import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


A_:   Union[str, Any]   =      '''<<<<<<< This should probably be modified because it mentions: '''

A_:   Dict   =      '''=======
>>>>>>>
'''

A_:   Optional[int]   =      [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

A_:   Dict   =      [
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'''tfds\.core''', R'''datasets'''),
    (R'''tf\.io\.gfile\.GFile''', R'''open'''),
    (R'''tf\.([\w\d]+)''', R'''datasets.Value(\'\1\')'''),
    (R'''tfds\.features\.Text\(\)''', R'''datasets.Value(\'string\')'''),
    (R'''tfds\.features\.Text\(''', R'''datasets.Value(\'string\'),'''),
    (R'''features\s*=\s*tfds.features.FeaturesDict\(''', R'''features=datasets.Features('''),
    (R'''tfds\.features\.FeaturesDict\(''', R'''dict('''),
    (R'''The TensorFlow Datasets Authors''', R'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (R'''tfds\.''', R'''datasets.'''),
    (R'''dl_manager\.manual_dir''', R'''self.config.data_dir'''),
    (R'''self\.builder_config''', R'''self.config'''),
]


def        __lowerCAmelCase      (	_A       ):



							"""simple docstring"""


							return ConvertCommand(args.tfds_path			,args.datasets_directory       )







class _lowercase       (							a_    ):





							"""simple docstring"""




							@staticmethod
							def      _UpperCAmelCase							(     UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						parser.add_parser(
														    """convert"""  , help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""  , )
														train_parser.add_argument(
														    """--tfds_path"""  , type=lowercase_  , required=lowercase_  , help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""  , )
														train_parser.add_argument(
														    """--datasets_directory"""  , type=lowercase_  , required=lowercase_  , help="""Path to the HuggingFace Datasets folder."""	)
														train_parser.set_defaults(func=lowercase_	)



							def __init__(     self  , UpperCAmelCase  , UpperCAmelCase  , *UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						get_logger("""datasets-cli/converting"""	)

														_lowercase					=						tfds_path
														_lowercase					=						datasets_directory



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														if os.path.isdir(self._tfds_path	):
																					_lowercase					=						os.path.abspath(self._tfds_path	)
														elif os.path.isfile(self._tfds_path	):
																					_lowercase					=						os.path.dirname(self._tfds_path	)
														else:
																					raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""	)

														_lowercase					=						os.path.abspath(self._datasets_directory	)

														self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''	)

														_lowercase					=						[]
														_lowercase					=						[]
														_lowercase					=						{}

														if os.path.isdir(self._tfds_path	):
																					_lowercase					=						os.listdir(lowercase_	)
														else:
																					_lowercase					=						[os.path.basename(self._tfds_path	)]

														for f_name in file_names:
																					self._logger.info(F'''Looking at file {f_name}'''	)
																					_lowercase					=						os.path.join(lowercase_  , lowercase_	)
																					_lowercase					=						os.path.join(lowercase_  , lowercase_	)

																					if not os.path.isfile(lowercase_	) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																												self._logger.info("""Skipping file"""	)
																												continue

																					with open(lowercase_  , encoding="""utf-8"""	) as f:
																												_lowercase					=						f.readlines()

																					_lowercase					=						[]
																					_lowercase					=						False
																					_lowercase					=						False
																					_lowercase					=						[]
																					for line in lines:
																												_lowercase					=						line

																												# Convert imports
																												if "import tensorflow.compat.v2 as tf" in out_line:
																																			continue
																												elif "@tfds.core" in out_line:
																																			continue
																												elif "builder=self" in out_line:
																																			continue
																												elif "import tensorflow_datasets.public_api as tfds" in out_line:
																																			_lowercase					=						'''import datasets\n'''
																												elif "import tensorflow" in out_line:
																																			# order is important here
																																			_lowercase					=						''''''
																																			continue
																												elif "from absl import logging" in out_line:
																																			_lowercase					=						'''from datasets import logging\n'''
																												elif "getLogger" in out_line:
																																			_lowercase					=						out_line.replace("""getLogger"""  , """get_logger"""	)
																												elif any(expression in out_line for expression in TO_HIGHLIGHT	):
																																			_lowercase					=						True
																																			_lowercase					=						list(filter(lambda UpperCAmelCase	: e in out_line  , lowercase_	)	)
																																			out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowercase_	) + """\n"""	)
																																			out_lines.append(lowercase_	)
																																			out_lines.append(lowercase_	)
																																			continue
																												else:
																																			for pattern, replacement in TO_CONVERT:
																																										_lowercase					=						re.sub(lowercase_  , lowercase_  , lowercase_	)

