

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


A__						=							get_tests_dir("fixtures")

class 				__UpperCamelCase				(					unittest.TestCase			):







 def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

      '''simple docstring'''





      __magic_name__							=    mock.Mock()
      __magic_name__							=    5_00
      __magic_name__							=    {}
      __magic_name__							=    HTTPError
      __magic_name__							=    {}

      # Download this model to make sure it's in the cache.
      __magic_name__							=    ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'      )
      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch('requests.Session.request'  , return_value=A_      ) as mock_head:
           __magic_name__							=    ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'      )
           # This check we did call the fake head request
           mock_head.assert_called()







 def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

      '''simple docstring'''





      __magic_name__							=    ViTImageProcessor.from_pretrained(
          'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'      )







 def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

      '''simple docstring'''





      with self.assertRaises(A_      ):
           # config is in subfolder, the following should not work without specifying the subfolder
           __magic_name__							=    AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'      )

      __magic_name__							=    AutoImageProcessor.from_pretrained(
          'hf-internal-testing/stable-diffusion-all-variants'  , subfolder='feature_extractor'      )

      self.assertIsNotNone(A_      )




@is_staging_test
class 				__UpperCamelCase				(					unittest.TestCase			):







 @classmethod
 def 	_SCREAMING_SNAKE_CASE							(   cls: Any      ):

      '''simple docstring'''





      __magic_name__							=    TOKEN
      HfFolder.save_token(A_      )







 @classmethod
 def 	_SCREAMING_SNAKE_CASE							(   cls: Any      ):

      '''simple docstring'''





      try:
           delete_repo(token=cls._token  , repo_id='test-image-processor'      )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token  , repo_id='valid_org/test-image-processor-org'      )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token  , repo_id='test-dynamic-image-processor'      )
      except HTTPError:
           pass







 def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

      '''simple docstring'''





      __magic_name__							=    ViTImageProcessor.from_pretrained(A_      )
      image_processor.push_to_hub('test-image-processor'  , use_auth_token=self._token      )

      __magic_name__							=    ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor'      )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A_  , getattr(A_  , A_      )      )

      # Reset repo
      delete_repo(token=self._token  , repo_id='test-image-processor'      )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               A_  , repo_id='test-image-processor'  , push_to_hub=A_  , use_auth_token=self._token      )

      __magic_name__							=    ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor'      )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A_  , getattr(A_  , A_      )      )







 def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

      '''simple docstring'''





      __magic_name__							=    ViTImageProcessor.from_pretrained(A_      )
      image_processor.push_to_hub('valid_org/test-image-processor'  , use_auth_token=self._token      )

      __magic_name__							=    ViTImageProcessor.from_pretrained('valid_org/test-image-processor'      )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A_  , getattr(A_  , A_      )      )

      # Reset repo
      delete_repo(token=self._token  , repo_id='valid_org/test-image-processor'      )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               A_  , repo_id='valid_org/test-image-processor-org'  , push_to_hub=A_  , use_auth_token=self._token      )

      __magic_name__							=    ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org'      )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A_  , getattr(A_  , A_      )      )







 def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

      '''simple docstring'''





      CustomImageProcessor.register_for_auto_class()
      __magic_name__							=    CustomImageProcessor.from_pretrained(A_      )

      image_processor.push_to_hub('test-dynamic-image-processor'  , use_auth_token=self._token      )

      # This has added the proper auto_map field to the config
      self.assertDictEqual(
          image_processor.auto_map  , {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'}  , )

      __magic_name__							=    AutoImageProcessor.from_pretrained(
          F'{USER}/test-dynamic-image-processor'  , trust_remote_code=A_      )
      # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
      self.assertEqual(new_image_processor.__class__.__name__  , 'CustomImageProcessor'      )







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				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "layoutlmv3"







	def __init__(   self: Tuple  , __UpperCamelCase: List[Any]=5_02_65  , __UpperCamelCase: Union[str, Any]=7_68  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=12  , __UpperCamelCase: List[Any]=30_72  , __UpperCamelCase: Union[str, Any]="gelu"  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: List[Any]=5_12  , __UpperCamelCase: int=2  , __UpperCamelCase: Union[str, Any]=0.02  , __UpperCamelCase: Dict=1E-5  , __UpperCamelCase: int=1  , __UpperCamelCase: Dict=0  , __UpperCamelCase: Tuple=2  , __UpperCamelCase: Any=10_24  , __UpperCamelCase: Union[str, Any]=1_28  , __UpperCamelCase: Dict=1_28  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=32  , __UpperCamelCase: int=1_28  , __UpperCamelCase: List[str]=64  , __UpperCamelCase: Union[str, Any]=2_56  , __UpperCamelCase: int=True  , __UpperCamelCase: Optional[int]=True  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Union[str, Any]=2_24  , __UpperCamelCase: Tuple=3  , __UpperCamelCase: Any=16  , __UpperCamelCase: List[Any]=None  , **__UpperCamelCase: str  , ):

						'''simple docstring'''





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




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Any       				=  version.parse("1.12"			)







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''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 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return 1E-5







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return 12







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: "ProcessorMixin"  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional["TensorType"] = None  , __UpperCamelCase: int = 3  , __UpperCamelCase: int = 40  , __UpperCamelCase: int = 40  , ):

						'''simple docstring'''





						setattr(processor.image_processor  , 'apply_ocr'  , __UpperCamelCase      )

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

						# Generate dummy bounding boxes
						__magic_name__							=    [[[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)
						__magic_name__							=    self._generate_dummy_images(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						__magic_name__							=    dict(
						    processor(
						        __UpperCamelCase  , text=__UpperCamelCase  , boxes=__UpperCamelCase  , return_tensors=__UpperCamelCase  , )      )

						return inputs




