



'''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        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       List[Any]       		= tempfile.mkdtemp()

          # fmt: off
          snake_case__    :       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
          snake_case__    :       Union[str, Any]       		= dict(zip(snake_case_					,	range(len(snake_case_		)		)		)		)
          snake_case__    :       Dict       		= ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
          snake_case__    :       List[Any]       		= {"""unk_token""": """<unk>"""}

          snake_case__    :       List[Any]       		= os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES["""vocab_file"""]		)
          snake_case__    :       List[str]       		= 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(snake_case_		) + """\n"""		)
          with open(self.merges_file					,	"""w"""					,	encoding="""utf-8"""		) as fp:
                fp.write("""\n""".join(snake_case_		)		)

          snake_case__    :       Any       		= {
              """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],
          }
          snake_case__    :       Optional[int]       		= os.path.join(self.tmpdirname					,	snake_case_		)
          with open(self.image_processor_file					,	"""w"""					,	encoding="""utf-8"""		) as fp:
                json.dump(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Any					,	**snake_case_   :	List[str]		):
          return CLIPTokenizer.from_pretrained(self.tmpdirname					,	**snake_case_		)




    def lowerCamelCase			(       self   :	List[str]					,	**snake_case_   :	Optional[Any]		):
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,	**snake_case_		)




    def lowerCamelCase			(       self   :	Dict					,	**snake_case_   :	Optional[Any]		):
          return ViTImageProcessor.from_pretrained(self.tmpdirname					,	**snake_case_		)




    def lowerCamelCase			(       self   :	List[str]		):
          shutil.rmtree(self.tmpdirname		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       str       		= [np.random.randint(255					,	size=(3, 30, 400)					,	dtype=np.uinta		)]

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

          return image_inputs




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= self.get_tokenizer()
          snake_case__    :       Any       		= self.get_rust_tokenizer()
          snake_case__    :       Tuple       		= self.get_image_processor()

          snake_case__    :       int       		= CLIPSegProcessor(tokenizer=snake_case_					,	image_processor=snake_case_		)
          processor_slow.save_pretrained(self.tmpdirname		)
          snake_case__    :       Optional[Any]       		= CLIPSegProcessor.from_pretrained(self.tmpdirname					,	use_fast=snake_case_		)

          snake_case__    :       Tuple       		= CLIPSegProcessor(tokenizer=snake_case_					,	image_processor=snake_case_		)
          processor_fast.save_pretrained(self.tmpdirname		)
          snake_case__    :       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					,	snake_case_		)
          self.assertIsInstance(processor_fast.tokenizer					,	snake_case_		)

          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					,	snake_case_		)
          self.assertIsInstance(processor_fast.image_processor					,	snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       List[Any]       		= CLIPSegProcessor(tokenizer=self.get_tokenizer()					,	image_processor=self.get_image_processor()		)
          processor.save_pretrained(self.tmpdirname		)

          snake_case__    :       Union[str, Any]       		= self.get_tokenizer(bos_token="""(BOS)"""					,	eos_token="""(EOS)"""		)
          snake_case__    :       Tuple       		= self.get_image_processor(do_normalize=snake_case_					,	padding_value=1.0		)

          snake_case__    :       Optional[Any]       		= CLIPSegProcessor.from_pretrained(
              self.tmpdirname					,	bos_token="""(BOS)"""					,	eos_token="""(EOS)"""					,	do_normalize=snake_case_					,	padding_value=1.0		)

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

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




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[Any]       		= self.get_image_processor()
          snake_case__    :       Dict       		= self.get_tokenizer()

          snake_case__    :       str       		= CLIPSegProcessor(tokenizer=snake_case_					,	image_processor=snake_case_		)

          snake_case__    :       List[Any]       		= self.prepare_image_inputs()

          snake_case__    :       Any       		= image_processor(snake_case_					,	return_tensors="""np"""		)
          snake_case__    :       int       		= processor(images=snake_case_					,	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   :	List[Any]		):
          snake_case__    :       Tuple       		= self.get_image_processor()
          snake_case__    :       Tuple       		= self.get_tokenizer()

          snake_case__    :       Dict       		= CLIPSegProcessor(tokenizer=snake_case_					,	image_processor=snake_case_		)

          snake_case__    :       Dict       		= """lower newer"""

          snake_case__    :       Optional[Any]       		= processor(text=snake_case_		)

          snake_case__    :       List[Any]       		= tokenizer(snake_case_		)

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




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       int       		= self.get_image_processor()
          snake_case__    :       Dict       		= self.get_tokenizer()

          snake_case__    :       Dict       		= CLIPSegProcessor(tokenizer=snake_case_					,	image_processor=snake_case_		)

          snake_case__    :       Optional[Any]       		= """lower newer"""
          snake_case__    :       Union[str, Any]       		= self.prepare_image_inputs()

          snake_case__    :       List[Any]       		= processor(text=snake_case_					,	images=snake_case_		)