                # Take care of saving utilities (to later move them together with main script)
																												if "tensorflow_datasets" in out_line:
																																			_lowercase					=						re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""  , lowercase_	)
																																			tfds_imports.extend(imp.strip() for imp in match.group(1	).split(""","""	)	)
																																			_lowercase					=						'''from . import ''' + match.group(1	)

																												# Check we have not forget anything
																												if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																																			raise ValueError(F'''Error converting {out_line.strip()}'''	)

																												if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																																			_lowercase					=						True
																												out_lines.append(lowercase_	)

																					if is_builder or "wmt" in f_name:
																												# We create a new directory for each dataset
																												_lowercase					=						f_name.replace(""".py"""  , """"""	)
																												_lowercase					=						os.path.join(lowercase_  , lowercase_	)
																												_lowercase					=						os.path.join(lowercase_  , lowercase_	)
																												os.makedirs(lowercase_  , exist_ok=lowercase_	)
																												self._logger.info(F'''Adding directory {output_dir}'''	)
																												imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}	)
																					else:
																												# Utilities will be moved at the end
																												utils_files.append(lowercase_	)

																					if needs_manual_update:
																												with_manual_update.append(lowercase_	)

																					with open(lowercase_  , """w"""  , encoding="""utf-8"""	) as f:
																												f.writelines(lowercase_	)
																					self._logger.info(F'''Converted in {output_file}'''	)

														for utils_file in utils_files:
																					try:
																												_lowercase					=						os.path.basename(lowercase_	)
																												_lowercase					=						imports_to_builder_map[f_name.replace(""".py"""  , """"""	)]
																												self._logger.info(F'''Moving {dest_folder} to {utils_file}'''	)
																												shutil.copy(lowercase_  , lowercase_	)
																					except KeyError:
																												self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.'''	)

														if with_manual_update:
																					for file_path in with_manual_update:
																												self._logger.warning(
																												    F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''	)





"""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    : 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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							str  ,lowercase_     :							Optional[Any]  ,lowercase_     :							Optional[Any]=1_6  ,lowercase_     :							int=1_3  ,lowercase_     :							Optional[int]=7  ,lowercase_     :							int=1_4  ,lowercase_     :							str=1_0  ,lowercase_     :							List[Any]=1_9  ,lowercase_     :							Any=5  ,lowercase_     :							Any=4  ,lowercase_     :							List[str]=True  ,lowercase_     :							Union[str, Any]=1_6  ,lowercase_     :							Tuple=2  ,lowercase_     :							str=4  ,lowercase_     :							Tuple=4  ,lowercase_     :							int="gelu"  ,lowercase_     :							Any=0.1  ,lowercase_     :							int=0.1  ,lowercase_     :							Optional[int]=[1, 2, 3, 4, 5]  ,lowercase_     :							List[Any]=2_5  ,lowercase_     :							Union[str, Any]=5  ,):
						lowerCAmelCase__      :		List[str]    							=      d_model
						lowerCAmelCase__      :		Optional[Any]    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		Any    							=      prediction_length
						lowerCAmelCase__      :		str    							=      context_length
						lowerCAmelCase__      :		int    							=      cardinality
						lowerCAmelCase__      :		Dict    							=      num_time_features
						lowerCAmelCase__      :		str    							=      lags_sequence
						lowerCAmelCase__      :		int    							=      embedding_dimension
						lowerCAmelCase__      :		Dict    							=      is_training
						lowerCAmelCase__      :		Optional[Any]    							=      hidden_size
						lowerCAmelCase__      :		Optional[int]    							=      num_hidden_layers
						lowerCAmelCase__      :		List[str]    							=      num_attention_heads
						lowerCAmelCase__      :		int    							=      intermediate_size
						lowerCAmelCase__      :		List[str]    							=      hidden_act
						lowerCAmelCase__      :		Dict    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Optional[int]    							=      attention_probs_dropout_prob

						lowerCAmelCase__      :		Tuple    							=      context_length
						lowerCAmelCase__      :		Optional[Any]    							=      prediction_length + label_length
						lowerCAmelCase__      :		Union[str, Any]    							=      label_length

						lowerCAmelCase__      :		Optional[int]    							=      moving_average
						lowerCAmelCase__      :		Dict    							=      autocorrelation_factor







