





"""simple docstring"""


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__magic_name__       :					Optional[int]						=			"""\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

__magic_name__       :					Optional[Any]						=			"""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

__magic_name__       :					int						=			"""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,     _KWARGS_DESCRIPTION	)
class 							lowercase__			(   datasets.Metric	):

			"""simple docstring"""







			def   _a      (      self    ):






									'''simple docstring'''







									return datasets.MetricInfo(
									    description=_DESCRIPTION						,   citation=_CITATION						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
									        {
									            """predictions""": datasets.Sequence(datasets.Value("""string"""						,   id="""token"""    )						,   id="""sequence"""    ),
									            """references""": datasets.Sequence(
									                datasets.Sequence(datasets.Value("""string"""						,   id="""token"""    )						,   id="""sequence"""    )						,   id="""references"""    ),
									        }    )						,   )







			def   _a      (      self						,   _A						,   _A						,   _A = 1						,   _A = 4						,   ):






									'''simple docstring'''







									return {
									    "google_bleu": gleu_score.corpus_gleu(
									        list_of_references=_A						,   hypotheses=_A						,   min_len=_A						,   max_len=_A    )
									}










"""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


__magic_name__       :					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 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A=1_6						,   _A=1_3						,   _A=7						,   _A=1_4						,   _A=1_0						,   _A=1_9						,   _A=5						,   _A=4						,   _A=True						,   _A=1_6						,   _A=2						,   _A=4						,   _A=4						,   _A="gelu"						,   _A=0.1						,   _A=0.1						,   _A=[1, 2, 3, 4, 5]						,   _A=2_5						,   _A=5						,   ):






									'''simple docstring'''







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

									UpperCamelCase				:		Union[str, Any]				   =			context_length
									UpperCamelCase				:		str				   =			prediction_length + label_length
									UpperCamelCase				:		Dict				   =			label_length

									UpperCamelCase				:		Any				   =			moving_average
									UpperCamelCase				:		int				   =			autocorrelation_factor







			def   _a      (      self    ):






									'''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    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			config.context_length + max(config.lags_sequence    )

									UpperCamelCase				:		Any				   =			ids_tensor([self.batch_size, 1]						,   config.cardinality[0]    )
									UpperCamelCase				:		Any				   =			floats_tensor([self.batch_size, _past_length, config.num_time_features]    )
									UpperCamelCase				:		Union[str, Any]				   =			floats_tensor([self.batch_size, _past_length]    )
									UpperCamelCase				:		str				   =			floats_tensor([self.batch_size, _past_length]    ) > 0.5

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

									UpperCamelCase				:		Dict				   =			{
									    """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    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.get_config()
									UpperCamelCase				:		int				   =			self.prepare_autoformer_inputs_dict(_A    )
									return config, inputs_dict







			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			AutoformerModel(config=_A    ).to(_A    ).eval()
									UpperCamelCase				:		List[str]				   =			model(**_A    )

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

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

									UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			model.create_network_inputs(**_A    )
									UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]    )

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

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

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

									with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCamelCase				:		Any				   =			model.get_decoder()
															decoder.save_pretrained(_A    )
															UpperCamelCase				:		Optional[Any]				   =			AutoformerDecoder.from_pretrained(_A    ).to(_A    )

									UpperCamelCase				:		str				   =			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 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Tuple										=  (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
			__lowerCAmelCase     :				str										=  (AutoformerForPrediction,) if is_torch_available() else ()
			__lowerCAmelCase     :				Dict										=  {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
			__lowerCAmelCase     :				List[Any]										=  False
			__lowerCAmelCase     :				Any										=  False
			__lowerCAmelCase     :				List[Any]										=  False
			__lowerCAmelCase     :				Optional[int]										=  False
			__lowerCAmelCase     :				Optional[Any]										=  False
			__lowerCAmelCase     :				Optional[int]										=  False







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			AutoformerModelTester(self    )
									UpperCamelCase				:		int				   =			ConfigTester(self						,   config_class=_A						,   has_text_modality=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.config_tester.run_common_tests()







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															UpperCamelCase				:		Optional[int]				   =			model_class(_A    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			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    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			self.model_tester.prepare_config_and_inputs_for_common()

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

															UpperCamelCase				:		Optional[Any]				   =			[
															    """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    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase				:		Dict				   =			True

