




from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor

class 						__lowerCAmelCase      :
							def __init__(		self		:Dict						, __magic_name__		:List[Any]						, __magic_name__		:Union[str, Any]=13						, __magic_name__		:Tuple=30						, __magic_name__		:List[Any]=2						, __magic_name__		:Optional[int]=3						, __magic_name__		:List[Any]=True						, __magic_name__		:Any=True						, __magic_name__		:Any=32						, __magic_name__		:List[str]=2						, __magic_name__		:Tuple=4						, __magic_name__		:Optional[int]=37						, __magic_name__		:Optional[Any]="gelu"						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:Union[str, Any]=10						, __magic_name__		:str=0.02						, __magic_name__		:Tuple=3						, __magic_name__		:Any=0.6						, __magic_name__		:List[Any]=None						, ):







								'''simple docstring'''







								a									=				parent
								a									=				batch_size
								a									=				image_size
								a									=				patch_size
								a									=				num_channels
								a									=				is_training
								a									=				use_labels
								a									=				hidden_size
								a									=				num_hidden_layers
								a									=				num_attention_heads
								a									=				intermediate_size
								a									=				hidden_act
								a									=				hidden_dropout_prob
								a									=				attention_probs_dropout_prob
								a									=				type_sequence_label_size
								a									=				initializer_range
								a									=				mask_ratio
								a									=				scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								a									=				(image_size // patch_size) ** 2
								a									=				int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

								a									=				self.get_config()

								return config, pixel_values, labels
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								return ViTMAEConfig(
								    image_size=self.image_size						, patch_size=self.patch_size						, num_channels=self.num_channels						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, decoder_hidden_size=self.hidden_size						, decoder_num_hidden_layers=self.num_hidden_layers						, decoder_num_attention_heads=self.num_attention_heads						, decoder_intermediate_size=self.intermediate_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, is_decoder=__magic_name__						, initializer_range=self.initializer_range						, mask_ratio=self.mask_ratio						, )
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:Dict						, __magic_name__		:List[Any]						, __magic_name__		:List[Any]							):







								'''simple docstring'''







								a									=				TFViTMAEModel(config=__magic_name__							)
								a									=				model(__magic_name__						, training=__magic_name__							)
								self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)							)
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:Dict						, __magic_name__		:Any						, __magic_name__		:Dict							):







								'''simple docstring'''







								a									=				TFViTMAEForPreTraining(__magic_name__							)
								a									=				model(__magic_name__						, training=__magic_name__							)
								# expected sequence length = num_patches
								a									=				(self.image_size // self.patch_size) ** 2
								a									=				self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, num_patches, expected_num_channels)							)

								# test greyscale images
								a									=				1
								a									=				TFViTMAEForPreTraining(__magic_name__							)

								a									=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								a									=				model(__magic_name__						, training=__magic_name__							)
								a									=				self.patch_size**2
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, num_patches, expected_num_channels)							)





							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				self.prepare_config_and_inputs()
								((a)							,					(a)							,					(a))									=				config_and_inputs
								a									=				{"""pixel_values""": pixel_values}
								return config, inputs_dict







@require_tf
class 						__lowerCAmelCase      (      __magic_name__ ,			__magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							UpperCamelCase__   	=    {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								a									=				TFViTMAEModelTester(self							)
								a									=				ConfigTester(self						, config_class=__magic_name__						, has_text_modality=__magic_name__						, hidden_size=37							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								pass
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a									=				model_class(__magic_name__							)
									self.assertIsInstance(model.get_input_embeddings()						, (tf.keras.layers.Layer)							)
									a									=				model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(__magic_name__						, tf.keras.layers.Layer							)							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

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

									a									=				["""pixel_values"""]
									self.assertListEqual(arg_names[:1]						, __magic_name__							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__magic_name__							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*__magic_name__							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								np.random.seed(2							)

								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								a									=				int((config.image_size // config.patch_size) ** 2							)
								a									=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

								for model_class in self.all_model_classes:
									a									=				model_class(__magic_name__							)
									a									=				self._prepare_for_class(__magic_name__						, __magic_name__							)

									a									=				model(__magic_name__						, noise=__magic_name__							)

									a									=				copy.deepcopy(self._prepare_for_class(__magic_name__						, __magic_name__							)							)
									a									=				model(**__magic_name__						, noise=__magic_name__							)
									a									=				outputs_dict[0].numpy()
									a									=				outputs_keywords[0].numpy()

									self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)						, 1E-6							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								np.random.seed(2							)

