





'''simple docstring'''







import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    FocalNetBackbone,
			    FocalNetForImageClassification,
			    FocalNetForMaskedImageModeling,
			    FocalNetModel,
			)
			from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor
class 			_lowercase						:







					def __init__( self    ,   A__    ,   A__=13    ,   A__=32    ,   A__=2    ,   A__=3    ,   A__=16    ,   A__=[32, 64, 1_28]    ,   A__=[1, 2, 1]    ,   A__=[2, 2, 4]    ,   A__=2    ,   A__=2.0    ,   A__=True    ,   A__=0.0    ,   A__=0.0    ,   A__=0.1    ,   A__="gelu"    ,   A__=False    ,   A__=True    ,   A__=0.0_2    ,   A__=1e-5    ,   A__=True    ,   A__=None    ,   A__=True    ,   A__=10    ,   A__=8    ,   A__=["stage1", "stage2"]    ,   A__=[1, 2]    ,   ) ->	Union[str, Any]:
						snake_case				=			parent
						snake_case				=			batch_size
						snake_case				=			image_size
						snake_case				=			patch_size
						snake_case				=			num_channels
						snake_case				=			embed_dim
						snake_case				=			hidden_sizes
						snake_case				=			depths
						snake_case				=			num_heads
						snake_case				=			window_size
						snake_case				=			mlp_ratio
						snake_case				=			qkv_bias
						snake_case				=			hidden_dropout_prob
						snake_case				=			attention_probs_dropout_prob
						snake_case				=			drop_path_rate
						snake_case				=			hidden_act
						snake_case				=			use_absolute_embeddings
						snake_case				=			patch_norm
						snake_case				=			layer_norm_eps
						snake_case				=			initializer_range
						snake_case				=			is_training
						snake_case				=			scope
						snake_case				=			use_labels
						snake_case				=			type_sequence_label_size
						snake_case				=			encoder_stride
						snake_case				=			out_features
						snake_case				=			out_indices







					def     UpperCamelCase			( self    ) ->	Optional[int]:
						snake_case				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

						snake_case				=			None
						if self.use_labels:
							snake_case				=			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size    )

						snake_case				=			self.get_config()

						return config, pixel_values, labels







					def     UpperCamelCase			( self    ) ->	Optional[Any]:
						return FocalNetConfig(
						    image_size=self.image_size    ,   patch_size=self.patch_size    ,   num_channels=self.num_channels    ,   embed_dim=self.embed_dim    ,   hidden_sizes=self.hidden_sizes    ,   depths=self.depths    ,   num_heads=self.num_heads    ,   window_size=self.window_size    ,   mlp_ratio=self.mlp_ratio    ,   qkv_bias=self.qkv_bias    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   drop_path_rate=self.drop_path_rate    ,   hidden_act=self.hidden_act    ,   use_absolute_embeddings=self.use_absolute_embeddings    ,   path_norm=self.patch_norm    ,   layer_norm_eps=self.layer_norm_eps    ,   initializer_range=self.initializer_range    ,   encoder_stride=self.encoder_stride    ,   out_features=self.out_features    ,   out_indices=self.out_indices    ,   )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	str:
						snake_case				=			FocalNetModel(config=A__    )
						model.to(A__    )
						model.eval()
						snake_case				=			model(A__    )

						snake_case				=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
						snake_case				=			int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

						self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, expected_seq_len, expected_dim)    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
						snake_case				=			FocalNetBackbone(config=A__    )
						model.to(A__    )
						model.eval()
						snake_case				=			model(A__    )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps    )    ,   len(config.out_features    )    )
						self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,   [self.batch_size, self.image_size, 8, 8]    )

						# verify channels
						self.parent.assertEqual(len(model.channels    )    ,   len(config.out_features    )    )
						self.parent.assertListEqual(model.channels    ,   config.hidden_sizes[:-1]    )

						# verify backbone works with out_features=None
						snake_case				=			None
						snake_case				=			FocalNetBackbone(config=A__    )
						model.to(A__    )
						model.eval()
						snake_case				=			model(A__    )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps    )    ,   1    )
						self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,   [self.batch_size, self.image_size * 2, 4, 4]    )