									UpperCamelCase				:		Tuple				   =			getattr(self.model_tester						,   """seq_length"""						,   _A    )
									UpperCamelCase				:		Dict				   =			getattr(self.model_tester						,   """decoder_seq_length"""						,   _A    )
									UpperCamelCase				:		Optional[Any]				   =			getattr(self.model_tester						,   """encoder_seq_length"""						,   _A    )
									UpperCamelCase				:		Optional[int]				   =			getattr(self.model_tester						,   """d_model"""						,   _A    )
									UpperCamelCase				:		Optional[Any]				   =			getattr(self.model_tester						,   """num_attention_heads"""						,   _A    )
									UpperCamelCase				:		Tuple				   =			d_model // num_attention_heads

									for model_class in self.all_model_classes:
															UpperCamelCase				:		List[Any]				   =			True
															UpperCamelCase				:		Any				   =			False
															UpperCamelCase				:		List[str]				   =			True
															UpperCamelCase				:		List[str]				   =			model_class(_A    )
															model.to(_A    )
															model.eval()
															with torch.no_grad():
																					UpperCamelCase				:		Tuple				   =			model(**self._prepare_for_class(_A						,   _A    )    )
															UpperCamelCase				:		Optional[Any]				   =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
															self.assertEqual(len(_A    )						,   self.model_tester.num_hidden_layers    )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															UpperCamelCase				:		Tuple				   =			True
															UpperCamelCase				:		Optional[int]				   =			model_class(_A    )
															model.to(_A    )
															model.eval()
															with torch.no_grad():
																					UpperCamelCase				:		List[Any]				   =			model(**self._prepare_for_class(_A						,   _A    )    )
															UpperCamelCase				:		List[str]				   =			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]						,   )
															UpperCamelCase				:		List[str]				   =			len(_A    )

															UpperCamelCase				:		List[str]				   =			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
															UpperCamelCase				:		Optional[int]				   =			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
															UpperCamelCase				:		int				   =			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
									UpperCamelCase				:		Any				   =			True
									UpperCamelCase				:		Dict				   =			True
									UpperCamelCase				:		int				   =			model_class(_A    )
									model.to(_A    )
									model.eval()
									with torch.no_grad():
															UpperCamelCase				:		int				   =			model(**self._prepare_for_class(_A						,   _A    )    )

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

									UpperCamelCase				:		str				   =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

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







			@is_flaky()
			def   _a      (      self    ):






									'''simple docstring'''







									super().test_retain_grad_hidden_states_attentions()


def      UpperCamelCase							(SCREAMING_SNAKE_CASE="train-batch.pt"     ):
						UpperCamelCase				:		List[str]				   =			hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""							,     filename=SCREAMING_SNAKE_CASE							,     repo_type="""dataset"""     )
						UpperCamelCase				:		Optional[int]				   =			torch.load(SCREAMING_SNAKE_CASE							,     map_location=SCREAMING_SNAKE_CASE     )
						return batch






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

			"""simple docstring"""







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(_A    )
									UpperCamelCase				:		List[str]				   =			prepare_batch()

									with torch.no_grad():
															UpperCamelCase				:		Dict				   =			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				:		Optional[int]				   =			torch.Size(
									    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)    )
									self.assertEqual(output.shape						,   _A    )

									UpperCamelCase				:		Optional[int]				   =			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=_A    )
									self.assertTrue(torch.allclose(output[0, :3, :3]						,   _A						,   atol=_A    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(_A    )
									UpperCamelCase				:		Dict				   =			prepare_batch("""val-batch.pt"""    )
									with torch.no_grad():
															UpperCamelCase				:		Optional[Any]				   =			model(
															    past_values=batch["""past_values"""]						,   past_time_features=batch["""past_time_features"""]						,   past_observed_mask=batch["""past_observed_mask"""]						,   static_categorical_features=batch["""static_categorical_features"""]						,   ).encoder_last_hidden_state
									UpperCamelCase				:		Dict				   =			torch.Size((6_4, model.config.context_length, model.config.d_model)    )
									self.assertEqual(output.shape						,   _A    )

									UpperCamelCase				:		Any				   =			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=_A    )
									self.assertTrue(torch.allclose(output[0, :3, :3]						,   _A						,   atol=_A    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""    ).to(_A    )
									UpperCamelCase				:		List[Any]				   =			prepare_batch("""val-batch.pt"""    )
									with torch.no_grad():
															UpperCamelCase				:		Optional[Any]				   =			model.generate(
															    static_categorical_features=batch["""static_categorical_features"""]						,   past_time_features=batch["""past_time_features"""]						,   past_values=batch["""past_values"""]						,   future_time_features=batch["""future_time_features"""]						,   past_observed_mask=batch["""past_observed_mask"""]						,   )
									UpperCamelCase				:		Tuple				   =			torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)    )
									self.assertEqual(outputs.sequences.shape						,   _A    )

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



