





'''simple docstring'''






import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

A_		:  List[str]		 =						yaml.safe_load(
    "\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n  - name: \"Dataset Card for X\" # First-level markdown heading\n    allow_empty: false\n    allow_empty_text: true\n    subsections:\n      - name: \"Table of Contents\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections: null\n      - name: \"Dataset Description\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections:\n          - name: \"Dataset Summary\"\n            allow_empty: false\n            allow_empty_text: false\n            subsections: null\n          - name: \"Supported Tasks and Leaderboards\"\n            allow_empty: true\n            allow_empty_text: true\n            subsections: null\n          - name: Languages\n            allow_empty: false\n            allow_empty_text: true\n            subsections: null\n"
)


A_		:  List[str]		 =						{
    "name": "root",
    "text": "",
    "is_empty_text": True,
    "subsections": [
        {
            "name": "Dataset Card for My Dataset",
            "text": "",
            "is_empty_text": True,
            "subsections": [
                {"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
                {
                    "name": "Dataset Description",
                    "text": "Some text here.",
                    "is_empty_text": False,
                    "subsections": [
                        {
                            "name": "Dataset Summary",
                            "text": "Some text here.",
                            "is_empty_text": False,
                            "subsections": [],
                        },
                        {
                            "name": "Supported Tasks and Leaderboards",
                            "text": "",
                            "is_empty_text": True,
                            "subsections": [],
                        },
                        {"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
                    ],
                },
            ],
        }
    ],
}


A_		:  Any		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"


A_		:  str		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

A_		:  Optional[Any]		 =						{
    "name": "root",
    "text": "",
    "is_empty_text": True,
    "subsections": [
        {
            "name": "Dataset Card for My Dataset",
            "text": "",
            "is_empty_text": True,
            "subsections": [
                {"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
                {
                    "name": "Dataset Description",
                    "text": "Some text here.",
                    "is_empty_text": False,
                    "subsections": [
                        {
                            "name": "Dataset Summary",
                            "text": "Some text here.",
                            "is_empty_text": False,
                            "subsections": [
                                {
                                    "name": "Extra Ignored Subsection",
                                    "text": "",
                                    "is_empty_text": True,
                                    "subsections": [],
                                }
                            ],
                        },
                        {
                            "name": "Supported Tasks and Leaderboards",
                            "text": "",
                            "is_empty_text": True,
                            "subsections": [],
                        },
                        {"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
                    ],
                },
            ],
        }
    ],
}

A_		:  int		 =						"\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

A_		:  List[str]		 =						(
    "The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."
)

A_		:  Union[str, Any]		 =						"\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

A_		:  int		 =						(
    "The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."
)

A_		:  Optional[int]		 =						"\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

A_		:  Any		 =						"The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."

A_		:  Tuple		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
A_		:  Any		 =						"The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."


A_		:  Optional[int]		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"
A_		:  Any		 =						"The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."

A_		:  Union[str, Any]		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n"

A_		:  List[Any]		 =						"The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."


A_		:  Tuple		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n"

A_		:  str		 =						"The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."

A_		:  List[str]		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
A_		:  Dict		 =						"The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."

A_		:  Any		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n"

A_		:  str		 =						"The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."

A_		:  Dict		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

A_		:  List[str]		 =						"The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."

A_		:  List[str]		 =						""

A_		:  Union[str, Any]		 =						"The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."

A_		:  Tuple		 =						"\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"

A_		:  Tuple		 =						"The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."



@pytest.mark.parametrize(
    """readme_md, expected_dict"""		,       [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   Optional[int]      )				->			Optional[Any]:
 '''simple docstring'''






 assert ReadMe.from_string(__magic_name__		,       __magic_name__      ).to_dict() == expected_dict



@pytest.mark.parametrize(
    """readme_md, expected_error"""		,       [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   int		,       __magic_name__  :   List[Any]      )				->			Any:
 '''simple docstring'''






 with pytest.raises(__magic_name__		,       match=re.escape(expected_error.format(path="""root"""      )      )      ):
  snake_case__     :					Optional[Any]    =  ReadMe.from_string(__magic_name__		,       __magic_name__      )
  readme.validate()