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						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     :							Union[str, Any]  ,lowercase_     :							str						):
						lowerCAmelCase__      :		str    							=      config.context_length + max(config.lags_sequence						)

						lowerCAmelCase__      :		Optional[int]    							=      ids_tensor([self.batch_size, 1]  ,config.cardinality[0]						)
						lowerCAmelCase__      :		int    							=      floats_tensor([self.batch_size, _past_length, config.num_time_features]						)
						lowerCAmelCase__      :		Any    							=      floats_tensor([self.batch_size, _past_length]						)
						lowerCAmelCase__      :		List[str]    							=      floats_tensor([self.batch_size, _past_length]						) > 0.5

						# decoder inputs
						lowerCAmelCase__      :		List[str]    							=      floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]						)
						lowerCAmelCase__      :		Tuple    							=      floats_tensor([self.batch_size, config.prediction_length]						)

						lowerCAmelCase__      :		Optional[int]    							=      {
						    '''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]						):
						lowerCAmelCase__      :		Dict    							=      self.get_config()
						lowerCAmelCase__      :		Dict    							=      self.prepare_autoformer_inputs_dict(lowercase_						)
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      self.prepare_config_and_inputs()
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Optional[int]  ,lowercase_     :							Union[str, Any]						):
						lowerCAmelCase__      :		Any    							=      AutoformerModel(config=lowercase_						).to(lowercase_						).eval()
						lowerCAmelCase__      :		Tuple    							=      model(**lowercase_						)

						lowerCAmelCase__      :		Any    							=      outputs.encoder_last_hidden_state
						lowerCAmelCase__      :		str    							=      outputs.last_hidden_state

						with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase__      :		List[Any]    							=      model.get_encoder()
							encoder.save_pretrained(lowercase_						)
							lowerCAmelCase__      :		Union[str, Any]    							=      AutoformerEncoder.from_pretrained(lowercase_						).to(lowercase_						)

						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      model.create_network_inputs(**lowercase_						)
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]						)

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

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

						lowerCAmelCase__      :		List[Any]    							=      torch.cat(
						    (
						        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)  ,dim=1						),
						        feature[:, config.context_length - config.label_length :, ...],
						    )  ,dim=-1  ,)
						lowerCAmelCase__      :		Any    							=      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:
							lowerCAmelCase__      :		Optional[int]    							=      model.get_decoder()
							decoder.save_pretrained(lowercase_						)
							lowerCAmelCase__      :		Tuple    							=      AutoformerDecoder.from_pretrained(lowercase_						).to(lowercase_						)

						lowerCAmelCase__      :		Union[str, Any]    							=      decoder(
						    trend=lowercase_  ,inputs_embeds=lowercase_  ,encoder_hidden_states=lowercase_  ,)[0]

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







@require_torch
class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
					lowercase__												=			(AutoformerForPrediction,) if is_torch_available() else ()
					lowercase__												=			{"feature-extraction": AutoformerModel} if is_torch_available() else {}
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False







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







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		Optional[Any]    							=      model_class(lowercase_						)

							with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(lowercase_						)
								lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      model_class.from_pretrained(lowercase_  ,output_loading_info=lowercase_						)
							self.assertEqual(info['''missing_keys''']  ,[]						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Tuple    							=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*lowercase_						)







					@unittest.skip(reason='''Model has no tokens embeddings'''						)
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						pass







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







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase__      :		Optional[int]    							=      [
							    '''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(lowercase_						)]  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__      :		List[str]    							=      True

						lowerCAmelCase__      :		int    							=      getattr(self.model_tester  ,'''seq_length'''  ,lowercase_						)
						lowerCAmelCase__      :		Union[str, Any]    							=      getattr(self.model_tester  ,'''decoder_seq_length'''  ,lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      getattr(self.model_tester  ,'''encoder_seq_length'''  ,lowercase_						)
						lowerCAmelCase__      :		List[Any]    							=      getattr(self.model_tester  ,'''d_model'''  ,lowercase_						)
						lowerCAmelCase__      :		Union[str, Any]    							=      getattr(self.model_tester  ,'''num_attention_heads'''  ,lowercase_						)
						lowerCAmelCase__      :		str    							=      d_model // num_attention_heads

