

'''simple docstring'''



from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
		import PIL

# soft dependency
if is_pytesseract_available():
		import pytesseract

__snake_case			:     int										=      logging.get_logger(__name__)
def 						__lowerCamelCase	(       __snake_case					:		Optional[int],		__snake_case					:		Dict,		__snake_case					:		Tuple	)	->							Dict:






						"""simple docstring"""





						return [
						    int(1_000 * (box[0] / width)	),
						    int(1_000 * (box[1] / height)	),
						    int(1_000 * (box[2] / width)	),
						    int(1_000 * (box[3] / height)	),
						]
def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		Optional[str],		__snake_case					:		Optional[str] = None	)	->							Union[str, Any]:






						"""simple docstring"""





						A__					:       List[Any]     =tesseract_config if tesseract_config is not None else """"""

						# apply OCR
						A__					:       Optional[int]     =to_pil_image(__snake_case	)
						A__   ,						A__					:       Optional[int]     =pil_image.size
						A__					:       Tuple     =pytesseract.image_to_data(__snake_case,		lang=__snake_case,		output_type="""dict""",		config=__snake_case	)
						A__   ,						A__   ,						A__   ,						A__   ,						A__					:       Any     =data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

						# filter empty words and corresponding coordinates
						A__					:       Any     =[idx for idx, word in enumerate(__snake_case	) if not word.strip()]
						A__					:       Optional[Any]     =[word for idx, word in enumerate(__snake_case	) if idx not in irrelevant_indices]
						A__					:       int     =[coord for idx, coord in enumerate(__snake_case	) if idx not in irrelevant_indices]
						A__					:       Tuple     =[coord for idx, coord in enumerate(__snake_case	) if idx not in irrelevant_indices]
						A__					:       Optional[Any]     =[coord for idx, coord in enumerate(__snake_case	) if idx not in irrelevant_indices]
						A__					:       Any     =[coord for idx, coord in enumerate(__snake_case	) if idx not in irrelevant_indices]

						# turn coordinates into (left, top, left+width, top+height) format
						A__					:       Optional[int]     =[]
						for x, y, w, h in zip(__snake_case,		__snake_case,		__snake_case,		__snake_case	):
												A__					:       Optional[Any]     =[x, y, x + w, y + h]
												actual_boxes.append(__snake_case	)

						# finally, normalize the bounding boxes
						A__					:       Optional[int]     =[]
						for box in actual_boxes:
												normalized_boxes.append(normalize_box(__snake_case,		__snake_case,		__snake_case	)	)

						assert len(__snake_case	) == len(__snake_case	), "Not as many words as there are bounding boxes"

						return words, normalized_boxes



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	['pixel_values']


			def __init__(				self	:						Dict		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Dict[str, int] = None		,      lowerCAmelCase_	:						PILImageResampling = PILImageResampling.BILINEAR		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[str] = None		,      lowerCAmelCase_	:						Optional[str] = ""		,      **lowerCAmelCase_	:						Optional[Any]		,      )      ->		None:





									'''simple docstring'''
									super().__init__(**lowerCAmelCase_			)
									A__					:       List[str]     =size if size is not None else {"""height""": 2_24, """width""": 2_24}
									A__					:       int     =get_size_dict(lowerCAmelCase_			)

									A__					:       Any     =do_resize
									A__					:       Optional[int]     =size
									A__					:       Optional[Any]     =resample
									A__					:       Dict     =apply_ocr
									A__					:       int     =ocr_lang
									A__					:       List[str]     =tesseract_config


			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						np.ndarray		,      lowerCAmelCase_	:						Dict[str, int]		,      lowerCAmelCase_	:						PILImageResampling = PILImageResampling.BILINEAR		,      lowerCAmelCase_	:						Optional[Union[str, ChannelDimension]] = None		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		np.ndarray:





									'''simple docstring'''
									A__					:       int     =get_size_dict(lowerCAmelCase_			)
									if "height" not in size or "width" not in size:
															raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"			)
									A__					:       Optional[int]     =(size["""height"""], size["""width"""])
									return resize(lowerCAmelCase_		,      size=lowerCAmelCase_		,      resample=lowerCAmelCase_		,      data_format=lowerCAmelCase_		,      **lowerCAmelCase_			)



			def lowercase__						(				self	:						List[Any]		,      lowerCAmelCase_	:						ImageInput		,      lowerCAmelCase_	:						bool = None		,      lowerCAmelCase_	:						Dict[str, int] = None		,      lowerCAmelCase_	:						PILImageResampling = None		,      lowerCAmelCase_	:						bool = None		,      lowerCAmelCase_	:						Optional[str] = None		,      lowerCAmelCase_	:						Optional[str] = None		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      lowerCAmelCase_	:						ChannelDimension = ChannelDimension.FIRST		,      **lowerCAmelCase_	:						Optional[Any]		,      )      ->		PIL.Image.Image:





									'''simple docstring'''
									A__					:       List[str]     =do_resize if do_resize is not None else self.do_resize
									A__					:       int     =size if size is not None else self.size
									A__					:       Tuple     =get_size_dict(lowerCAmelCase_			)
									A__					:       List[str]     =resample if resample is not None else self.resample
									A__					:       List[str]     =apply_ocr if apply_ocr is not None else self.apply_ocr
									A__					:       str     =ocr_lang if ocr_lang is not None else self.ocr_lang
									A__					:       Tuple     =tesseract_config if tesseract_config is not None else self.tesseract_config

									A__					:       Optional[int]     =make_list_of_images(lowerCAmelCase_			)

									if not valid_images(lowerCAmelCase_			):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""			)

									if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""			)

									# All transformations expect numpy arrays.
									A__					:       Dict     =[to_numpy_array(lowerCAmelCase_			) for image in images]

									if apply_ocr:
															requires_backends(self		,      """pytesseract"""			)
															A__					:       str     =[]
															A__					:       Any     =[]
															for image in images:
																					A__   ,						A__					:       str     =apply_tesseract(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)
																					words_batch.append(lowerCAmelCase_			)
																					boxes_batch.append(lowerCAmelCase_			)

									if do_resize:
															A__					:       List[str]     =[self.resize(image=lowerCAmelCase_		,      size=lowerCAmelCase_		,      resample=lowerCAmelCase_			) for image in images]

									# flip color channels from RGB to BGR (as Detectron2 requires this)
									A__					:       Tuple     =[flip_channel_order(lowerCAmelCase_			) for image in images]
									A__					:       Any     =[to_channel_dimension_format(lowerCAmelCase_		,      lowerCAmelCase_			) for image in images]

									A__					:       Optional[Any]     =BatchFeature(data={"""pixel_values""": images}		,      tensor_type=lowerCAmelCase_			)

									if apply_ocr:
															A__					:       int     =words_batch
															A__					:       Optional[Any]     =boxes_batch
									return data




'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[int]			)      ->		int:





									'''simple docstring'''
									A__					:       int     =tempfile.mkdtemp()

									# fmt: off
									A__					:       Optional[int]     =["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
									# fmt: on
									A__					:       List[Any]     =dict(zip(lowerCAmelCase_		,      range(len(lowerCAmelCase_			)			)			)			)
									A__					:       Tuple     =["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
									A__					:       int     ={"""unk_token""": """<unk>"""}

									A__					:       Optional[Any]     =os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]			)
									A__					:       int     =os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""merges_file"""]			)
									with open(self.vocab_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															fp.write(json.dumps(lowerCAmelCase_			) + """\n"""			)
									with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															fp.write("""\n""".join(lowerCAmelCase_			)			)

									A__					:       Dict     ={
									    """do_resize""": True,
									    """size""": 20,
									    """do_center_crop""": True,
									    """crop_size""": 18,
									    """do_normalize""": True,
									    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
									    """image_std""": [0.26862954, 0.26130258, 0.27577711],
									}
									A__					:       Dict     =os.path.join(self.tmpdirname		,      lowerCAmelCase_			)
									with open(self.image_processor_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															json.dump(lowerCAmelCase_		,      lowerCAmelCase_			)