						# verify channels
						self.parent.assertEqual(len(model.channels    )    ,   1    )
						self.parent.assertListEqual(model.channels    ,   [config.hidden_sizes[-1]]    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Dict:
						snake_case				=			FocalNetForMaskedImageModeling(config=A__    )
						model.to(A__    )
						model.eval()
						snake_case				=			model(A__    )
						self.parent.assertEqual(
						    result.reconstruction.shape    ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

						# test greyscale images
						snake_case				=			1
						snake_case				=			FocalNetForMaskedImageModeling(A__    )
						model.to(A__    )
						model.eval()

						snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
						snake_case				=			model(A__    )
						self.parent.assertEqual(result.reconstruction.shape    ,   (self.batch_size, 1, self.image_size, self.image_size)    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
						snake_case				=			self.type_sequence_label_size
						snake_case				=			FocalNetForImageClassification(A__    )
						model.to(A__    )
						model.eval()
						snake_case				=			model(A__    ,   labels=A__    )
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

						# test greyscale images
						snake_case				=			1
						snake_case				=			FocalNetForImageClassification(A__    )
						model.to(A__    )
						model.eval()

						snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
						snake_case				=			model(A__    )
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







					def     UpperCamelCase			( self    ) ->	int:
						snake_case				=			self.prepare_config_and_inputs()

						snake_case				,		snake_case				,		snake_case				=			config_and_inputs
						snake_case				=			{'''pixel_values''': pixel_values}
						return config, inputs_dict




@require_torch
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
					_UpperCAmelCase										=					(
					    (
					        FocalNetModel,
					        FocalNetForImageClassification,
					        FocalNetForMaskedImageModeling,
					        FocalNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					_UpperCAmelCase										=					(
					    {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
					    if is_torch_available()
					    else {}
					)
					_UpperCAmelCase										=					False

					_UpperCAmelCase										=					False
					_UpperCAmelCase										=					False
					_UpperCAmelCase										=					False
					_UpperCAmelCase										=					False







					def     UpperCamelCase			( self    ) ->	Dict:
						snake_case				=			FocalNetModelTester(self    )
						snake_case				=			ConfigTester(self    ,   config_class=A__    ,   embed_dim=37    ,   has_text_modality=A__    )







					def     UpperCamelCase			( self    ) ->	Dict:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()







					def     UpperCamelCase			( self    ) ->	List[Any]:
						return







					def     UpperCamelCase			( self    ) ->	Tuple:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )







					def     UpperCamelCase			( self    ) ->	List[Any]:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*A__    )







					def     UpperCamelCase			( self    ) ->	Optional[int]:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*A__    )







					def     UpperCamelCase			( self    ) ->	Any:
						snake_case				=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__    )







					@unittest.skip(reason='''FocalNet does not use inputs_embeds'''    )
					def     UpperCamelCase			( self    ) ->	List[Any]:
						pass







					@unittest.skip(reason='''FocalNet does not use feedforward chunking'''    )
					def     UpperCamelCase			( self    ) ->	Optional[int]:
						pass







					def     UpperCamelCase			( self    ) ->	int:
						snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes[:-1]:
							snake_case				=			model_class(A__    )
							self.assertIsInstance(model.get_input_embeddings()    ,   (nn.Module)    )
							snake_case				=			model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(A__    ,   nn.Linear    )    )







					def     UpperCamelCase			( self    ) ->	List[str]:
						snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes[:-1]:
							snake_case				=			model_class(A__    )
							snake_case				=			inspect.signature(model.forward    )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case				=			[*signature.parameters.keys()]

							snake_case				=			['''pixel_values''']
							self.assertListEqual(arg_names[:1]    ,   A__    )







					def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
						snake_case				=			model_class(A__    )
						model.to(A__    )
						model.eval()

						with torch.no_grad():
							snake_case				=			model(**self._prepare_for_class(A__    ,   A__    )    )

						snake_case				=			outputs.hidden_states

						snake_case				=			getattr(
						    self.model_tester    ,   '''expected_num_hidden_layers'''    ,   len(self.model_tester.depths    ) + 1    )
						self.assertEqual(len(A__    )    ,   A__    )