								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								a									=				int((config.image_size // config.patch_size) ** 2							)
								a									=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

								def prepare_numpy_arrays(__magic_name__		:Optional[int]							):
									a									=				{}
									for k, v in inputs_dict.items():
										if tf.is_tensor(__magic_name__							):
											a									=				v.numpy()
										else:
											a									=				np.array(__magic_name__							)

									return inputs_np_dict

								for model_class in self.all_model_classes:
									a									=				model_class(__magic_name__							)

									a									=				self._prepare_for_class(__magic_name__						, __magic_name__							)
									a									=				prepare_numpy_arrays(__magic_name__							)

									a									=				model(__magic_name__						, noise=__magic_name__							)
									a									=				model(**__magic_name__						, noise=__magic_name__							)
									self.assert_outputs_same(__magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:List[str]						, __magic_name__		:List[str]						, __magic_name__		:str							):







								'''simple docstring'''







								np.random.seed(2							)

								a									=				int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
								a									=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
								a									=				tf.constant(__magic_name__							)

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								a									=				tf_noise

								super().check_pt_tf_models(__magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								np.random.seed(2							)

								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								a									=				{
								    module_member
								    for model_class in self.all_model_classes
								    for module in (import_module(model_class.__module__							),)
								    for module_member_name in dir(__magic_name__							)
								    if module_member_name.endswith("""MainLayer"""							)
								    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
								    and module_member_name[: -len("""MainLayer"""							)] == model_class.__name__[: -len("""Model"""							)]
								    for module_member in (getattr(__magic_name__						, __magic_name__							),)
								    if isinstance(__magic_name__						, __magic_name__							)
								    and tf.keras.layers.Layer in module_member.__bases__
								    and getattr(__magic_name__						, """_keras_serializable"""						, __magic_name__							)
								}

								a									=				int((config.image_size // config.patch_size) ** 2							)
								a									=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
								a									=				tf.convert_to_tensor(__magic_name__							)
								inputs_dict.update({"""noise""": noise}							)

								for main_layer_class in tf_main_layer_classes:
									a									=				main_layer_class(__magic_name__							)

									a									=				{
									    name: tf.keras.Input(tensor.shape[1:]						, dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
									}

									a									=				tf.keras.Model(__magic_name__						, outputs=main_layer(__magic_name__							)							)
									a									=				model(__magic_name__							)

									with tempfile.TemporaryDirectory() as tmpdirname:
										a									=				os.path.join(__magic_name__						, """keras_model.h5"""							)
										model.save(__magic_name__							)
										a									=				tf.keras.models.load_model(
										    __magic_name__						, custom_objects={main_layer_class.__name__: main_layer_class}							)
										assert isinstance(__magic_name__						, tf.keras.Model							)
										a									=				model(__magic_name__							)
										self.assert_outputs_same(__magic_name__						, __magic_name__							)
							@slow
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								np.random.seed(2							)

								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								a									=				int((config.image_size // config.patch_size) ** 2							)
								a									=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

								for model_class in self.all_model_classes:
									a									=				model_class(__magic_name__							)
									a									=				self._prepare_for_class(__magic_name__						, __magic_name__							)
									a									=				model(__magic_name__						, noise=__magic_name__							)

									if model_class.__name__ == "TFViTMAEModel":
										a									=				outputs.last_hidden_state.numpy()
										a									=				0
									else:
										a									=				outputs.logits.numpy()
										a									=				0

									with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__magic_name__						, saved_model=__magic_name__							)
										a									=				model_class.from_pretrained(__magic_name__							)
										a									=				model(__magic_name__						, noise=__magic_name__							)

										if model_class.__name__ == "TFViTMAEModel":
											a									=				after_outputs["""last_hidden_state"""].numpy()
											a									=				0
										else:
											a									=				after_outputs["""logits"""].numpy()
											a									=				0

										a									=				np.amax(np.abs(out_a - out_a							)							)
										self.assertLessEqual(__magic_name__						, 1E-5							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								np.random.seed(2							)

