
"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__UpperCAmelCase 					=    False

if is_vision_available():
				from PIL import Image

				from transformers import (
				    AutoProcessor,
				    PixaStructImageProcessor,
				    PixaStructProcessor,
				    PreTrainedTokenizerFast,
				    TaTokenizer,
				)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,					reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."      ,					)
@require_vision
@require_torch
class      _SCREAMING_SNAKE_CASE   (     unittest.TestCase				):






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :Optional[Any]     	=    tempfile.mkdtemp()

						lowerCAmelCase_       :int     	=    PixaStructImageProcessor()
						lowerCAmelCase_       :List[str]     	=    TaTokenizer.from_pretrained("""t5-small"""			)

						lowerCAmelCase_       :List[Any]     	=    PixaStructProcessor(__A			,  __A			)

						processor.save_pretrained(self.tmpdirname			)






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	str:
						return AutoProcessor.from_pretrained(self.tmpdirname			,  **__A			).tokenizer






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	List[Any]:
						return AutoProcessor.from_pretrained(self.tmpdirname			,  **__A			).image_processor






			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						shutil.rmtree(self.tmpdirname			)






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Optional[Any]     	=    [np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta			)]

						lowerCAmelCase_       :int     	=    [Image.fromarray(np.moveaxis(__A			,  0			,  -1			)			) for x in image_inputs]

						return image_inputs






			def 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :List[Any]     	=    PixaStructProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()			)
						processor.save_pretrained(self.tmpdirname			)

						lowerCAmelCase_       :Tuple     	=    self.get_tokenizer(bos_token="""(BOS)"""			,  eos_token="""(EOS)"""			)
						lowerCAmelCase_       :Tuple     	=    self.get_image_processor(do_normalize=__A			,  padding_value=1.0			)

						lowerCAmelCase_       :Any     	=    PixaStructProcessor.from_pretrained(
						    self.tmpdirname			,  bos_token="""(BOS)"""			,  eos_token="""(EOS)"""			,  do_normalize=__A			,  padding_value=1.0			)

						self.assertEqual(processor.tokenizer.get_vocab()			,  tokenizer_add_kwargs.get_vocab()			)
						self.assertIsInstance(processor.tokenizer			,  __A			)

						self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()			)
						self.assertIsInstance(processor.image_processor			,  __A			)






			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_       :Any     	=    self.get_image_processor()
						lowerCAmelCase_       :List[str]     	=    self.get_tokenizer()

						lowerCAmelCase_       :List[Any]     	=    PixaStructProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :Any     	=    self.prepare_image_inputs()

						lowerCAmelCase_       :Tuple     	=    image_processor(__A			,  return_tensors="""np"""			)
						lowerCAmelCase_       :Optional[int]     	=    processor(images=__A			,  return_tensors="""np"""			)

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2			)






			def 					__lowerCAmelCase					(   self			)     ->	str:
						lowerCAmelCase_       :int     	=    self.get_image_processor()
						lowerCAmelCase_       :Any     	=    self.get_tokenizer()

						lowerCAmelCase_       :Optional[Any]     	=    PixaStructProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :Optional[Any]     	=    """lower newer"""

						lowerCAmelCase_       :Any     	=    processor(text=__A			)

						lowerCAmelCase_       :List[str]     	=    tokenizer(__A			,  return_token_type_ids=__A			,  add_special_tokens=__A			)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]			,  encoded_processor[key]			)






			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						lowerCAmelCase_       :List[str]     	=    self.get_image_processor()
						lowerCAmelCase_       :Optional[int]     	=    self.get_tokenizer()

						lowerCAmelCase_       :Dict     	=    PixaStructProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :int     	=    """lower newer"""
						lowerCAmelCase_       :Optional[Any]     	=    self.prepare_image_inputs()

						lowerCAmelCase_       :Optional[int]     	=    processor(text=__A			,  images=__A			)

						self.assertListEqual(
						    list(inputs.keys()			)			,  ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]			)