@pytest.mark.parametrize(
    """readme_md, expected_error"""		,       [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   Union[str, Any]		,       __magic_name__  :   Optional[Any]      )				->			Optional[int]:
 '''simple docstring'''






 with pytest.raises(__magic_name__		,       match=re.escape(expected_error.format(path="""root"""      )      )      ):
  ReadMe.from_string(__magic_name__		,       __magic_name__      )



@pytest.mark.parametrize(
    """readme_md,"""		,       [
        (README_MULTIPLE_SAME_HEADING_1),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]      )				->			str:
 '''simple docstring'''






 ReadMe.from_string(__magic_name__		,       __magic_name__		,       suppress_parsing_errors=__magic_name__      )



@pytest.mark.parametrize(
    """readme_md, expected_dict"""		,       [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]		,       __magic_name__  :   Optional[Any]      )				->			int:
 '''simple docstring'''






 with tempfile.TemporaryDirectory() as tmp_dir:
  snake_case__     :					Dict    =  Path(__magic_name__      ) / """README.md"""
  with open(__magic_name__		,       """w+"""      ) as readme_file:
   readme_file.write(__magic_name__      )
  snake_case__     :					Optional[Any]    =  ReadMe.from_readme(__magic_name__		,       __magic_name__      ).to_dict()
  assert out["name"] == path
  assert out["text"] == ""
  assert out["is_empty_text"]
  assert out["subsections"] == expected_dict["subsections"]



@pytest.mark.parametrize(
    """readme_md, expected_error"""		,       [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   Any      )				->			Tuple:
 '''simple docstring'''






 with tempfile.TemporaryDirectory() as tmp_dir:
  snake_case__     :					Any    =  Path(__magic_name__      ) / """README.md"""
  with open(__magic_name__		,       """w+"""      ) as readme_file:
   readme_file.write(__magic_name__      )
  snake_case__     :					Tuple    =  expected_error.format(path=__magic_name__      )
  with pytest.raises(__magic_name__		,       match=re.escape(__magic_name__      )      ):
   snake_case__     :					Tuple    =  ReadMe.from_readme(__magic_name__		,       __magic_name__      )
   readme.validate()



@pytest.mark.parametrize(
    """readme_md, expected_error"""		,       [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   Tuple      )				->			int:
 '''simple docstring'''






 with tempfile.TemporaryDirectory() as tmp_dir:
  snake_case__     :					Any    =  Path(__magic_name__      ) / """README.md"""
  with open(__magic_name__		,       """w+"""      ) as readme_file:
   readme_file.write(__magic_name__      )
  snake_case__     :					List[str]    =  expected_error.format(path=__magic_name__      )
  with pytest.raises(__magic_name__		,       match=re.escape(__magic_name__      )      ):
   ReadMe.from_readme(__magic_name__		,       __magic_name__      )





@pytest.mark.parametrize(
    """readme_md,"""		,       [
        (README_MULTIPLE_SAME_HEADING_1),
    ]		,       )
def        UpperCamelCase__		(						__magic_name__  :   List[str]      )				->			List[Any]:
 '''simple docstring'''






 with tempfile.TemporaryDirectory() as tmp_dir:
  snake_case__     :					int    =  Path(__magic_name__      ) / """README.md"""
  with open(__magic_name__		,       """w+"""      ) as readme_file:
   readme_file.write(__magic_name__      )
  ReadMe.from_readme(__magic_name__		,       __magic_name__		,       suppress_parsing_errors=__magic_name__      )










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__     :     Optional[Any]		    =       1E-4

if is_torch_available():
				import torch

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



@require_torch
class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=13      ,     SCREAMING_SNAKE_CASE=7      ,     SCREAMING_SNAKE_CASE=14      ,     SCREAMING_SNAKE_CASE=10      ,     SCREAMING_SNAKE_CASE=19      ,     SCREAMING_SNAKE_CASE=5      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=[1, 2, 3, 4, 5]      ,     SCREAMING_SNAKE_CASE=25      ,     SCREAMING_SNAKE_CASE=5      ,     )  ->					Union[str, Any]:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_								=						context_length
				SCREAMING_SNAKE_CASE_								=						prediction_length + label_length
				SCREAMING_SNAKE_CASE_								=						label_length

