


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:
     snake_case       :					Any        =  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 	_snake_case						(   unittest.TestCase							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= tempfile.mkdtemp()

       a		:str											= PixaStructImageProcessor()
       a		:Dict											= TaTokenizer.from_pretrained('''t5-small'''					)

       a		:Optional[Any]											= PixaStructProcessor(_lowerCamelCase      ,						_lowerCamelCase					)

       processor.save_pretrained(self.tmpdirname					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,						**_lowerCamelCase					).tokenizer




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,						**_lowerCamelCase					).image_processor




 def  SCREAMING_SNAKE_CASE__       (	self					):
       shutil.rmtree(self.tmpdirname					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Union[str, Any]											= [np.random.randint(255      ,						size=(3, 30, 400)      ,						dtype=np.uinta					)]

       a		:str											= [Image.fromarray(np.moveaxis(_lowerCamelCase      ,						0      ,						-1					)					) for x in image_inputs]

       return image_inputs




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= PixaStructProcessor(tokenizer=self.get_tokenizer()      ,						image_processor=self.get_image_processor()					)
       processor.save_pretrained(self.tmpdirname					)

       a		:Optional[int]											= self.get_tokenizer(bos_token='''(BOS)'''      ,						eos_token='''(EOS)'''					)
       a		:Dict											= self.get_image_processor(do_normalize=_lowerCamelCase      ,						padding_value=1.0					)

       a		:int											= PixaStructProcessor.from_pretrained(
           self.tmpdirname      ,						bos_token='''(BOS)'''      ,						eos_token='''(EOS)'''      ,						do_normalize=_lowerCamelCase      ,						padding_value=1.0					)

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

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= self.get_image_processor()
       a		:int											= self.get_tokenizer()

       a		:str											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Optional[Any]											= self.prepare_image_inputs()

       a		:Any											= image_processor(_lowerCamelCase      ,						return_tensors='''np'''					)
       a		:List[Any]											= processor(images=_lowerCamelCase      ,						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  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= self.get_image_processor()
       a		:Optional[Any]											= self.get_tokenizer()

       a		:Dict											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Union[str, Any]											= '''lower newer'''

       a		:Any											= processor(text=_lowerCamelCase					)

       a		:Optional[Any]											= tokenizer(_lowerCamelCase      ,						return_token_type_ids=_lowerCamelCase      ,						add_special_tokens=_lowerCamelCase					)

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= self.get_image_processor()
       a		:Optional[Any]											= self.get_tokenizer()

       a		:Any											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Any											= '''lower newer'''
       a		:int											= self.prepare_image_inputs()

       a		:Dict											= processor(text=_lowerCamelCase      ,						images=_lowerCamelCase					)

       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(_lowerCamelCase					):
             processor()




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= self.get_image_processor()
       a		:Tuple											= self.get_tokenizer()

       a		:Optional[int]											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Union[str, Any]											= '''lower newer'''
       a		:str											= self.prepare_image_inputs()

       a		:Any											= processor(text=_lowerCamelCase      ,						images=_lowerCamelCase					)

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

       # without text input
       for i, max_patch in enumerate(_lowerCamelCase					):
             a		:str											= processor(images=_lowerCamelCase      ,						max_patches=_lowerCamelCase					)
             self.assertEqual(inputs['''flattened_patches'''][0].shape[0]      ,						_lowerCamelCase					)
             self.assertEqual(inputs['''flattened_patches'''][0].shape[1]      ,						expected_hidden_size[i]					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= self.get_image_processor()
       a		:Tuple											= self.get_tokenizer()

       a		:List[str]											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:str											= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       a		:List[str]											= processor.batch_decode(_lowerCamelCase					)
       a		:str											= tokenizer.batch_decode(_lowerCamelCase					)

       self.assertListEqual(_lowerCamelCase      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= self.get_image_processor()
       a		:List[str]											= self.get_tokenizer()

       a		:Any											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Any											= '''lower newer'''
       a		:Optional[Any]											= self.prepare_image_inputs()

       a		:Dict											= processor(text=_lowerCamelCase      ,						images=_lowerCamelCase					)

       # 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''']					)

       a		:Dict											= processor(text=_lowerCamelCase					)

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









from typing import Optional, Union

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







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'MarkupLMFeatureExtractor'
 SCREAMING_SNAKE_CASE__   = ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 SCREAMING_SNAKE_CASE__   = True




 def __call__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = 0      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       # 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`'''					)

             a		:int											= self.feature_extractor(_lowerCamelCase					)
             a		:Tuple											= features['''nodes''']
             a		:Dict											= 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(_lowerCamelCase      ,						_lowerCamelCase					):
                   a		:Tuple											= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

       return encoded_inputs




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.batch_decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.tokenizer.model_input_names
       return tokenizer_input_names