								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()

								a									=				int((config.image_size // config.patch_size) ** 2							)
								a									=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

								for model_class in self.all_model_classes:
									a									=				model_class(__magic_name__							)
									a									=				self._prepare_for_class(__magic_name__						, __magic_name__							)

									a									=				model(__magic_name__						, noise=__magic_name__							)
									a									=				model.get_config()
									# make sure that returned config is jsonifiable, which is required by keras
									json.dumps(__magic_name__							)
									a									=				model_class.from_config(model.get_config()							)
									# make sure it also accepts a normal config
									a									=				model_class.from_config(model.config							)
									a									=				new_model(__magic_name__							)  # Build model
									new_model.set_weights(model.get_weights()							)
									a									=				new_model(__magic_name__						, noise=__magic_name__							)

									self.assert_outputs_same(__magic_name__						, __magic_name__							)
							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								pass
							@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								pass





							@slow
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""							)
								self.assertIsNotNone(__magic_name__							)







def 						__A						(      ) ->			Tuple:
	a									=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image



@require_tf
@require_vision
class 						__lowerCAmelCase      (      unittest.TestCase					):
							@cached_property
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None





							@slow
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								np.random.seed(2							)

								a									=				TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							)

								a									=				self.default_image_processor
								a									=				prepare_img()
								a									=				image_processor(images=__magic_name__						, return_tensors="""tf"""							)

								# prepare a noise vector that will be also used for testing the TF model
								# (this way we can ensure that the PT and TF models operate on the same inputs)
								a									=				ViTMAEConfig()
								a									=				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
								a									=				np.random.uniform(size=(1, num_patches)							)

								# forward pass
								a									=				model(**__magic_name__						, noise=__magic_name__							)

								# verify the logits
								a									=				tf.convert_to_tensor([1, 196, 768]							)
								self.assertEqual(outputs.logits.shape						, __magic_name__							)

								a									=				tf.convert_to_tensor(
								    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

								tf.debugging.assert_near(outputs.logits[0, :3, :3]						, __magic_name__						, atol=1E-4							)







import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf

		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__UpperCamelCase   :			Dict          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      enum.Enum					):
							UpperCamelCase__   	=    0
							UpperCamelCase__   	=    1







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''generated'''
							def __init__(		self		:Any						, *__magic_name__		:Tuple						, **__magic_name__		:Tuple							):







								'''simple docstring'''







								super().__init__(*__magic_name__						, **__magic_name__							)

								self.check_model_type(
								    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
								    if self.framework == """tf"""
								    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING							)
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Any=None						, __magic_name__		:Optional[Any]=None						, __magic_name__		:Any=None						, __magic_name__		:List[str]=None						, __magic_name__		:Tuple=None						, __magic_name__		:str=None						, **__magic_name__		:List[Any]						, ):







								'''simple docstring'''







								a									=				{}
								if truncation is not None:
									a									=				truncation

								a									=				generate_kwargs

								a									=				{}
								if return_tensors is not None and return_type is None:
									a									=				ReturnType.TENSORS if return_tensors else ReturnType.TEXT
								if return_type is not None:
									a									=				return_type

								if clean_up_tokenization_spaces is not None:
									a									=				clean_up_tokenization_spaces

								if stop_sequence is not None:
									a									=				self.tokenizer.encode(__magic_name__						, add_special_tokens=__magic_name__							)
									if len(__magic_name__							) > 1:
										warnings.warn(
										    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
										    """ the stop sequence will be used as the stop sequence string in the interim."""							)
									a									=				stop_sequence_ids[0]

								return preprocess_params, forward_params, postprocess_params
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								return True
							def  lowerCamelCase__				(		self		:Dict						, *__magic_name__		:Optional[int]						, __magic_name__		:List[str]							):







								'''simple docstring'''







								a									=				self.model.config.prefix if self.model.config.prefix is not None else """"""
								if isinstance(args[0]						, __magic_name__							):
									if self.tokenizer.pad_token_id is None:
										raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""							)
									a									=				([prefix + arg for arg in args[0]],)
									a									=				True

								elif isinstance(args[0]						, __magic_name__							):
									a									=				(prefix + args[0],)
									a									=				False
								else:
									raise ValueError(
									    F' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'							)
								a									=				self.tokenizer(*__magic_name__						, padding=__magic_name__						, truncation=__magic_name__						, return_tensors=self.framework							)
								# This is produced by tokenizers but is an invalid generate kwargs
								if "token_type_ids" in inputs:
									del inputs["token_type_ids"]
								return inputs
							def __call__(		self		:Tuple						, *__magic_name__		:Any						, **__magic_name__		:str							):







