






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 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     tempfile.mkdtemp()

												# fmt: off
												__lowercase      :							Tuple				=     ["""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
												__lowercase      :							Optional[Any]				=     dict(zip(lowerCamelCase_		,     range(len(lowerCamelCase_							)							)							)							)
												__lowercase      :							str				=     ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
												__lowercase      :							Any				=     {"""unk_token""": """<unk>"""}

												__lowercase      :							str				=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]							)
												__lowercase      :							List[Any]				=     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_							)							)

												__lowercase      :							str				=     {
												    """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],
												}
												__lowercase      :							Tuple				=     os.path.join(self.tmpdirname		,     lowerCamelCase_							)
												with open(self.image_processor_file		,     """w"""		,     encoding="""utf-8"""							) as fp:
																			json.dump(lowerCamelCase_		,     lowerCamelCase_							)



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


												"""simple docstring"""

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



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


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     **__a				:	Union[str, Any]							)			->  List[Any]:


												"""simple docstring"""

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



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


												"""simple docstring"""

												shutil.rmtree(self.tmpdirname							)



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


												"""simple docstring"""

												__lowercase      :							Any				=     [np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta							)]

												__lowercase      :							Tuple				=     [Image.fromarray(np.moveaxis(lowerCamelCase_		,     0		,     -1							)							) for x in image_inputs]

												return image_inputs



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.get_tokenizer()
												__lowercase      :							Optional[Any]				=     self.get_rust_tokenizer()
												__lowercase      :							str				=     self.get_image_processor()

												__lowercase      :							Optional[int]				=     CLIPProcessor(tokenizer=lowerCamelCase_		,     image_processor=lowerCamelCase_							)
												processor_slow.save_pretrained(self.tmpdirname							)
												__lowercase      :							Optional[int]				=     CLIPProcessor.from_pretrained(self.tmpdirname		,     use_fast=lowerCamelCase_							)

												__lowercase      :							Optional[Any]				=     CLIPProcessor(tokenizer=lowerCamelCase_		,     image_processor=lowerCamelCase_							)
												processor_fast.save_pretrained(self.tmpdirname							)
												__lowercase      :							Tuple				=     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       lowerCAmelCase				(  self				:	List[Any]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     CLIPProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()							)
												processor.save_pretrained(self.tmpdirname							)

												__lowercase      :							Dict				=     self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)"""							)
												__lowercase      :							Union[str, Any]				=     self.get_image_processor(do_normalize=lowerCamelCase_		,     padding_value=1.0							)

												__lowercase      :							Optional[Any]				=     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       lowerCAmelCase				(  self				:	Optional[Any]							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.get_image_processor()
												__lowercase      :							List[Any]				=     self.get_tokenizer()

												__lowercase      :							Optional[Any]				=     CLIPProcessor(tokenizer=lowerCamelCase_		,     image_processor=lowerCamelCase_							)

												__lowercase      :							Optional[int]				=     self.prepare_image_inputs()

												__lowercase      :							Any				=     image_processor(lowerCamelCase_		,     return_tensors="""np"""							)
												__lowercase      :							Union[str, Any]				=     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       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.get_image_processor()
												__lowercase      :							Any				=     self.get_tokenizer()

												__lowercase      :							int				=     CLIPProcessor(tokenizer=lowerCamelCase_		,     image_processor=lowerCamelCase_							)

												__lowercase      :							Any				=     """lower newer"""

												__lowercase      :							Dict				=     processor(text=lowerCamelCase_							)

												__lowercase      :							Optional[int]				=     tokenizer(lowerCamelCase_							)

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



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


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.get_image_processor()
												__lowercase      :							Tuple				=     self.get_tokenizer()

												__lowercase      :							Tuple				=     CLIPProcessor(tokenizer=lowerCamelCase_		,     image_processor=lowerCamelCase_							)

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

												__lowercase      :							Dict				=     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       lowerCAmelCase				(  self				:	Dict							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.get_image_processor()
												__lowercase      :							Tuple				=     self.get_tokenizer()

												__lowercase      :							List[str]				=     CLIPProcessor(tokenizer=lowerCamelCase_		,     image_processor=lowerCamelCase_							)

