




from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		)

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor

class 						__lowerCAmelCase      :
							def __init__(		self		:Union[str, Any]						, __magic_name__		:Optional[int]						, __magic_name__		:List[Any]=2						, __magic_name__		:Optional[int]=3						, __magic_name__		:List[str]=4						, __magic_name__		:List[Any]=2						, __magic_name__		:Union[str, Any]=7						, __magic_name__		:Dict=True						, __magic_name__		:Tuple=True						, __magic_name__		:str=True						, __magic_name__		:List[str]=True						, __magic_name__		:str=99						, __magic_name__		:Optional[Any]=36						, __magic_name__		:List[Any]=2						, __magic_name__		:Union[str, Any]=4						, __magic_name__		:Optional[int]=37						, __magic_name__		:List[Any]="gelu"						, __magic_name__		:Tuple=0.1						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:Tuple=512						, __magic_name__		:List[str]=16						, __magic_name__		:Optional[Any]=2						, __magic_name__		:List[Any]=0.02						, __magic_name__		:Dict=6						, __magic_name__		:int=6						, __magic_name__		:Optional[Any]=3						, __magic_name__		:int=4						, __magic_name__		:str=None						, __magic_name__		:int=1000						, ):







								'''simple docstring'''







								a									=				parent
								a									=				batch_size
								a									=				num_channels
								a									=				image_size
								a									=				patch_size
								a									=				is_training
								a									=				use_input_mask
								a									=				use_token_type_ids
								a									=				use_labels
								a									=				vocab_size
								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									=				max_position_embeddings
								a									=				type_vocab_size
								a									=				type_sequence_label_size
								a									=				initializer_range
								a									=				coordinate_size
								a									=				shape_size
								a									=				num_labels
								a									=				num_choices
								a									=				scope
								a									=				range_bbox

								# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
								a									=				text_seq_length
								a									=				(image_size // patch_size) ** 2 + 1
								a									=				self.text_seq_length + self.image_seq_length
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				ids_tensor([self.batch_size, self.text_seq_length]						, self.vocab_size							)

								a									=				ids_tensor([self.batch_size, self.text_seq_length, 4]						, self.range_bbox							)
								a									=				bbox.numpy()
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]							):
									for j in range(bbox.shape[1]							):
										if bbox[i, j, 3] < bbox[i, j, 1]:
											a									=				bbox[i, j, 3]
											a									=				bbox[i, j, 1]
											a									=				tmp_coordinate
										if bbox[i, j, 2] < bbox[i, j, 0]:
											a									=				bbox[i, j, 2]
											a									=				bbox[i, j, 0]
											a									=				tmp_coordinate
								a									=				tf.constant(__magic_name__							)

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

								a									=				None
								if self.use_input_mask:
									a									=				random_attention_mask([self.batch_size, self.text_seq_length]							)

								a									=				None
								if self.use_token_type_ids:
									a									=				ids_tensor([self.batch_size, self.text_seq_length]						, self.type_vocab_size							)

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

								a									=				LayoutLMvaConfig(
								    vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, 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						, max_position_embeddings=self.max_position_embeddings						, type_vocab_size=self.type_vocab_size						, initializer_range=self.initializer_range						, coordinate_size=self.coordinate_size						, shape_size=self.shape_size						, input_size=self.image_size						, patch_size=self.patch_size						, )

								return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:str						, __magic_name__		:Tuple						, __magic_name__		:Union[str, Any]						, __magic_name__		:str						, __magic_name__		:Optional[Any]						, __magic_name__		:Any							):







								'''simple docstring'''







								a									=				TFLayoutLMvaModel(config=__magic_name__							)

								# text + image
								a									=				model(__magic_name__						, pixel_values=__magic_name__						, training=__magic_name__							)
								a									=				model(
								    __magic_name__						, bbox=__magic_name__						, pixel_values=__magic_name__						, attention_mask=__magic_name__						, token_type_ids=__magic_name__						, training=__magic_name__						, )
								a									=				model(__magic_name__						, bbox=__magic_name__						, pixel_values=__magic_name__						, training=__magic_name__							)

								self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)							)

								# text only
								a									=				model(__magic_name__						, training=__magic_name__							)
								self.parent.assertEqual(
								    result.last_hidden_state.shape						, (self.batch_size, self.text_seq_length, self.hidden_size)							)

