





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							(						__lowercase    ):
      _snake_case				  =['''image_processor''', '''tokenizer''']
      _snake_case				  ='''Pix2StructImageProcessor'''
      _snake_case				  =('''T5Tokenizer''', '''T5TokenizerFast''')



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    List[str]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =False
         super().__init__(_lowerCAmelCase		,	_lowerCAmelCase							)



      def __call__(						self:    Tuple		,	_lowerCAmelCase:    str=None		,	_lowerCAmelCase:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    Union[bool, str, PaddingStrategy] = False		,	_lowerCAmelCase:    Union[bool, str, TruncationStrategy] = None		,	_lowerCAmelCase:    Optional[int] = None		,	_lowerCAmelCase:    Optional[int] = 2048		,	_lowerCAmelCase:    int = 0		,	_lowerCAmelCase:    Optional[int] = None		,	_lowerCAmelCase:    Optional[bool] = None		,	_lowerCAmelCase:    bool = False		,	_lowerCAmelCase:    bool = False		,	_lowerCAmelCase:    bool = False		,	_lowerCAmelCase:    bool = False		,	_lowerCAmelCase:    bool = False		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    Optional[Union[str, TensorType]] = None		,	**_lowerCAmelCase:    int		,	)    ->     BatchEncoding:
         '''simple docstring'''

         if images is None and text is None:
            raise ValueError("You have to specify either images or text."							)

         # Get only text
         if images is None and not self.image_processor.is_vqa:
            UpperCAmelCase_           =self.tokenizer
            UpperCAmelCase_           =self.tokenizer(
                text=_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase		,	padding=_lowerCAmelCase		,	truncation=_lowerCAmelCase		,	max_length=_lowerCAmelCase		,	stride=_lowerCAmelCase		,	pad_to_multiple_of=_lowerCAmelCase		,	return_attention_mask=_lowerCAmelCase		,	return_overflowing_tokens=_lowerCAmelCase		,	return_special_tokens_mask=_lowerCAmelCase		,	return_offsets_mapping=_lowerCAmelCase		,	return_token_type_ids=_lowerCAmelCase		,	return_length=_lowerCAmelCase		,	verbose=_lowerCAmelCase		,	return_tensors=_lowerCAmelCase		,	**_lowerCAmelCase		,	)
            return text_encoding

         if not self.image_processor.is_vqa:
            # add pixel_values
            UpperCAmelCase_           =self.image_processor(
                _lowerCAmelCase		,	return_tensors=_lowerCAmelCase		,	max_patches=_lowerCAmelCase		,	**_lowerCAmelCase							)
         else:
            # add pixel_values and bbox
            UpperCAmelCase_           =self.image_processor(
                _lowerCAmelCase		,	return_tensors=_lowerCAmelCase		,	max_patches=_lowerCAmelCase		,	header_text=_lowerCAmelCase		,	**_lowerCAmelCase							)

         if text is not None and not self.image_processor.is_vqa:
            UpperCAmelCase_           =self.tokenizer(
                text=_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase		,	padding=_lowerCAmelCase		,	truncation=_lowerCAmelCase		,	max_length=_lowerCAmelCase		,	stride=_lowerCAmelCase		,	pad_to_multiple_of=_lowerCAmelCase		,	return_attention_mask=_lowerCAmelCase		,	return_overflowing_tokens=_lowerCAmelCase		,	return_special_tokens_mask=_lowerCAmelCase		,	return_offsets_mapping=_lowerCAmelCase		,	return_token_type_ids=_lowerCAmelCase		,	return_length=_lowerCAmelCase		,	verbose=_lowerCAmelCase		,	return_tensors=_lowerCAmelCase		,	**_lowerCAmelCase		,	)

            if "attention_mask" in text_encoding:
               UpperCAmelCase_           =text_encoding.pop("attention_mask"							)
            if "input_ids" in text_encoding:
               UpperCAmelCase_           =text_encoding.pop("input_ids"							)
         else:
            UpperCAmelCase_           =None

         if text_encoding is not None:
            encoding_image_processor.update(_lowerCAmelCase							)

         return encoding_image_processor



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	*_lowerCAmelCase:    Any		,	**_lowerCAmelCase:    int							)    ->     Optional[Any]:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    str		,	*_lowerCAmelCase:    List[str]		,	**_lowerCAmelCase:    List[str]							)    ->     int:
         '''simple docstring'''

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






      @property
      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.tokenizer.model_input_names
         UpperCAmelCase_           =self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)










'''simple docstring'''


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor

class      A       (						unittest.TestCase       ):







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=7				,     lowerCAmelCase_			:    Dict=3				,     lowerCAmelCase_			:    List[Any]=18				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    Optional[int]=4_00				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    List[str]=True				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							size if size is not None else {'''height''': 18, '''width''': 18}
														_a      	=							parent
														_a      	=							batch_size
														_a      	=							num_channels
														_a      	=							image_size
														_a      	=							min_resolution
														_a      	=							max_resolution
														_a      	=							do_resize
														_a      	=							size
														_a      	=							do_normalize







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
														            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
														        ]   ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}



@require_torch
@require_vision
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							ImageGPTImageProcessor if is_vision_available() else None







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessingTester(self   )







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''clusters'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_resize'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''size'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_normalize'''   )   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict   )
														self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}   )

														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42   )
														self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														_a      	=							json.loads(image_processor.to_json_string()   )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     obj[key]   )   )
																					else:
																												self.assertEqual(obj[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							os.path.join(lowerCAmelCase_				,     '''image_processor.json'''   )
																					image_processor_first.to_json_file(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_json_file(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_pretrained(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							@unittest.skip('''ImageGPT requires clusters at initialization'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

							_a      	=							Image.open(dataset[4]['''file'''] )
							_a      	=							Image.open(dataset[5]['''file'''] )

							_a      	=							[imagea, imagea]

							return images

@require_vision
@require_torch
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

														_a      	=							prepare_images()

														# test non-batched
														_a      	=							image_processing(images[0]				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (1, 10_24)   )

														_a      	=							[3_06, 1_91, 1_91]
														self.assertEqual(encoding.input_ids[0, :3].tolist()				,     lowerCAmelCase_   )

														# test batched
														_a      	=							image_processing(lowerCAmelCase_				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (2, 10_24)   )

														_a      	=							[3_03, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     lowerCAmelCase_   )