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

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




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= self.get_image_processor()
          snake_case__    :       str       		= self.get_tokenizer()

          snake_case__    :       Any       		= CLIPSegProcessor(tokenizer=snake_case_					,	image_processor=snake_case_		)

          snake_case__    :       Tuple       		= self.prepare_image_inputs()
          snake_case__    :       Optional[Any]       		= self.prepare_image_inputs()

          snake_case__    :       Any       		= processor(images=snake_case_					,	visual_prompt=snake_case_		)

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

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




    def lowerCamelCase			(       self   :	str		):
          snake_case__    :       Dict       		= self.get_image_processor()
          snake_case__    :       List[Any]       		= self.get_tokenizer()

          snake_case__    :       Optional[Any]       		= CLIPSegProcessor(tokenizer=snake_case_					,	image_processor=snake_case_		)

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

          snake_case__    :       Union[str, Any]       		= processor.batch_decode(snake_case_		)
          snake_case__    :       int       		= tokenizer.batch_decode(snake_case_		)

          self.assertListEqual(snake_case_					,	snake_case_		)





'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"layoutlmv3"




    def __init__(       self   :	Any					,	snake_case_   :	Dict=50_265					,	snake_case_   :	Union[str, Any]=768					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	Optional[int]=12					,	snake_case_   :	Union[str, Any]=3_072					,	snake_case_   :	List[str]="gelu"					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=512					,	snake_case_   :	List[str]=2					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	List[Any]=1E-5					,	snake_case_   :	Any=1					,	snake_case_   :	Any=0					,	snake_case_   :	int=2					,	snake_case_   :	Optional[Any]=1_024					,	snake_case_   :	int=128					,	snake_case_   :	Union[str, Any]=128					,	snake_case_   :	int=True					,	snake_case_   :	Tuple=32					,	snake_case_   :	str=128					,	snake_case_   :	Optional[int]=64					,	snake_case_   :	Optional[Any]=256					,	snake_case_   :	List[str]=True					,	snake_case_   :	str=True					,	snake_case_   :	Dict=True					,	snake_case_   :	int=224					,	snake_case_   :	Any=3					,	snake_case_   :	int=16					,	snake_case_   :	Tuple=None					,	**snake_case_   :	Optional[Any]					,	):
          super().__init__(
              vocab_size=snake_case_					,	hidden_size=snake_case_					,	num_hidden_layers=snake_case_					,	num_attention_heads=snake_case_					,	intermediate_size=snake_case_					,	hidden_act=snake_case_					,	hidden_dropout_prob=snake_case_					,	attention_probs_dropout_prob=snake_case_					,	max_position_embeddings=snake_case_					,	type_vocab_size=snake_case_					,	initializer_range=snake_case_					,	layer_norm_eps=snake_case_					,	pad_token_id=snake_case_					,	bos_token_id=snake_case_					,	eos_token_id=snake_case_					,	**snake_case_					,	)
          snake_case__    :       List[Any]       		= max_ad_position_embeddings
          snake_case__    :       Optional[int]       		= coordinate_size
          snake_case__    :       int       		= shape_size
          snake_case__    :       Optional[int]       		= has_relative_attention_bias
          snake_case__    :       Dict       		= rel_pos_bins
          snake_case__    :       List[Any]       		= max_rel_pos
          snake_case__    :       str       		= has_spatial_attention_bias
          snake_case__    :       Optional[Any]       		= rel_ad_pos_bins
          snake_case__    :       Optional[int]       		= max_rel_ad_pos
          snake_case__    :       Union[str, Any]       		= text_embed
          snake_case__    :       List[Any]       		= visual_embed
          snake_case__    :       Union[str, Any]       		= input_size
          snake_case__    :       int       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Dict       		= classifier_dropout







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          # The order of inputs is different for question answering and sequence classification
          if self.task in ["question-answering", "sequence-classification"]:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ]		)
          else:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
                    ]		)




    @property
    def lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	"ProcessorMixin"					,	snake_case_   :	int = -1					,	snake_case_   :	int = -1					,	snake_case_   :	bool = False					,	snake_case_   :	Optional["TensorType"] = None					,	snake_case_   :	int = 3					,	snake_case_   :	int = 40					,	snake_case_   :	int = 40					,	):
          setattr(processor.image_processor					,	"""apply_ocr"""					,	snake_case_		)

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          snake_case__    :       Optional[Any]       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case__    :       List[Any]       		= processor.tokenizer.num_special_tokens_to_add(snake_case_		)
          snake_case__    :       Tuple       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=snake_case_		)
          # Generate dummy inputs according to compute batch and sequence
          snake_case__    :       int       		= [[""" """.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          snake_case__    :       int       		= [[[48, 84, 73, 128]]] * batch_size

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
          snake_case__    :       Optional[int]       		= self._generate_dummy_images(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= dict(
              processor(
                  snake_case_					,	text=snake_case_					,	boxes=snake_case_					,	return_tensors=snake_case_					,	)		)

          return inputs