								'''simple docstring'''







								a									=				super().__call__(*__magic_name__						, **__magic_name__							)
								if (
								    isinstance(args[0]						, __magic_name__							)
								    and all(isinstance(__magic_name__						, __magic_name__							) for el in args[0]							)
								    and all(len(__magic_name__							) == 1 for res in result							)
								):
									return [res[0] for res in result]
								return result
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Optional[Any]						, __magic_name__		:List[str]=TruncationStrategy.DO_NOT_TRUNCATE						, **__magic_name__		:Any							):







								'''simple docstring'''







								a									=				self._parse_and_tokenize(__magic_name__						, truncation=__magic_name__						, **__magic_name__							)
								return inputs
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int						, **__magic_name__		:int							):







								'''simple docstring'''







								if self.framework == "pt":
									a							,					a									=				model_inputs["""input_ids"""].shape
								elif self.framework == "tf":
									a							,					a									=				tf.shape(model_inputs["""input_ids"""]							).numpy()

								a									=				generate_kwargs.get("""min_length"""						, self.model.config.min_length							)
								a									=				generate_kwargs.get("""max_length"""						, self.model.config.max_length							)
								self.check_inputs(__magic_name__						, generate_kwargs["""min_length"""]						, generate_kwargs["""max_length"""]							)
								a									=				self.model.generate(**__magic_name__						, **__magic_name__							)
								a									=				output_ids.shape[0]
								if self.framework == "pt":
									a									=				output_ids.reshape(__magic_name__						, out_b // in_b						, *output_ids.shape[1:]							)
								elif self.framework == "tf":
									a									=				tf.reshape(__magic_name__						, (in_b, out_b // in_b, *output_ids.shape[1:])							)
								return {"output_ids": output_ids}





							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Dict						, __magic_name__		:Any=ReturnType.TEXT						, __magic_name__		:int=False							):







								'''simple docstring'''







								a									=				[]
								for output_ids in model_outputs["output_ids"][0]:
									if return_type == ReturnType.TENSORS:
										a									=				{F'{self.return_name}_token_ids': output_ids}
									elif return_type == ReturnType.TEXT:
										a									=				{
										    F'{self.return_name}_text': self.tokenizer.decode(
										        __magic_name__						, skip_special_tokens=__magic_name__						, clean_up_tokenization_spaces=__magic_name__						, )
										}
									records.append(__magic_name__							)
								return records







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''summary'''
							def __call__(		self		:Any						, *__magic_name__		:List[str]						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







								return super().__call__(*__magic_name__						, **__magic_name__							)





							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								if max_length < min_length:
									logger.warning(F'Your min_length={min_length} must be inferior than your max_length={max_length}.'							)

								if input_length < max_length:
									logger.warning(
									    F'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
									    """a summarization task, where outputs shorter than the input are typically wanted, you might """
									    F'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'							)







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''translation'''
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								if input_length > 0.9 * max_length:
									logger.warning(
									    F'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
									    """increasing your max_length manually, e.g. translator('...', max_length=400)"""							)
								return True
							def  lowerCamelCase__				(		self		:str						, *__magic_name__		:Union[str, Any]						, __magic_name__		:Any=TruncationStrategy.DO_NOT_TRUNCATE						, __magic_name__		:Optional[Any]=None						, __magic_name__		:List[str]=None							):







								'''simple docstring'''







								if getattr(self.tokenizer						, """_build_translation_inputs"""						, __magic_name__							):
									return self.tokenizer._build_translation_inputs(
									    *__magic_name__						, return_tensors=self.framework						, truncation=__magic_name__						, src_lang=__magic_name__						, tgt_lang=__magic_name__							)
								else:
									return super()._parse_and_tokenize(*__magic_name__						, truncation=__magic_name__							)
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:List[str]=None						, __magic_name__		:Union[str, Any]=None						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







								a							,					a							,					a									=				super()._sanitize_parameters(**__magic_name__							)
								if src_lang is not None:
									a									=				src_lang
								if tgt_lang is not None:
									a									=				tgt_lang
								if src_lang is None and tgt_lang is None:
									# Backward compatibility, direct arguments use is preferred.
									a									=				kwargs.get("""task"""						, self.task							)
									a									=				task.split("""_"""							)
									if task and len(__magic_name__							) == 4:
										# translation, XX, to YY
										a									=				items[1]
										a									=				items[3]
								return preprocess_params, forward_params, postprocess_params





							def __call__(		self		:Optional[Any]						, *__magic_name__		:Any						, **__magic_name__		:str							):







								'''simple docstring'''







								return super().__call__(*__magic_name__						, **__magic_name__							)