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

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							lowerCAmelCase__      :		List[str]    							=      True
							lowerCAmelCase__      :		Union[str, Any]    							=      model_class(lowercase_						)
							model.to(lowercase_						)
							model.eval()
							with torch.no_grad():
								lowerCAmelCase__      :		Dict    							=      model(**self._prepare_for_class(lowercase_  ,lowercase_						)						)
							lowerCAmelCase__      :		int    							=      outputs.encoder_attentions
							self.assertEqual(len(lowercase_						)  ,self.model_tester.num_hidden_layers						)

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

							lowerCAmelCase__      :		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(lowercase_  ,lowercase_						)

							# decoder attentions
							lowerCAmelCase__      :		str    							=      outputs.decoder_attentions
							self.assertIsInstance(lowercase_  ,(list, tuple)						)
							self.assertEqual(len(lowercase_						)  ,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
							lowerCAmelCase__      :		Any    							=      outputs.cross_attentions
							self.assertIsInstance(lowercase_  ,(list, tuple)						)
							self.assertEqual(len(lowercase_						)  ,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
						lowerCAmelCase__      :		str    							=      True
						lowerCAmelCase__      :		Tuple    							=      True
						lowerCAmelCase__      :		Optional[Any]    							=      model_class(lowercase_						)
						model.to(lowercase_						)
						model.eval()
						with torch.no_grad():
							lowerCAmelCase__      :		Optional[Any]    							=      model(**self._prepare_for_class(lowercase_  ,lowercase_						)						)

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

						lowerCAmelCase__      :		Optional[Any]    							=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

						self.assertEqual(len(lowercase_						)  ,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     :							str						):
						super().test_retain_grad_hidden_states_attentions()

def     __SCREAMING_SNAKE_CASE    ( A_="train-batch.pt"					):
	lowerCAmelCase__      :		Any    							=      hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''		,	filename=A_		,	repo_type='''dataset'''					)
	lowerCAmelCase__      :		Union[str, Any]    							=      torch.load(A_		,	map_location=A_					)
	return batch







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






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		List[Any]    							=      AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		int    							=      prepare_batch()

						with torch.no_grad():
							lowerCAmelCase__      :		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''']  ,future_values=batch['''future_values''']  ,future_time_features=batch['''future_time_features''']  ,)[0]

						lowerCAmelCase__      :		Dict    							=      torch.Size(
						    (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)						)
						self.assertEqual(output.shape  ,lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      torch.tensor(
						    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]  ,device=lowercase_						)
						self.assertTrue(torch.allclose(output[0, :3, :3]  ,lowercase_  ,atol=lowercase_						)						)







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      prepare_batch('''val-batch.pt'''						)
						with torch.no_grad():
							lowerCAmelCase__      :		str    							=      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
						lowerCAmelCase__      :		Any    							=      torch.Size((6_4, model.config.context_length, model.config.d_model)						)
						self.assertEqual(output.shape  ,lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      torch.tensor(
						    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]  ,device=lowercase_						)
						self.assertTrue(torch.allclose(output[0, :3, :3]  ,lowercase_  ,atol=lowercase_						)						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''						).to(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      prepare_batch('''val-batch.pt'''						)
						with torch.no_grad():
							lowerCAmelCase__      :		int    							=      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''']  ,)
						lowerCAmelCase__      :		List[Any]    							=      torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)						)
						self.assertEqual(outputs.sequences.shape  ,lowercase_						)

						lowerCAmelCase__      :		Union[str, Any]    							=      torch.tensor([3130.6763, 4056.5293, 7053.0786]  ,device=lowercase_						)
						lowerCAmelCase__      :		Dict    							=      outputs.sequences.mean(dim=1						)
						self.assertTrue(torch.allclose(mean_prediction[0, -3:]  ,lowercase_  ,rtol=1E-1						)						)