				SCREAMING_SNAKE_CASE_								=						moving_average
				SCREAMING_SNAKE_CASE_								=						autocorrelation_factor





	def 						A_(							self      )  ->					List[str]:




				"""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      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



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

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

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

				SCREAMING_SNAKE_CASE_								=						{
				    '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      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_config()
				SCREAMING_SNAKE_CASE_								=						self.prepare_autoformer_inputs_dict(SCREAMING_SNAKE_CASE      )
				return config, inputs_dict





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




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerModel(config=SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      ).eval()
				SCREAMING_SNAKE_CASE_								=						model(**SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						outputs.encoder_last_hidden_state
				SCREAMING_SNAKE_CASE_								=						outputs.last_hidden_state

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

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						model.create_network_inputs(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]      )

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

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

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

				SCREAMING_SNAKE_CASE_								=						decoder(
				    trend=SCREAMING_SNAKE_CASE      ,     inputs_embeds=SCREAMING_SNAKE_CASE      ,     encoder_hidden_states=SCREAMING_SNAKE_CASE      ,     )[0]

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




@require_torch
class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
	A    							= (AutoformerForPrediction,) if is_torch_available() else ()
	A    							= {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
	A    							= False
	A    							= False
	A    							= False
	A    							= False
	A    							= False
	A    							= False





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerModelTester(self      )
				SCREAMING_SNAKE_CASE_								=						ConfigTester(self      ,     config_class=SCREAMING_SNAKE_CASE      ,     has_text_modality=SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				self.config_tester.run_common_tests()





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_								=						model_class(SCREAMING_SNAKE_CASE      )

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





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE      )





	@unittest.skip(reason='Model has no tokens embeddings'      )
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_common()

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

							SCREAMING_SNAKE_CASE_								=						[
							    '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(SCREAMING_SNAKE_CASE      )]      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'seq_length'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'decoder_seq_length'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'encoder_seq_length'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'd_model'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'num_attention_heads'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						d_model // num_attention_heads

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

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

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

							SCREAMING_SNAKE_CASE_								=						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(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

							# decoder attentions
							SCREAMING_SNAKE_CASE_								=						outputs.decoder_attentions
							self.assertIsInstance(SCREAMING_SNAKE_CASE      ,     (list, tuple)      )
							self.assertEqual(len(SCREAMING_SNAKE_CASE      )      ,     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
							SCREAMING_SNAKE_CASE_								=						outputs.cross_attentions
							self.assertIsInstance(SCREAMING_SNAKE_CASE      ,     (list, tuple)      )
							self.assertEqual(len(SCREAMING_SNAKE_CASE      )      ,     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
				SCREAMING_SNAKE_CASE_								=						True
				SCREAMING_SNAKE_CASE_								=						True
				SCREAMING_SNAKE_CASE_								=						model_class(SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )      )

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

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

				self.assertEqual(len(SCREAMING_SNAKE_CASE      )      ,     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      )  ->					Any:




				"""simple docstring"""



				super().test_retain_grad_hidden_states_attentions()




def lowercase    (  SCREAMING_SNAKE_CASE="train-batch.pt" )			->							str:


			'''simple docstring'''







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



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





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						prepare_batch()

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						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]

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

				SCREAMING_SNAKE_CASE_								=						torch.tensor(
				    [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]      ,     device=SCREAMING_SNAKE_CASE      )
				self.assertTrue(torch.allclose(output[0, :3, :3]      ,     SCREAMING_SNAKE_CASE      ,     atol=SCREAMING_SNAKE_CASE      )      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_								=						torch.tensor(
				    [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]      ,     device=SCREAMING_SNAKE_CASE      )
				self.assertTrue(torch.allclose(output[0, :3, :3]      ,     SCREAMING_SNAKE_CASE      ,     atol=SCREAMING_SNAKE_CASE      )      )





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_								=						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=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						outputs.sequences.mean(dim=1      )
				self.assertTrue(torch.allclose(mean_prediction[0, -3:]      ,     SCREAMING_SNAKE_CASE      ,     rtol=1e-1      )      )
