

import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MaskFormerSwinBackbone
					from transformers.models.maskformer import MaskFormerSwinModel






class        A_		:
							def __init__(		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Dict   ,SCREAMING_SNAKE_CASE__		:			List[str]=1_3   ,SCREAMING_SNAKE_CASE__		:			int=3_2   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=2   ,SCREAMING_SNAKE_CASE__		:			Any=3   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=1_6   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=[1, 2, 1]   ,SCREAMING_SNAKE_CASE__		:			Tuple=[2, 2, 4]   ,SCREAMING_SNAKE_CASE__		:			List[Any]=2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=2.0   ,SCREAMING_SNAKE_CASE__		:			Any=True   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=0.0   ,SCREAMING_SNAKE_CASE__		:			int=0.0   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=0.1   ,SCREAMING_SNAKE_CASE__		:			List[str]="gelu"   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=False   ,SCREAMING_SNAKE_CASE__		:			List[str]=True   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=0.02   ,SCREAMING_SNAKE_CASE__		:			List[Any]=1E-5   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=True   ,SCREAMING_SNAKE_CASE__		:			int=None   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=True   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=1_0   ,SCREAMING_SNAKE_CASE__		:			int=8   ,SCREAMING_SNAKE_CASE__		:			Tuple=["stage1", "stage2", "stage3"]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=[1, 2, 3]   ,):
								__lowerCamelCase				:				Optional[int]					       = parent
								__lowerCamelCase				:				Optional[Any]					       = batch_size
								__lowerCamelCase				:				Optional[Any]					       = image_size
								__lowerCamelCase				:				Any					       = patch_size
								__lowerCamelCase				:				List[Any]					       = num_channels
								__lowerCamelCase				:				Any					       = embed_dim
								__lowerCamelCase				:				Union[str, Any]					       = depths
								__lowerCamelCase				:				str					       = num_heads
								__lowerCamelCase				:				Any					       = window_size
								__lowerCamelCase				:				List[Any]					       = mlp_ratio
								__lowerCamelCase				:				str					       = qkv_bias
								__lowerCamelCase				:				Union[str, Any]					       = hidden_dropout_prob
								__lowerCamelCase				:				int					       = attention_probs_dropout_prob
								__lowerCamelCase				:				List[str]					       = drop_path_rate
								__lowerCamelCase				:				Optional[int]					       = hidden_act
								__lowerCamelCase				:				Optional[int]					       = use_absolute_embeddings
								__lowerCamelCase				:				Dict					       = patch_norm
								__lowerCamelCase				:				List[Any]					       = layer_norm_eps
								__lowerCamelCase				:				Optional[int]					       = initializer_range
								__lowerCamelCase				:				Tuple					       = is_training
								__lowerCamelCase				:				Union[str, Any]					       = scope
								__lowerCamelCase				:				Optional[int]					       = use_labels
								__lowerCamelCase				:				str					       = type_sequence_label_size
								__lowerCamelCase				:				str					       = encoder_stride
								__lowerCamelCase				:				Union[str, Any]					       = out_features
								__lowerCamelCase				:				List[Any]					       = out_indices
							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				str					       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								__lowerCamelCase				:				Dict					       = None
								if self.use_labels:
									__lowerCamelCase				:				Optional[int]					       = ids_tensor([self.batch_size]   ,self.type_sequence_label_size)

								__lowerCamelCase				:				Union[str, Any]					       = self.get_config()

								return config, pixel_values, labels
							def     lowerCAmelCase     (		self		:			List[Any]):
								return MaskFormerSwinConfig(
								    image_size=self.image_size   ,patch_size=self.patch_size   ,num_channels=self.num_channels   ,embed_dim=self.embed_dim   ,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     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Any):
								__lowerCamelCase				:				Union[str, Any]					       = MaskFormerSwinModel(config=_UpperCamelCase)
								model.to(_UpperCamelCase)
								model.eval()
								__lowerCamelCase				:				Union[str, Any]					       = model(_UpperCamelCase)

								__lowerCamelCase				:				Dict					       = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
								__lowerCamelCase				:				Dict					       = 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     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								__lowerCamelCase				:				Any					       = MaskFormerSwinBackbone(config=_UpperCamelCase)
								model.to(_UpperCamelCase)
								model.eval()
								__lowerCamelCase				:				int					       = model(_UpperCamelCase)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps)   ,len(config.out_features))
								self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,[1_3, 1_6, 1_6, 1_6])

								# verify channels
								self.parent.assertEqual(len(model.channels)   ,len(config.out_features))
								self.parent.assertListEqual(model.channels   ,[1_6, 3_2, 6_4])

								# verify ValueError
								with self.parent.assertRaises(_UpperCamelCase):
									__lowerCamelCase				:				List[Any]					       = ['stem']
									__lowerCamelCase				:				List[Any]					       = MaskFormerSwinBackbone(config=_UpperCamelCase)