						# FocalNet has a different seq_length
						snake_case				=			(
						    config.patch_size
						    if isinstance(config.patch_size    ,   collections.abc.Iterable    )
						    else (config.patch_size, config.patch_size)
						)

						snake_case				=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]    )    ,   [num_patches, self.model_tester.embed_dim]    ,   )

						snake_case				=			outputs.reshaped_hidden_states
						self.assertEqual(len(A__    )    ,   A__    )

						snake_case				,		snake_case				,		snake_case				,		snake_case				=			reshaped_hidden_states[0].shape
						snake_case				=			(
						    reshaped_hidden_states[0].view(A__    ,   A__    ,   height * width    ).permute(0    ,   2    ,   1    )
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]    )    ,   [num_patches, self.model_tester.embed_dim]    ,   )







					def     UpperCamelCase			( self    ) ->	Optional[int]:
						snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

						snake_case				=			(
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size    ,   collections.abc.Iterable    )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes[:-1]:
							snake_case				=			True
							self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   A__    )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							snake_case				=			True

							self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   A__    )







					def     UpperCamelCase			( self    ) ->	Dict:
						snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()
						snake_case				=			3

						snake_case				=			(
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size    ,   collections.abc.Iterable    )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						snake_case				=			(
						    config.patch_size
						    if isinstance(config.patch_size    ,   collections.abc.Iterable    )
						    else (config.patch_size, config.patch_size)
						)

						snake_case				=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						snake_case				=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes[:-1]:
							snake_case				=			True
							self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   (padded_height, padded_width)    )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							snake_case				=			True
							self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   (padded_height, padded_width)    )







					@slow
					def     UpperCamelCase			( self    ) ->	Optional[Any]:
						for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case				=			FocalNetModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )







					def     UpperCamelCase			( self    ) ->	int:
						snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

						snake_case				=			_config_zero_init(A__    )
						for model_class in self.all_model_classes:
							snake_case				=			model_class(config=A__    )
							for name, param in model.named_parameters():
								if "embeddings" not in name and param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9).round() / 1e9).item()    ,   [0.0, 1.0]    ,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""    ,   )




@require_vision
@require_torch
class 			_lowercase						(       unittest.TestCase					):







					@cached_property
					def     UpperCamelCase			( self    ) ->	Optional[int]:
						# TODO update organization
						return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''    ) if is_vision_available() else None







					@slow
					def     UpperCamelCase			( self    ) ->	Tuple:
						snake_case				=			FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''    ).to(A__    )
						snake_case				=			self.default_image_processor

						snake_case				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
						snake_case				=			image_processor(images=A__    ,   return_tensors='''pt'''    ).to(A__    )

						# forward pass
						with torch.no_grad():
							snake_case				=			model(**A__    )

						# verify the logits
						snake_case				=			torch.Size((1, 10_00)    )
						self.assertEqual(outputs.logits.shape    ,   A__    )
						snake_case				=			torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]    ).to(A__    )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,   A__    ,   atol=1e-4    )    )
						self.assertTrue(outputs.logits.argmax(dim=-1    ).item()    ,   2_81    )




@require_torch
class 			_lowercase						(       __a				,		unittest.TestCase					):
					_UpperCAmelCase										=					(FocalNetBackbone,) if is_torch_available() else ()
					_UpperCAmelCase										=					FocalNetConfig

					_UpperCAmelCase										=					False







					def     UpperCamelCase			( self    ) ->	Tuple:
						snake_case				=			FocalNetModelTester(self    )








