code: 
  '''simple docstring'''
  import warnings
  from typing import List, Optional, Union
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from ...utils import TensorType
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	["""image_processor""", """tokenizer"""]
  		lowerCamelCase           =	"""LayoutLMv2ImageProcessor"""
  		lowerCamelCase           =	("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  
  
  
  		def __init__(			self							: Optional[int]     ,  UpperCamelCase__							: List[str]=None     ,  UpperCamelCase__							: Optional[int]=None     ,  **UpperCamelCase__							: Optional[Any]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	None
  				if "feature_extractor" in kwargs:
  						warnings.warn(
  						    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
  						    ''' instead.'''     ,  UpperCamelCase__     ,  )
  						snake_case     :					Dict     =	kwargs.pop('''feature_extractor'''			)
  
  				snake_case     :					str     =	image_processor if image_processor is not None else feature_extractor
  				if image_processor is None:
  						raise ValueError('''You need to specify an `image_processor`.'''			)
  				if tokenizer is None:
  						raise ValueError('''You need to specify a `tokenizer`.'''			)
  
  				super().__init__(UpperCamelCase__     ,  UpperCamelCase__			)
  
  
  
  		def __call__(			self							: Any     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,  UpperCamelCase__							: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,  UpperCamelCase__							: Union[List[List[int]], List[List[List[int]]]] = None     ,  UpperCamelCase__							: Optional[Union[List[int], List[List[int]]]] = None     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Union[bool, str, PaddingStrategy] = False     ,  UpperCamelCase__							: Union[bool, str, TruncationStrategy] = False     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: int = 0     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: Optional[bool] = None     ,  UpperCamelCase__							: Optional[bool] = None     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase__							: Optional[int]     ,  )	-> BatchEncoding:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self.image_processor.apply_ocr and (boxes is not None):
  						raise ValueError(
  						    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''			)
  
  				if self.image_processor.apply_ocr and (word_labels is not None):
  						raise ValueError(
  						    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''			)
  
  				if return_overflowing_tokens is True and return_offsets_mapping is False:
  						raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''			)
  
  				# first, apply the image processor
  				snake_case     :					List[Any]     =	self.image_processor(images=UpperCamelCase__     ,  return_tensors=UpperCamelCase__			)
  
  				# second, apply the tokenizer
  				if text is not None and self.image_processor.apply_ocr and text_pair is None:
  						if isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  								snake_case     :					Optional[Any]     =	[text]  # add batch dimension (as the image processor always adds a batch dimension)
  						snake_case     :					Any     =	features['''words''']
  
  				snake_case     :					List[Any]     =	self.tokenizer(
  				    text=text if text is not None else features['''words''']     ,  text_pair=text_pair if text_pair is not None else None     ,  boxes=boxes if boxes is not None else features['''boxes''']     ,  word_labels=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  stride=UpperCamelCase__     ,  pad_to_multiple_of=UpperCamelCase__     ,  return_token_type_ids=UpperCamelCase__     ,  return_attention_mask=UpperCamelCase__     ,  return_overflowing_tokens=UpperCamelCase__     ,  return_special_tokens_mask=UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  return_length=UpperCamelCase__     ,  verbose=UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     ,  )
  
  				# add pixel values
  				snake_case     :					Optional[Any]     =	features.pop('''pixel_values'''			)
  				if return_overflowing_tokens is True:
  						snake_case     :					int     =	self.get_overflowing_images(UpperCamelCase__     ,  encoded_inputs['''overflow_to_sample_mapping''']			)
  				snake_case     :					List[str]     =	images
  
  				return encoded_inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	[]
  				for sample_idx in overflow_to_sample_mapping:
  						images_with_overflow.append(images[sample_idx]			)
  
  				if len(UpperCamelCase__			) != len(UpperCamelCase__			):
  						raise ValueError(
  						    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
  						    f' {len(UpperCamelCase__			)} and {len(UpperCamelCase__			)}'			)
  
  				return images_with_overflow
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  *UpperCamelCase__							: str     ,  **UpperCamelCase__							: Any			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.batch_decode(*UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  *UpperCamelCase__							: Optional[Any]     ,  **UpperCamelCase__							: str			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.decode(*UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				warnings.warn(
  				    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,  UpperCamelCase__     ,  )
  				return self.image_processor_class
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: int			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				warnings.warn(
  				    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,  UpperCamelCase__     ,  )
  				return self.image_processor
  
