoder_seq_length"""   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      getattr(self.model_tester   ,				"""d_model"""   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      getattr(self.model_tester   ,				"""num_attention_heads"""   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      d_model // num_attention_heads

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

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

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

        _UpperCAmelCase						 =      7

        if "last_hidden_state" in outputs:
          correct_outlen += 1

        if "trend" in outputs:
          correct_outlen += 1

        if "past_key_values" in outputs:
          correct_outlen += 1  # past_key_values have been returned

        if "loss" in outputs:
          correct_outlen += 1

        if "params" in outputs:
          correct_outlen += 1

        self.assertEqual(__lowerCAmelCase   ,				__lowerCAmelCase					)

        # decoder attentions
        _UpperCAmelCase						 =      outputs.decoder_attentions
        self.assertIsInstance(__lowerCAmelCase   ,				(list, tuple)					)
        self.assertEqual(len(__lowerCAmelCase					)   ,				self.model_tester.num_hidden_layers					)
        self.assertListEqual(
            list(decoder_attentions[0].shape[-3:]					)   ,				[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,				)

        # cross attentions
        _UpperCAmelCase						 =      outputs.cross_attentions
        self.assertIsInstance(__lowerCAmelCase   ,				(list, tuple)					)
        self.assertEqual(len(__lowerCAmelCase					)   ,				self.model_tester.num_hidden_layers					)
        self.assertListEqual(
            list(cross_attentions[0].shape[-3:]					)   ,				[self.model_tester.num_attention_heads, decoder_seq_length, dim]   ,				)

      # Check attention is always last and order is fine
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      model_class(__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      with torch.no_grad():
        _UpperCAmelCase						 =      model(**self._prepare_for_class(__lowerCAmelCase   ,				__lowerCAmelCase					)					)

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

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

      self.assertEqual(len(__lowerCAmelCase					)   ,				self.model_tester.num_hidden_layers					)
      self.assertListEqual(
          list(self_attentions[0].shape[-3:]					)   ,				[self.model_tester.num_attention_heads, encoder_seq_length, dim]   ,				)


    @is_flaky()
    def      lowerCAmelCase_  (					self	:  List[str]					):
      super().test_retain_grad_hidden_states_attentions()






def     __UpperCAmelCase					(			lowercase="train-batch.pt"		):







  """simple docstring"""
  _UpperCAmelCase						 =      hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""   ,filename=lowercase   ,repo_type="""dataset"""		)
  _UpperCAmelCase						 =      torch.load(lowercase   ,map_location=lowercase		)
  return batch


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


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      prepare_batch()

      with torch.no_grad():
        _UpperCAmelCase						 =      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]

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

      _UpperCAmelCase						 =      torch.tensor(
          [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]   ,				device=__lowerCAmelCase					)
      self.assertTrue(torch.allclose(output[0, :3, :3]   ,				__lowerCAmelCase   ,				atol=__lowerCAmelCase					)					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      prepare_batch("""val-batch.pt"""					)
      with torch.no_grad():
        _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      torch.Size((64, model.config.context_length, model.config.d_model)					)
      self.assertEqual(output.shape   ,				__lowerCAmelCase					)

      _UpperCAmelCase						 =      torch.tensor(
          [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]   ,				device=__lowerCAmelCase					)
      self.assertTrue(torch.allclose(output[0, :3, :3]   ,				__lowerCAmelCase   ,				atol=__lowerCAmelCase					)					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      prepare_batch("""val-batch.pt"""					)
      with torch.no_grad():
        _UpperCAmelCase						 =      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"""]   ,				)
      _UpperCAmelCase						 =      torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)					)
      self.assertEqual(outputs.sequences.shape   ,				__lowerCAmelCase					)

      _UpperCAmelCase						 =      torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]   ,				device=__lowerCAmelCase					)
      _UpperCAmelCase						 =      outputs.sequences.mean(dim=1					)
      self.assertTrue(torch.allclose(mean_prediction[0, -3:]   ,				__lowerCAmelCase   ,				rtol=1e-1					)					)

"""simple docstring"""


# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase__  		=						abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  from diffusers.utils.testing_utils import pytest_addoption_shared

  pytest_addoption_shared(lowercase		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  from diffusers.utils.testing_utils import pytest_terminal_summary_main

  _UpperCAmelCase						 =      terminalreporter.config.getoption("""--make-reports"""		)
  if make_reports:
    pytest_terminal_summary_main(lowercase   ,id=lowercase		)