						# test if it raises when no input is passed
						with pytest.raises(__A			):
									processor()






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :Dict     	=    self.get_image_processor()
						lowerCAmelCase_       :Dict     	=    self.get_tokenizer()

						lowerCAmelCase_       :Optional[int]     	=    PixaStructProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :Any     	=    """lower newer"""
						lowerCAmelCase_       :Any     	=    self.prepare_image_inputs()

						lowerCAmelCase_       :Union[str, Any]     	=    processor(text=__A			,  images=__A			)

						lowerCAmelCase_       :Dict     	=    [512, 1024, 2048, 4096]
						lowerCAmelCase_       :List[str]     	=    [770, 770, 770, 770]
						# with text
						for i, max_patch in enumerate(__A			):
									lowerCAmelCase_       :Tuple     	=    processor(text=__A			,  images=__A			,  max_patches=__A			)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[0]			,  __A			)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[1]			,  expected_hidden_size[i]			)

						# without text input
						for i, max_patch in enumerate(__A			):
									lowerCAmelCase_       :Any     	=    processor(images=__A			,  max_patches=__A			)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[0]			,  __A			)
									self.assertEqual(inputs["""flattened_patches"""][0].shape[1]			,  expected_hidden_size[i]			)






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Optional[Any]     	=    self.get_image_processor()
						lowerCAmelCase_       :Optional[int]     	=    self.get_tokenizer()

						lowerCAmelCase_       :Optional[Any]     	=    PixaStructProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :Optional[int]     	=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase_       :List[Any]     	=    processor.batch_decode(__A			)
						lowerCAmelCase_       :int     	=    tokenizer.batch_decode(__A			)

						self.assertListEqual(__A			,  __A			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :Optional[int]     	=    self.get_image_processor()
						lowerCAmelCase_       :List[str]     	=    self.get_tokenizer()

						lowerCAmelCase_       :Union[str, Any]     	=    PixaStructProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :Optional[Any]     	=    """lower newer"""
						lowerCAmelCase_       :Optional[Any]     	=    self.prepare_image_inputs()

						lowerCAmelCase_       :Optional[Any]     	=    processor(text=__A			,  images=__A			)

						# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
						self.assertListEqual(
						    list(inputs.keys()			)			,  ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]			)

						lowerCAmelCase_       :Optional[Any]     	=    processor(text=__A			)

						# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
						self.assertListEqual(list(inputs.keys()			)			,  ["""input_ids""", """attention_mask"""]			)








"""simple docstring"""






from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Union[str, Any]												=		"MarkupLMFeatureExtractor"
			UpperCAmelCase_    :Optional[Any]												=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
			UpperCAmelCase_    :List[Any]												=		True






			def __call__(   self			,  __A=None			,  __A=None			,  __A=None			,  __A=None			,  __A=None			,  __A = True			,  __A = False			,  __A = None			,  __A = None			,  __A = 0			,  __A = None			,  __A = None			,  __A = None			,  __A = False			,  __A = False			,  __A = False			,  __A = False			,  __A = True			,  __A = None			,  **__A			,  )     ->	BatchEncoding:
						# first, create nodes and xpaths
						if self.parse_html:
									if html_strings is None:
												raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""			)

									if nodes is not None or xpaths is not None or node_labels is not None:
												raise ValueError(
												    """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""			)

									lowerCAmelCase_       :Union[str, Any]     	=    self.feature_extractor(__A			)
									lowerCAmelCase_       :Any     	=    features["""nodes"""]
									lowerCAmelCase_       :Union[str, Any]     	=    features["""xpaths"""]
						else:
									if html_strings is not None:
												raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""			)
									if nodes is None or xpaths is None:
												raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""			)

        # # second, apply the tokenizer
						if questions is not None and self.parse_html:
									if isinstance(__A			,  __A			):
												lowerCAmelCase_       :Dict     	=    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

						lowerCAmelCase_       :Optional[Any]     	=    self.tokenizer(
						    text=questions if questions is not None else nodes			,  text_pair=nodes if questions is not None else None			,  xpaths=__A			,  node_labels=__A			,  add_special_tokens=__A			,  padding=__A			,  truncation=__A			,  max_length=__A			,  stride=__A			,  pad_to_multiple_of=__A			,  return_token_type_ids=__A			,  return_attention_mask=__A			,  return_overflowing_tokens=__A			,  return_special_tokens_mask=__A			,  return_offsets_mapping=__A			,  return_length=__A			,  verbose=__A			,  return_tensors=__A			,  **__A			,  )

						return encoded_inputs






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	Tuple:
						return self.tokenizer.batch_decode(*__A			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	str:
						return self.tokenizer.decode(*__A			,  **__A			)






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Any     	=    self.tokenizer.model_input_names
						return tokenizer_input_names






