





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 CLIPImageProcessor, CLIPProcessor


@require_vision
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    Union[str, Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   tempfile.mkdtemp()

					# fmt: off
					A__        =   ["""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__        =   dict(zip(__lowerCAmelCase							,	range(len(__lowerCAmelCase   )   )   )   )
					A__        =   ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
					A__        =   {"""unk_token""": """<unk>"""}

					A__        =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["""vocab_file"""]   )
					A__        =   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__        =   {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					A__        =   os.path.join(self.tmpdirname							,	__lowerCAmelCase   )
					with open(self.image_processor_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							json.dump(__lowerCAmelCase							,	__lowerCAmelCase   )






			def    a_       (			self   :    Tuple							,	**__lowerCAmelCase   :    Dict   )	->	str:







					"""simple docstring"""






					return CLIPTokenizer.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    Union[str, Any]							,	**__lowerCAmelCase   :    Dict   )	->	List[str]:







					"""simple docstring"""






					return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    List[str]							,	**__lowerCAmelCase   :    Optional[Any]   )	->	Dict:







					"""simple docstring"""






					return CLIPImageProcessor.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    str   )	->	Dict:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname   )






			def    a_       (			self   :    str   )	->	Any:







					"""simple docstring"""






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

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

					return image_inputs






			def    a_       (			self   :    Optional[int]   )	->	Tuple:







					"""simple docstring"""






					A__        =   self.get_tokenizer()
					A__        =   self.get_rust_tokenizer()
					A__        =   self.get_image_processor()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_slow.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.from_pretrained(self.tmpdirname							,	use_fast=__lowerCAmelCase   )

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_fast.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.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    a_       (			self   :    Optional[Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   CLIPProcessor(tokenizer=self.get_tokenizer()							,	image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

					A__        =   self.get_tokenizer(bos_token="""(BOS)"""							,	eos_token="""(EOS)"""   )
					A__        =   self.get_image_processor(do_normalize=__lowerCAmelCase							,	padding_value=1.0   )

					A__        =   CLIPProcessor.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    a_       (			self   :    List[Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   self.prepare_image_inputs()

					A__        =   image_processor(__lowerCAmelCase							,	return_tensors="""np"""   )
					A__        =   processor(images=__lowerCAmelCase							,	return_tensors="""np"""   )

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






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







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""

					A__        =   processor(text=__lowerCAmelCase   )

					A__        =   tokenizer(__lowerCAmelCase   )

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






			def    a_       (			self   :    Union[str, Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   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    a_       (			self   :    Tuple   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

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

					A__        =   processor.batch_decode(__lowerCAmelCase   )
					A__        =   tokenizer.batch_decode(__lowerCAmelCase   )

					self.assertListEqual(__lowerCAmelCase							,	__lowerCAmelCase   )







			def    a_       (			self   :    Optional[int]   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   processor(text=__lowerCAmelCase							,	images=__lowerCAmelCase   )

					self.assertListEqual(list(inputs.keys()   )							,	processor.model_input_names   )










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    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					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    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






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







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	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
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * 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)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