							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				Any					       = self.prepare_config_and_inputs()
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				List[str]					       = config_and_inputs
								__lowerCamelCase				:				Optional[int]					       = {'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class        A_		(       __A			,							__A			,							unittest.TestCase				):
							_UpperCAmelCase    :  Tuple						      =      (
							    (
							        MaskFormerSwinModel,
							        MaskFormerSwinBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase    :  List[Any]						      =      {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
							_UpperCAmelCase    :  List[Any]						      =      False
							_UpperCAmelCase    :  Optional[int]						      =      False
							_UpperCAmelCase    :  Union[str, Any]						      =      False
							_UpperCAmelCase    :  Dict						      =      False
							_UpperCAmelCase    :  List[Any]						      =      False
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase				:				Optional[Any]					       = MaskFormerSwinModelTester(self)
								__lowerCamelCase				:				Tuple					       = ConfigTester(self   ,config_class=_UpperCamelCase   ,embed_dim=3_7)
							@require_torch_multi_gpu
							@unittest.skip(
							    reason=(
							        '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
							        ' `nn.DataParallel`'
							    ))
							def     lowerCAmelCase     (		self		:			List[str]):
								pass
							def     lowerCAmelCase     (		self		:			int):
								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     lowerCAmelCase     (		self		:			int):
								return
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Dict					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCamelCase)
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase				:				Optional[Any]					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_UpperCamelCase)
							@unittest.skip('Swin does not use inputs_embeds')
							def     lowerCAmelCase     (		self		:			List[str]):
								pass
							@unittest.skip('Swin does not support feedforward chunking')
							def     lowerCAmelCase     (		self		:			List[Any]):
								pass
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase       , __lowerCamelCase				:				Any					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				Union[str, Any]					       = model_class(_UpperCamelCase)
									self.assertIsInstance(model.get_input_embeddings()   ,(nn.Module))
									__lowerCamelCase				:				Optional[Any]					       = model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_UpperCamelCase   ,nn.Linear))
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase				:				Dict					       = ['pixel_values']
									self.assertListEqual(arg_names[:1]   ,_UpperCamelCase)
							@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions')
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								pass
							@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone')
							def     lowerCAmelCase     (		self		:			int):
								pass
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]):
								__lowerCamelCase				:				List[Any]					       = model_class(_UpperCamelCase)
								model.to(_UpperCamelCase)
								model.eval()

								with torch.no_grad():
									__lowerCamelCase				:				Any					       = model(**self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase))

								__lowerCamelCase				:				Dict					       = outputs.hidden_states

								__lowerCamelCase				:				List[str]					       = getattr(
								    self.model_tester   ,'expected_num_hidden_layers'   ,len(self.model_tester.depths) + 1)
								self.assertEqual(len(_UpperCamelCase)   ,_UpperCamelCase)

								# Swin has a different seq_length
								__lowerCamelCase				:				int					       = (
								    config.patch_size
								    if isinstance(config.patch_size   ,collections.abc.Iterable)
								    else (config.patch_size, config.patch_size)
								)

								__lowerCamelCase				:				List[str]					       = (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]   ,)
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase       , __lowerCamelCase				:				Dict					       = self.model_tester.prepare_config_and_inputs_for_common()

								__lowerCamelCase				:				Optional[Any]					       = (
								    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:
									__lowerCamelCase				:				Any					       = True
									self.check_hidden_states_output(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase				:				Union[str, Any]					       = True

									self.check_hidden_states_output(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase)
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase       , __lowerCamelCase				:				str					       = self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase				:				Optional[int]					       = 3

								__lowerCamelCase				:				Dict					       = (
								    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)
								)
								__lowerCamelCase				:				Optional[int]					       = (
								    config.patch_size
								    if isinstance(config.patch_size   ,collections.abc.Iterable)
								    else (config.patch_size, config.patch_size)
								)

								__lowerCamelCase				:				Dict					       = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								__lowerCamelCase				:				List[Any]					       = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				List[Any]					       = True
									self.check_hidden_states_output(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase   ,(padded_height, padded_width))

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase				:				str					       = True
									self.check_hidden_states_output(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase   ,(padded_height, padded_width))
							@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints')
							def     lowerCAmelCase     (		self		:			List[str]):
								pass
							@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
							def     lowerCAmelCase     (		self		:			List[Any]):
								pass
							@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
							def     lowerCAmelCase     (		self		:			List[str]):
								pass