								# image only
								a									=				model({"""pixel_values""": pixel_values}						, training=__magic_name__							)
								self.parent.assertEqual(
								    result.last_hidden_state.shape						, (self.batch_size, self.image_seq_length, self.hidden_size)							)
							def  lowerCamelCase__				(		self		:Tuple						, __magic_name__		:List[str]						, __magic_name__		:Dict						, __magic_name__		:int						, __magic_name__		:List[Any]						, __magic_name__		:List[Any]						, __magic_name__		:Optional[Any]						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.num_labels
								a									=				TFLayoutLMvaForSequenceClassification(config=__magic_name__							)
								a									=				model(
								    __magic_name__						, bbox=__magic_name__						, pixel_values=__magic_name__						, attention_mask=__magic_name__						, token_type_ids=__magic_name__						, labels=__magic_name__						, training=__magic_name__						, )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels)							)
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:List[Any]						, __magic_name__		:Optional[int]						, __magic_name__		:List[str]						, __magic_name__		:str						, __magic_name__		:Optional[int]						, __magic_name__		:Dict						, __magic_name__		:Union[str, Any]							):







								'''simple docstring'''







								a									=				self.num_labels
								a									=				TFLayoutLMvaForTokenClassification(config=__magic_name__							)
								a									=				model(
								    __magic_name__						, bbox=__magic_name__						, pixel_values=__magic_name__						, attention_mask=__magic_name__						, token_type_ids=__magic_name__						, labels=__magic_name__						, training=__magic_name__						, )
								self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.text_seq_length, self.num_labels)							)
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Optional[int]						, __magic_name__		:Tuple						, __magic_name__		:Optional[int]						, __magic_name__		:Any						, __magic_name__		:str						, __magic_name__		:List[str]						, __magic_name__		:Optional[int]							):







								'''simple docstring'''







								a									=				2
								a									=				TFLayoutLMvaForQuestionAnswering(config=__magic_name__							)
								a									=				model(
								    __magic_name__						, bbox=__magic_name__						, pixel_values=__magic_name__						, attention_mask=__magic_name__						, token_type_ids=__magic_name__						, start_positions=__magic_name__						, end_positions=__magic_name__						, training=__magic_name__						, )
								self.parent.assertEqual(result.start_logits.shape						, (self.batch_size, self.seq_length)							)
								self.parent.assertEqual(result.end_logits.shape						, (self.batch_size, self.seq_length)							)





							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				self.prepare_config_and_inputs()
								((a)							,					(a)							,					(a)							,					(a)							,					(a)							,					(a)							,					(a)							,					(a))									=				config_and_inputs
								a									=				{
								    """input_ids""": input_ids,
								    """bbox""": bbox,
								    """pixel_values""": pixel_values,
								    """token_type_ids""": token_type_ids,
								    """attention_mask""": input_mask,
								}
								return config, inputs_dict







@require_tf
class 						__lowerCAmelCase      (      __magic_name__ ,			__magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    (
							    (
							        TFLayoutLMvaModel,
							        TFLayoutLMvaForQuestionAnswering,
							        TFLayoutLMvaForSequenceClassification,
							        TFLayoutLMvaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							UpperCamelCase__   	=    (
							    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
							    if is_tf_available()
							    else {}
							)

							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							def  lowerCamelCase__				(		self		:str						, __magic_name__		:Optional[Any]						, __magic_name__		:Optional[Any]						, __magic_name__		:int						, __magic_name__		:List[Any]						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								return True
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:List[str]						, __magic_name__		:Optional[Any]						, __magic_name__		:str=False							):







								'''simple docstring'''







								a									=				copy.deepcopy(__magic_name__							)

								if model_class in get_values(__magic_name__							):
									a									=				{
									    k: tf.tile(tf.expand_dims(__magic_name__						, 1							)						, (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
									    if isinstance(__magic_name__						, tf.Tensor							) and v.ndim > 0
									    else v
									    for k, v in inputs_dict.items()
									}

								if return_labels:
									if model_class in get_values(__magic_name__							):
										a									=				tf.ones(self.model_tester.batch_size						, dtype=tf.intaa							)
									elif model_class in get_values(__magic_name__							):
										a									=				tf.zeros(self.model_tester.batch_size						, dtype=tf.intaa							)
										a									=				tf.zeros(self.model_tester.batch_size						, dtype=tf.intaa							)
									elif model_class in get_values(__magic_name__							):
										a									=				tf.zeros(self.model_tester.batch_size						, dtype=tf.intaa							)
									elif model_class in get_values(__magic_name__							):
										a									=				tf.zeros(
										    (self.model_tester.batch_size, self.model_tester.text_seq_length)						, dtype=tf.intaa							)