  
  
  
style_context: 
  '''simple docstring'''
  from __future__ import annotations
  
  import unittest
  
  from transformers import CTRLConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  						import tensorflow as tf
  
  						from transformers.models.ctrl.modeling_tf_ctrl import (
  						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    TFCTRLForSequenceClassification,
  						    TFCTRLLMHeadModel,
  						    TFCTRLModel,
  						)
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[Any]     ,  )	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	parent
  				snake_case     :					str     =	13
  				snake_case     :					Any     =	7
  				snake_case     :					str     =	True
  				snake_case     :					int     =	True
  				snake_case     :					Optional[Any]     =	True
  				snake_case     :					Optional[Any]     =	True
  				snake_case     :					str     =	True
  				snake_case     :					Any     =	99
  				snake_case     :					int     =	32
  				snake_case     :					str     =	2
  				snake_case     :					Optional[Any]     =	4
  				snake_case     :					str     =	37
  				snake_case     :					Optional[int]     =	'''gelu'''
  				snake_case     :					List[Any]     =	0.1
  				snake_case     :					Dict     =	0.1
  				snake_case     :					str     =	512
  				snake_case     :					List[str]     =	16
  				snake_case     :					Tuple     =	2
  				snake_case     :					List[str]     =	0.02
  				snake_case     :					Optional[int]     =	3
  				snake_case     :					Optional[int]     =	4
  				snake_case     :					List[str]     =	None
  				snake_case     :					List[str]     =	self.vocab_size - 1
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)
  
  				snake_case     :					Optional[Any]     =	None
  				if self.use_input_mask:
  						snake_case     :					List[str]     =	random_attention_mask([self.batch_size, self.seq_length]			)
  
  				snake_case     :					str     =	None
  				if self.use_token_type_ids:
  						snake_case     :					str     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size			)
  
  				snake_case     :					Optional[int]     =	None
  				if self.use_mc_token_ids:
  						snake_case     :					List[str]     =	ids_tensor([self.batch_size, self.num_choices]     ,  self.seq_length			)
  
  				snake_case     :					Dict     =	None
  				snake_case     :					Any     =	None
  				snake_case     :					str     =	None
  				if self.use_labels:
  						snake_case     :					Optional[Any]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
  						snake_case     :					Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels			)
  						snake_case     :					List[str]     =	ids_tensor([self.batch_size]     ,  self.num_choices			)
  
  				snake_case     :					List[Any]     =	CTRLConfig(
  				    vocab_size=self.vocab_size     ,  n_embd=self.hidden_size     ,  n_layer=self.num_hidden_layers     ,  n_head=self.num_attention_heads     ,  n_positions=self.max_position_embeddings     ,  pad_token_id=self.pad_token_id     ,  )
  
  				snake_case     :					List[Any]     =	ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,  2			)
  
  				return (
  				    config,
  				    input_ids,
  				    input_mask,
  				    head_mask,
  				    token_type_ids,
  				    mc_token_ids,
  				    sequence_labels,
  				    token_labels,
  				    choice_labels,
  				)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: str			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	TFCTRLModel(config=UpperCamelCase__			)
  				snake_case     :					Union[str, Any]     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  				snake_case     :					Optional[int]     =	model(UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	[input_ids, None, input_mask]  # None is the input for 'past'
  				snake_case     :					str     =	model(UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	model(UpperCamelCase__			)
  