'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase       						=			logging.get_logger(__name__)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   **A__    ) ->	Union[str, Any]:
      snake_case				=			feature_size
      snake_case				=			sampling_rate
      snake_case				=			padding_value

      snake_case				=			kwargs.pop('''padding_side'''    ,   '''right'''    )
      snake_case				=			kwargs.pop('''return_attention_mask'''    ,   A__    )

      super().__init__(**A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = True    ,   A__ = None    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(A__    ,   (list, tuple)    ) and isinstance(processed_features[0]    ,   (dict, BatchFeature)    ):
       snake_case				=			{
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
       raise ValueError(
           '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
           F""" to this method that includes {self.model_input_names[0]}, but you provided"""
           F""" {list(processed_features.keys()    )}"""    )

      snake_case				=			processed_features[self.model_input_names[0]]
      snake_case				=			(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(A__    ) == 0:
       if return_attention_mask:
        snake_case				=			[]
       return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      snake_case				=			required_input[0]
      if isinstance(A__    ,   (list, tuple)    ):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       snake_case				=			0
       while len(required_input[index]    ) == 0:
        index += 1
       if index < len(A__    ):
        snake_case				=			required_input[index][0]

      if return_tensors is None:
       if is_tf_tensor(A__    ):
        snake_case				=			'''tf'''
       elif is_torch_tensor(A__    ):
        snake_case				=			'''pt'''
       elif isinstance(A__    ,   (int, float, list, tuple, np.ndarray)    ):
        snake_case				=			'''np'''
       else:
        raise ValueError(
            F"""type of {first_element} unknown: {type(A__    )}. """
            '''Should be one of a python, numpy, pytorch or tensorflow object.'''    )

      for key, value in processed_features.items():
       if isinstance(value[0]    ,   (int, float)    ):
        snake_case				=			to_numpy(A__    )
       else:
        snake_case				=			[to_numpy(A__    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      snake_case				=			self._get_padding_strategies(padding=A__    ,   max_length=A__    )

      snake_case				=			processed_features[self.model_input_names[0]]

      snake_case				=			len(A__    )
      if not all(len(A__    ) == batch_size for v in processed_features.values()    ):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			{k: v[i] for k, v in processed_features.items()}
       # truncation
       snake_case				=			self._truncate(
           A__    ,   max_length=A__    ,   pad_to_multiple_of=A__    ,   truncation=A__    ,   )
       truncated_inputs.append(A__    )

      if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       snake_case				=			max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
       snake_case				=			PaddingStrategy.MAX_LENGTH

      snake_case				=			{}
      for i in range(A__    ):
       # padding
       snake_case				=			self._pad(
           truncated_inputs[i]    ,   max_length=A__    ,   padding_strategy=A__    ,   pad_to_multiple_of=A__    ,   return_attention_mask=A__    ,   )

       for key, value in outputs.items():
        if key not in batch_outputs:
         snake_case				=			[]
        if value.dtype is np.dtype(np.floataa    ):
         snake_case				=			value.astype(np.floataa    )
        batch_outputs[key].append(A__    )

      return BatchFeature(A__    ,   tensor_type=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = PaddingStrategy.DO_NOT_PAD    ,   A__ = None    ,   A__ = None    ,   ) ->	dict:
      snake_case				=			processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       snake_case				=			len(A__    )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__    ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
       snake_case				=			np.ones(len(A__    )    ,   dtype=np.intaa    )

      if needs_to_be_padded:
       snake_case				=			max_length - len(A__    )
       if self.padding_side == "right":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (0, difference)    )
        snake_case				=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       elif self.padding_side == "left":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (difference, 0)    )
        snake_case				=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       else:
        raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

      return processed_features







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	Union[str, Any]:
      if not truncation:
       return processed_features
      elif truncation and max_length is None:
       raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''    )

      snake_case				=			processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			len(A__    ) > max_length

      if needs_to_be_truncated:
       snake_case				=			processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        snake_case				=			processed_features['''attention_mask'''][:max_length]

      return processed_features







     def     UpperCamelCase			( self    ,   A__=False    ,   A__=None    ) ->	Union[str, Any]:

      # Get padding strategy
      if padding is not False:
       if padding is True:
        snake_case				=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(A__    ,   A__    ):
        snake_case				=			PaddingStrategy(A__    )
       elif isinstance(A__    ,   A__    ):
        snake_case				=			padding
      else:
       snake_case				=			PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
           ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''    )

      return padding_strategy