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





									'''simple docstring'''
									return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Optional[Any]		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]		,      **lowerCAmelCase_	:						str			)      ->		Union[str, Any]:





									'''simple docstring'''
									return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									shutil.rmtree(self.tmpdirname			)


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





									'''simple docstring'''
									A__					:       Dict     =[np.random.randint(2_55		,      size=(3, 30, 4_00)		,      dtype=np.uinta			)]

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

									return image_inputs


			def lowercase__						(				self	:						Dict			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       int     =self.get_tokenizer()
									A__					:       Optional[int]     =self.get_rust_tokenizer()
									A__					:       Any     =self.get_image_processor()

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)
									processor_slow.save_pretrained(self.tmpdirname			)
									A__					:       Dict     =CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase_			)

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)
									processor_fast.save_pretrained(self.tmpdirname			)
									A__					:       Optional[int]     =CLIPSegProcessor.from_pretrained(self.tmpdirname			)

									self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()			)
									self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()			)
									self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()			)
									self.assertIsInstance(processor_slow.tokenizer		,      lowerCAmelCase_			)
									self.assertIsInstance(processor_fast.tokenizer		,      lowerCAmelCase_			)

									self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()			)
									self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()			)
									self.assertIsInstance(processor_slow.image_processor		,      lowerCAmelCase_			)
									self.assertIsInstance(processor_fast.image_processor		,      lowerCAmelCase_			)


			def lowercase__						(				self	:						str			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[str]     =CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()			)
									processor.save_pretrained(self.tmpdirname			)

									A__					:       List[Any]     =self.get_tokenizer(bos_token="""(BOS)"""		,      eos_token="""(EOS)"""			)
									A__					:       Union[str, Any]     =self.get_image_processor(do_normalize=lowerCAmelCase_		,      padding_value=1.0			)

									A__					:       Optional[int]     =CLIPSegProcessor.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 lowercase__						(				self	:						str			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.get_image_processor()
									A__					:       int     =self.get_tokenizer()

									A__					:       Optional[int]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Dict     =self.prepare_image_inputs()

									A__					:       List[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 lowercase__						(				self	:						Optional[int]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Any     =self.get_image_processor()
									A__					:       Optional[Any]     =self.get_tokenizer()

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Any     ="""lower newer"""

									A__					:       Optional[int]     =processor(text=lowerCAmelCase_			)

									A__					:       Optional[int]     =tokenizer(lowerCAmelCase_			)

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


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





									'''simple docstring'''
									A__					:       Any     =self.get_image_processor()
									A__					:       Optional[Any]     =self.get_tokenizer()

									A__					:       Union[str, Any]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Optional[Any]     ="""lower newer"""
									A__					:       List[str]     =self.prepare_image_inputs()

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

									self.assertListEqual(list(inputs.keys()			)		,      ["""input_ids""", """attention_mask""", """pixel_values"""]			)

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


			def lowercase__						(				self	:						Any			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.get_image_processor()
									A__					:       List[str]     =self.get_tokenizer()

									A__					:       Optional[int]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Tuple     =self.prepare_image_inputs()
									A__					:       str     =self.prepare_image_inputs()

									A__					:       int     =processor(images=lowerCAmelCase_		,      visual_prompt=lowerCAmelCase_			)

									self.assertListEqual(list(inputs.keys()			)		,      ["""pixel_values""", """conditional_pixel_values"""]			)

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



			def lowercase__						(				self	:						Optional[Any]			)      ->		str:





									'''simple docstring'''
									A__					:       List[str]     =self.get_image_processor()
									A__					:       Optional[int]     =self.get_tokenizer()

									A__					:       Any     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       List[Any]     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A__					:       Tuple     =processor.batch_decode(lowerCAmelCase_			)
									A__					:       List[Any]     =tokenizer.batch_decode(lowerCAmelCase_			)

									self.assertListEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