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

												__lowercase      :							Optional[Any]				=     processor.batch_decode(lowerCamelCase_							)
												__lowercase      :							Union[str, Any]				=     tokenizer.batch_decode(lowerCamelCase_							)

												self.assertListEqual(lowerCamelCase_		,     lowerCamelCase_							)



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


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.get_image_processor()
												__lowercase      :							Union[str, Any]				=     self.get_tokenizer()

												__lowercase      :							Optional[int]				=     CLIPProcessor(tokenizer=lowerCamelCase_		,     image_processor=lowerCamelCase_							)

												__lowercase      :							Tuple				=     """lower newer"""
												__lowercase      :							Any				=     self.prepare_image_inputs()

												__lowercase      :							int				=     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


lowerCamelCase : Optional[int]						   =       logging.get_logger(__name__)

lowerCamelCase : Tuple						   =       {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			int									=      '''layoutlmv3'''



					def __init__(  self				:	Dict		,     __a				:	List[str]=50265		,     __a				:	str=768		,     __a				:	List[Any]=12		,     __a				:	List[Any]=12		,     __a				:	List[str]=3072		,     __a				:	Optional[Any]="gelu"		,     __a				:	Optional[int]=0.1		,     __a				:	List[Any]=0.1		,     __a				:	Tuple=512		,     __a				:	int=2		,     __a				:	Any=0.02		,     __a				:	Union[str, Any]=1E-5		,     __a				:	List[str]=1		,     __a				:	List[Any]=0		,     __a				:	int=2		,     __a				:	str=1024		,     __a				:	str=128		,     __a				:	List[Any]=128		,     __a				:	Tuple=True		,     __a				:	Optional[int]=32		,     __a				:	Any=128		,     __a				:	List[Any]=64		,     __a				:	Tuple=256		,     __a				:	str=True		,     __a				:	int=True		,     __a				:	Optional[Any]=True		,     __a				:	Any=224		,     __a				:	str=3		,     __a				:	List[str]=16		,     __a				:	Union[str, Any]=None		,     **__a				:	List[Any]		,     )			->  List[str]:


												"""simple docstring"""

												super().__init__(
												    vocab_size=__a		,     hidden_size=__a		,     num_hidden_layers=__a		,     num_attention_heads=__a		,     intermediate_size=__a		,     hidden_act=__a		,     hidden_dropout_prob=__a		,     attention_probs_dropout_prob=__a		,     max_position_embeddings=__a		,     type_vocab_size=__a		,     initializer_range=__a		,     layer_norm_eps=__a		,     pad_token_id=__a		,     bos_token_id=__a		,     eos_token_id=__a		,     **__a		,     )
												__lowercase      :							int				=     max_ad_position_embeddings
												__lowercase      :							Any				=     coordinate_size
												__lowercase      :							Optional[Any]				=     shape_size
												__lowercase      :							str				=     has_relative_attention_bias
												__lowercase      :							int				=     rel_pos_bins
												__lowercase      :							Union[str, Any]				=     max_rel_pos
												__lowercase      :							str				=     has_spatial_attention_bias
												__lowercase      :							str				=     rel_ad_pos_bins
												__lowercase      :							List[Any]				=     max_rel_ad_pos
												__lowercase      :							Tuple				=     text_embed
												__lowercase      :							int				=     visual_embed
												__lowercase      :							Tuple				=     input_size
												__lowercase      :							Dict				=     num_channels
												__lowercase      :							str				=     patch_size
												__lowercase      :							Optional[int]				=     classifier_dropout







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			str									=      version.parse('''1.12'''			)



					@property
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  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       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  float:


												"""simple docstring"""

												return 1E-5



					@property
					def       lowerCAmelCase				(  self				:	str							)			->  int:


												"""simple docstring"""

												return 12



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


												"""simple docstring"""

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

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

												# Generate dummy bounding boxes
												__lowercase      :							Tuple				=     [[[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)
												__lowercase      :							Tuple				=     self._generate_dummy_images(__a		,     __a		,     __a		,     __a							)

												__lowercase      :							int				=     dict(
												    processor(
												        __a		,     text=__a		,     boxes=__a		,     return_tensors=__a		,     )							)

												return inputs