  				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: List[str]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	TFCTRLLMHeadModel(config=UpperCamelCase__			)
  				snake_case     :					int     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  				snake_case     :					Any     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Tuple     ,  *UpperCamelCase__							: Tuple			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	self.num_labels
  				snake_case     :					Optional[int]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
  				snake_case     :					Any     =	{
  				    '''input_ids''': input_ids,
  				    '''token_type_ids''': token_type_ids,
  				    '''labels''': sequence_labels,
  				}
  				snake_case     :					Dict     =	TFCTRLForSequenceClassification(UpperCamelCase__			)
  				snake_case     :					int     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	self.prepare_config_and_inputs()
  
  				(
  				    (
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,
  				)     :					str     =	config_and_inputs
  
  				snake_case     :					Any     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
  		lowerCamelCase           =	(TFCTRLLMHeadModel,) if is_tf_available() else ()
  		lowerCamelCase           =	(
  		    {
  		        """feature-extraction""": TFCTRLModel,
  		        """text-classification""": TFCTRLForSequenceClassification,
  		        """text-generation""": TFCTRLLMHeadModel,
  		        """zero-shot""": TFCTRLForSequenceClassification,
  		    }
  		    if is_tf_available()
  		    else {}
  		)
  		lowerCamelCase           =	False
  		lowerCamelCase           =	False
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Dict			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
  						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
  						# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
  						# config could not be created.
  						return True
  
  				return False
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	TFCTRLModelTester(self			)
  				snake_case     :					Dict     =	ConfigTester(self     ,  config_class=UpperCamelCase__     ,  n_embd=37			)
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case ,snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()
  				snake_case     :					int     =	[TFCTRLLMHeadModel]
  				snake_case     :					Tuple     =	[TFCTRLForSequenceClassification]
  
  				for model_class in self.all_model_classes:
  						snake_case     :					List[Any]     =	model_class(UpperCamelCase__			)
  						model.build()  # may be needed for the get_bias() call below
  						assert isinstance(model.get_input_embeddings()     ,  tf.keras.layers.Layer			)
  
  						if model_class in list_lm_models:
  								snake_case     :					Optional[Any]     =	model.get_output_embeddings()
  								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
  								snake_case     :					Any     =	model.get_bias()
  								assert isinstance(UpperCamelCase__     ,  UpperCamelCase__			)
  								for k, v in name.items():
  										assert isinstance(UpperCamelCase__     ,  tf.Variable			)
  						elif model_class in list_other_models_with_output_ebd:
  								snake_case     :					List[Any]     =	model.get_output_embeddings()
  								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
  								snake_case     :					int     =	model.get_bias()
  								assert name is None
  						else:
  								snake_case     :					Tuple     =	model.get_output_embeddings()
  								assert x is None
  								snake_case     :					Dict     =	model.get_bias()
  								assert name is None
  
  
  
  		@slow
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						snake_case     :					str     =	TFCTRLModel.from_pretrained(UpperCamelCase__			)
  						self.assertIsNotNone(UpperCamelCase__			)
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		@slow
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	TFCTRLLMHeadModel.from_pretrained('''ctrl'''			)
  				snake_case     :					Optional[Any]     =	tf.convert_to_tensor([[1_1859, 0, 1611, 8]]     ,  dtype=tf.intaa			)  # Legal the president is
  				snake_case     :					Dict     =	[
  				    1_1859,
  				    0,
  				    1611,
  				    8,
  				    5,
  				    150,
  				    2_6449,
  				    2,
  				    19,
  				    348,
  				    469,
  				    3,
  				    2595,
  				    48,
  				    2_0740,
  				    24_6533,
  				    24_6533,
  				    19,
  				    30,
  				    5,
  				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
  				snake_case     :					int     =	model.generate(UpperCamelCase__     ,  do_sample=UpperCamelCase__			)
  				self.assertListEqual(output_ids[0].numpy().tolist()     ,  UpperCamelCase__			)
  
  
  