


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




           """simple docstring"""

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

           _lowercase       :							Union[str, Any]      		=		context_length
           _lowercase       :							int      		=		prediction_length + label_length
           _lowercase       :							Any      		=		label_length

           _lowercase       :							str      		=		moving_average
           _lowercase       :							int      		=		autocorrelation_factor
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """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 			UpperCamelCase     (					self,      lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		config.context_length + max(config.lags_sequence)

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

           # decoder inputs
           _lowercase       :							str      		=		floats_tensor([self.batch_size, config.prediction_length, config.num_time_features])
           _lowercase       :							Dict      		=		floats_tensor([self.batch_size, config.prediction_length])

           _lowercase       :							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 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.get_config()
           _lowercase       :							List[str]      		=		self.prepare_autoformer_inputs_dict(_SCREAMING_SNAKE_CASE)
           return config, inputs_dict
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							int      		=		self.prepare_config_and_inputs()
           return config, inputs_dict




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		AutoformerModel(config=_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE).eval()
           _lowercase       :							List[str]      		=		model(**_SCREAMING_SNAKE_CASE)

           _lowercase       :							List[str]      		=		outputs.encoder_last_hidden_state
           _lowercase       :							Optional[Any]      		=		outputs.last_hidden_state

           with tempfile.TemporaryDirectory() as tmpdirname:
               _lowercase       :							Any      		=		model.get_encoder()
               encoder.save_pretrained(_SCREAMING_SNAKE_CASE)
               _lowercase       :							Optional[int]      		=		AutoformerEncoder.from_pretrained(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)

           _lowercase       :							Union[str, Any]      		=		model.create_network_inputs(**_SCREAMING_SNAKE_CASE)
           _lowercase       :							Tuple      		=		model.decomposition_layer(transformer_inputs[:, : config.context_length, ...])

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

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

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

           _lowercase       :							Dict      		=		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 _lowerCamelCase(						UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase   ):
       lowercase_ :	Any     =			(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
       lowercase_ :	Any     =			(AutoformerForPrediction,) if is_torch_available() else ()
       lowercase_ :	int     =			{'feature-extraction': AutoformerModel} if is_torch_available() else {}
       lowercase_ :	List[Any]     =			False
       lowercase_ :	Tuple     =			False
       lowercase_ :	Any     =			False
       lowercase_ :	Union[str, Any]     =			False
       lowercase_ :	Optional[Any]     =			False
       lowercase_ :	Union[str, Any]     =			False
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		AutoformerModelTester(self)
           _lowercase       :							Tuple      		=		ConfigTester(self,      config_class=_SCREAMING_SNAKE_CASE,      has_text_modality=_SCREAMING_SNAKE_CASE)
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               _lowercase       :							int      		=		model_class(_SCREAMING_SNAKE_CASE)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(_SCREAMING_SNAKE_CASE)
                   _lowercase       :							Union[str, Any]      		=		model_class.from_pretrained(_SCREAMING_SNAKE_CASE,      output_loading_info=_SCREAMING_SNAKE_CASE)
               self.assertEqual(info['missing_keys'],      [])
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		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 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		inspect.signature(getattr(_SCREAMING_SNAKE_CASE,      'forward'))
           # The main input is the name of the argument after `self`
           _lowercase       :							Any      		=		list(model_signature.parameters.keys())[1]
           self.assertEqual(AutoformerModel.main_input_name,      _SCREAMING_SNAKE_CASE)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							str      		=		self.model_tester.prepare_config_and_inputs_for_common()

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

               _lowercase       :							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(_SCREAMING_SNAKE_CASE)],      _SCREAMING_SNAKE_CASE)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase       :							List[Any]      		=		True

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

           for model_class in self.all_model_classes:
               _lowercase       :							Any      		=		True
               _lowercase       :							List[Any]      		=		False
               _lowercase       :							Union[str, Any]      		=		True
               _lowercase       :							List[Any]      		=		model_class(_SCREAMING_SNAKE_CASE)
               model.to(_SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   _lowercase       :							List[str]      		=		model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE))
               _lowercase       :							Optional[int]      		=		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"]
               _lowercase       :							str      		=		True
               _lowercase       :							Tuple      		=		model_class(_SCREAMING_SNAKE_CASE)
               model.to(_SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   _lowercase       :							Optional[int]      		=		model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE))
               _lowercase       :							Any      		=		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],      )
               _lowercase       :							Optional[Any]      		=		len(_SCREAMING_SNAKE_CASE)