							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase       , __lowerCamelCase				:				Dict					       = self.model_tester.prepare_config_and_inputs_for_common()

								def set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE__		:			Any):
									__lowerCamelCase				:				Optional[int]					       = 0
									return t

								def check_equivalence(SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			List[Any]={}):
									with torch.no_grad():
										__lowerCamelCase				:				Union[str, Any]					       = model(**_UpperCamelCase   ,return_dict=_UpperCamelCase   ,**_UpperCamelCase)
										__lowerCamelCase				:				List[Any]					       = model(**_UpperCamelCase   ,return_dict=_UpperCamelCase   ,**_UpperCamelCase).to_tuple()

										def recursive_check(SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			List[str]):
											if isinstance(_UpperCamelCase   ,(List, Tuple)):
												for tuple_iterable_value, dict_iterable_value in zip(_UpperCamelCase   ,_UpperCamelCase):
													recursive_check(_UpperCamelCase   ,_UpperCamelCase)
											elif isinstance(_UpperCamelCase   ,_UpperCamelCase):
												for tuple_iterable_value, dict_iterable_value in zip(
												    tuple_object.values()   ,dict_object.values()):
													recursive_check(_UpperCamelCase   ,_UpperCamelCase)
											elif tuple_object is None:
												return
											else:
												self.assertTrue(
												    torch.allclose(
												        set_nan_tensor_to_zero(_UpperCamelCase)   ,set_nan_tensor_to_zero(_UpperCamelCase)   ,atol=1E-5)   ,msg=(
												        'Tuple and dict output are not equal. Difference:'
												        F" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
												        F" {torch.isnan(_UpperCamelCase).any()} and `inf`: {torch.isinf(_UpperCamelCase)}. Dict has"
												        F" `nan`: {torch.isnan(_UpperCamelCase).any()} and `inf`: {torch.isinf(_UpperCamelCase)}."
												    )   ,)

										recursive_check(_UpperCamelCase   ,_UpperCamelCase)

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				Optional[Any]					       = model_class(_UpperCamelCase)
									model.to(_UpperCamelCase)
									model.eval()

									__lowerCamelCase				:				Dict					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase)
									__lowerCamelCase				:				Dict					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase)
									check_equivalence(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase)

									__lowerCamelCase				:				Optional[Any]					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase   ,return_labels=_UpperCamelCase)
									__lowerCamelCase				:				Any					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase   ,return_labels=_UpperCamelCase)
									check_equivalence(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase)

									__lowerCamelCase				:				List[str]					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase)
									__lowerCamelCase				:				Optional[Any]					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase)
									check_equivalence(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase   ,{'output_hidden_states': True})

									__lowerCamelCase				:				str					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase   ,return_labels=_UpperCamelCase)
									__lowerCamelCase				:				Optional[Any]					       = self._prepare_for_class(_UpperCamelCase   ,_UpperCamelCase   ,return_labels=_UpperCamelCase)
									check_equivalence(_UpperCamelCase   ,_UpperCamelCase   ,_UpperCamelCase   ,{'output_hidden_states': True})






@require_torch
class        A_		(       unittest.TestCase			,							__A				):
							_UpperCAmelCase    :  int						      =      (MaskFormerSwinBackbone,) if is_torch_available() else ()
							_UpperCAmelCase    :  int						      =      MaskFormerSwinConfig
							def     lowerCAmelCase     (		self		:			Any):
								__lowerCamelCase				:				Tuple					       = MaskFormerSwinModelTester(self)


							def     lowerCAmelCase     (		self		:			Dict):
								__lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase				:				Optional[Any]					       = inputs_dict['pixel_values'].shape[0]

								for backbone_class in self.all_model_classes:
									__lowerCamelCase				:				List[Any]					       = backbone_class(_UpperCamelCase)
									backbone.to(_UpperCamelCase)
									backbone.eval()

									__lowerCamelCase				:				Optional[Any]					       = backbone(**_UpperCamelCase)

									# Test default outputs and verify feature maps
									self.assertIsInstance(outputs.feature_maps   ,_UpperCamelCase)
									self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
									for feature_map, n_channels in zip(outputs.feature_maps   ,backbone.channels):
										self.assertTrue(feature_map.shape[:2]   ,(batch_size, n_channels))
									self.assertIsNone(outputs.hidden_states)
									self.assertIsNone(outputs.attentions)

									# Test output_hidden_states=True
									__lowerCamelCase				:				int					       = backbone(**_UpperCamelCase   ,output_hidden_states=_UpperCamelCase)
									self.assertIsNotNone(outputs.hidden_states)
									self.assertTrue(len(outputs.hidden_states)   ,len(backbone.stage_names))
									# We skip the stem layer
									for hidden_states, n_channels in zip(outputs.hidden_states[1:]   ,backbone.channels):
										for hidden_state in hidden_states:
											# Hidden states are in the format (batch_size, (height * width), n_channels)
											__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				int					       = hidden_state.shape
											self.assertTrue((h_batch_size, h_n_channels)   ,(batch_size, n_channels))

            # Test output_attentions=True
									if self.has_attentions:
										__lowerCamelCase				:				Optional[int]					       = backbone(**_UpperCamelCase   ,output_attentions=_UpperCamelCase)
										self.assertIsNotNone(outputs.attentions)







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)