



"""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 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	['image_processor', 'tokenizer']
	SCREAMING_SNAKE_CASE          =	'LayoutLMv2ImageProcessor'
	SCREAMING_SNAKE_CASE          =	('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')

	def __init__(     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Tuple=None       ,			_SCREAMING_SNAKE_CASE:		List[Any]=None       ,			**_SCREAMING_SNAKE_CASE:		List[str])				->       Any:

				"""simple docstring"""

				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."       ,			_SCREAMING_SNAKE_CASE       ,			)
							__lowerCAmelCase      :  Optional[int]												=       kwargs.pop("feature_extractor")

				__lowerCAmelCase      :  int												=       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__(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

	def __call__(     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,			_SCREAMING_SNAKE_CASE:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,			_SCREAMING_SNAKE_CASE:		Union[List[List[int]], List[List[List[int]]]] = None       ,			_SCREAMING_SNAKE_CASE:		Optional[Union[List[int], List[List[int]]]] = None       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			_SCREAMING_SNAKE_CASE:		Union[bool, str, PaddingStrategy] = False       ,			_SCREAMING_SNAKE_CASE:		Union[bool, str, TruncationStrategy] = None       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None       ,			_SCREAMING_SNAKE_CASE:		int = 0       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None       ,			_SCREAMING_SNAKE_CASE:		Optional[bool] = None       ,			_SCREAMING_SNAKE_CASE:		Optional[bool] = None       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			_SCREAMING_SNAKE_CASE:		Optional[Union[str, TensorType]] = None       ,			**_SCREAMING_SNAKE_CASE:		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
				__lowerCAmelCase      :  List[Any]												=       self.image_processor(images=_SCREAMING_SNAKE_CASE       ,			return_tensors=_SCREAMING_SNAKE_CASE)

				# second, apply the tokenizer
				if text is not None and self.image_processor.apply_ocr and text_pair is None:
							if isinstance(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE):
										__lowerCAmelCase      :  Tuple												=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
							__lowerCAmelCase      :  Optional[Any]												=       features["words"]

				__lowerCAmelCase      :  Union[str, 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=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			padding=_SCREAMING_SNAKE_CASE       ,			truncation=_SCREAMING_SNAKE_CASE       ,			max_length=_SCREAMING_SNAKE_CASE       ,			stride=_SCREAMING_SNAKE_CASE       ,			pad_to_multiple_of=_SCREAMING_SNAKE_CASE       ,			return_token_type_ids=_SCREAMING_SNAKE_CASE       ,			return_attention_mask=_SCREAMING_SNAKE_CASE       ,			return_overflowing_tokens=_SCREAMING_SNAKE_CASE       ,			return_special_tokens_mask=_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			return_length=_SCREAMING_SNAKE_CASE       ,			verbose=_SCREAMING_SNAKE_CASE       ,			return_tensors=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)

				# add pixel values
				__lowerCAmelCase      :  Dict												=       features.pop("pixel_values")
				if return_overflowing_tokens is True:
							__lowerCAmelCase      :  Dict												=       self.get_overflowing_images(_SCREAMING_SNAKE_CASE       ,			encoded_inputs["overflow_to_sample_mapping"])
				__lowerCAmelCase      :  Union[str, Any]												=       images

				return encoded_inputs

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       []
				for sample_idx in overflow_to_sample_mapping:
							images_with_overflow.append(images[sample_idx])

				if len(_SCREAMING_SNAKE_CASE) != len(_SCREAMING_SNAKE_CASE):
							raise ValueError(
							    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
							    F""" {len(_SCREAMING_SNAKE_CASE)} and {len(_SCREAMING_SNAKE_CASE)}""")

				return images_with_overflow

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			*_SCREAMING_SNAKE_CASE:		int       ,			**_SCREAMING_SNAKE_CASE:		List[Any])				->       Tuple:

				"""simple docstring"""

				return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			*_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			**_SCREAMING_SNAKE_CASE:		Optional[Any])				->       Optional[Any]:

				"""simple docstring"""

				return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       List[str]:

				"""simple docstring"""

				return ["input_ids", "bbox", "attention_mask", "image"]

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[str]:

				"""simple docstring"""

				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,			_SCREAMING_SNAKE_CASE       ,			)
				return self.image_processor_class







	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       int:

				"""simple docstring"""

				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,			_SCREAMING_SNAKE_CASE       ,			)
				return self.image_processor




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)