								return inputs_dict
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				TFLayoutLMvaModelTester(self							)
								a									=				ConfigTester(self						, config_class=__magic_name__						, hidden_size=37							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def  lowerCamelCase__				(		self		:Optional[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__							)
									if getattr(__magic_name__						, """hf_compute_loss"""						, __magic_name__							):
										# The number of elements in the loss should be the same as the number of elements in the label
										a									=				self._prepare_for_class(inputs_dict.copy()						, __magic_name__						, return_labels=__magic_name__							)
										a									=				prepared_for_class[
										    sorted(prepared_for_class.keys() - inputs_dict.keys()						, reverse=__magic_name__							)[0]
										]
										a									=				added_label.shape.as_list()[:1]

										# Test that model correctly compute the loss with kwargs
										a									=				self._prepare_for_class(inputs_dict.copy()						, __magic_name__						, return_labels=__magic_name__							)
										a									=				prepared_for_class.pop("""input_ids"""							)

										a									=				model(__magic_name__						, **__magic_name__							)[0]
										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

										# Test that model correctly compute the loss when we mask some positions
										a									=				self._prepare_for_class(inputs_dict.copy()						, __magic_name__						, return_labels=__magic_name__							)
										a									=				prepared_for_class.pop("""input_ids"""							)
										if "labels" in prepared_for_class:
											a									=				prepared_for_class["""labels"""].numpy()
											if len(labels.shape							) > 1 and labels.shape[1] != 1:
												a									=				-100
												a									=				tf.convert_to_tensor(__magic_name__							)
												a									=				model(__magic_name__						, **__magic_name__							)[0]
												self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)
												self.assertTrue(not np.any(np.isnan(loss.numpy()							)							)							)

                # Test that model correctly compute the loss with a dict
										a									=				self._prepare_for_class(inputs_dict.copy()						, __magic_name__						, return_labels=__magic_name__							)
										a									=				model(__magic_name__							)[0]
										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

										# Test that model correctly compute the loss with a tuple
										a									=				self._prepare_for_class(inputs_dict.copy()						, __magic_name__						, return_labels=__magic_name__							)

										# Get keys that were added with the _prepare_for_class function
										a									=				prepared_for_class.keys() - inputs_dict.keys()
										a									=				inspect.signature(model.call							).parameters
										a									=				list(signature.keys()							)

										# Create a dictionary holding the location of the tensors in the tuple
										a									=				{0: """input_ids"""}
										for label_key in label_keys:
											a									=				signature_names.index(__magic_name__							)
											a									=				label_key
										a									=				sorted(tuple_index_mapping.items()							)
										# Initialize a list with their default values, update the values and convert to a tuple
										a									=				[]

										for name in signature_names:
											if name != "kwargs":
												list_input.append(signature[name].default							)

										for index, value in sorted_tuple_index_mapping:
											a									=				prepared_for_class[value]

										a									=				tuple(__magic_name__							)

										# Send to model
										a									=				model(tuple_input[:-1]							)[0]

										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								(
								    (
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					
								)									=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(__magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								(
								    (
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					
								)									=				self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									a									=				type
									self.model_tester.create_and_check_model(__magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								(
								    (
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					
								)									=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(
								    __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								(
								    (
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					
								)									=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(
								    __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								(
								    (
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					(
								    a
								)							,					
								)									=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(
								    __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__						, __magic_name__							)





							@slow
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a									=				TFLayoutLMvaModel.from_pretrained(__magic_name__							)
									self.assertIsNotNone(__magic_name__							)







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



@require_tf
class 						__lowerCAmelCase      (      unittest.TestCase					):
							@cached_property
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								return LayoutLMvaImageProcessor(apply_ocr=__magic_name__							) if is_vision_available() else None





							@slow
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								a									=				TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""							)

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

								a									=				tf.constant([[1, 2]]							)
								a									=				tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]							)						, axis=0							)

								# forward pass
								a									=				model(input_ids=__magic_name__						, bbox=__magic_name__						, pixel_values=__magic_name__						, training=__magic_name__							)

								# verify the logits
								a									=				(1, 199, 768)
								self.assertEqual(outputs.last_hidden_state.shape						, __magic_name__							)

								a									=				tf.constant(
								    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							)

								self.assertTrue(np.allclose(outputs.last_hidden_state[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__							)