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

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

           _lowercase       :							Tuple      		=		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 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           super().test_retain_grad_hidden_states_attentions()



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






@require_torch
@slow
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							str      		=		AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly').to(_SCREAMING_SNAKE_CASE)
           _lowercase       :							Union[str, Any]      		=		prepare_batch()

           with torch.no_grad():
               _lowercase       :							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]

           _lowercase       :							Union[str, Any]      		=		torch.Size(
               (64, model.config.prediction_length + model.config.label_length, model.config.feature_size))
           self.assertEqual(output.shape,      _SCREAMING_SNAKE_CASE)

           _lowercase       :							str      		=		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 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Any      		=		AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly').to(_SCREAMING_SNAKE_CASE)
           _lowercase       :							Dict      		=		prepare_batch('val-batch.pt')
           with torch.no_grad():
               _lowercase       :							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'],      ).encoder_last_hidden_state
           _lowercase       :							List[str]      		=		torch.Size((64, model.config.context_length, model.config.d_model))
           self.assertEqual(output.shape,      _SCREAMING_SNAKE_CASE)

           _lowercase       :							Union[str, Any]      		=		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 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							str      		=		AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly').to(_SCREAMING_SNAKE_CASE)
           _lowercase       :							Optional[Any]      		=		prepare_batch('val-batch.pt')
           with torch.no_grad():
               _lowercase       :							Union[str, 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'],      )
           _lowercase       :							List[Any]      		=		torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length))
           self.assertEqual(outputs.sequences.shape,      _SCREAMING_SNAKE_CASE)

           _lowercase       :							Tuple      		=		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)
           _lowercase       :							Dict      		=		outputs.sequences.mean(dim=1)
           self.assertTrue(torch.allclose(mean_prediction[0, -3:],      _SCREAMING_SNAKE_CASE,      rtol=1E-1))







"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def       _snake_case						(						*UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[Union[Dict, Any]] = None			,		UpperCamelCase						:		Tuple=True			,		UpperCamelCase						:		Optional[int]=2						):
 from .. import __version__

 UpperCAmelCase	:					Tuple	=	take_from
 UpperCAmelCase	:					Optional[Any]	=	()
 if not isinstance(args[0]			,		UpperCamelCase						):
  UpperCAmelCase	:					List[str]	=	(args,)

 for attribute, version_name, message in args:
  if version.parse(version.parse(UpperCamelCase						).base_version						) >= version.parse(UpperCamelCase						):
   raise ValueError(
       F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
       F" version {__version__} is >= {version_name}"						)

  UpperCAmelCase	:					Optional[int]	=	None
  if isinstance(UpperCamelCase			,		UpperCamelCase						) and attribute in deprecated_kwargs:
   values += (deprecated_kwargs.pop(UpperCamelCase						),)
   UpperCAmelCase	:					List[str]	=	F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
  elif hasattr(UpperCamelCase			,		UpperCamelCase						):
   values += (getattr(UpperCamelCase			,		UpperCamelCase						),)
   UpperCAmelCase	:					List[Any]	=	F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
  elif deprecated_kwargs is None:
   UpperCAmelCase	:					int	=	F"`{attribute}` is deprecated and will be removed in version {version_name}."

  if warning is not None:
   UpperCAmelCase	:					Optional[Any]	=	warning + """ """ if standard_warn else """"""
   warnings.warn(warning + message			,		UpperCamelCase			,		stacklevel=UpperCamelCase						)

 if isinstance(UpperCamelCase			,		UpperCamelCase						) and len(UpperCamelCase						) > 0:
  UpperCAmelCase	:					Optional[int]	=	inspect.getouterframes(inspect.currentframe()						)[1]
  UpperCAmelCase	:					Union[str, Any]	=	call_frame.filename
  UpperCAmelCase	:					List[Any]	=	call_frame.lineno
  UpperCAmelCase	:					List[str]	=	call_frame.function
  UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	next(iter(deprecated_kwargs.items()						)						)
  raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"						)

 if len(UpperCamelCase						) == 0:
  return
 elif len(UpperCamelCase						) == 1:
  return values[0]
 return values
