





from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_A					:    Optional[int]   =  {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class    __SCREAMING_SNAKE_CASE			( UpperCamelCase__					):
       _UpperCAmelCase      :  str     =    "ernie_m"
       _UpperCAmelCase      :  Dict     =    {"dropout": "classifier_dropout", "num_classes": "num_labels"}







       def __init__(      self     :	List[Any]		,					A     :	int = 2_5_0_0_0_2		,					A     :	List[str] = 7_6_8		,					A     :	Optional[Any] = 1_2		,					A     :	Union[str, Any] = 1_2		,					A     :	Dict = 3_0_7_2		,					A     :	List[Any] = "gelu"		,					A     :	Dict = 0.1		,					A     :	List[str] = 0.1		,					A     :	Optional[Any] = 5_1_4		,					A     :	Optional[Any] = 0.02		,					A     :	str = 1		,					A     :	int = 1e-05		,					A     :	Union[str, Any]=None		,					A     :	str=False		,					A     :	Dict=0.0		,					**A     :	str		,					)   ->str:
          super().__init__(pad_token_id=_a		,					**_a			)
          lowerCamelCase__   :						Union[str, Any]					   =							vocab_size
          lowerCamelCase__   :						Any					   =							hidden_size
          lowerCamelCase__   :						Union[str, Any]					   =							num_hidden_layers
          lowerCamelCase__   :						Optional[Any]					   =							num_attention_heads
          lowerCamelCase__   :						Dict					   =							intermediate_size
          lowerCamelCase__   :						Dict					   =							hidden_act
          lowerCamelCase__   :						Optional[Any]					   =							hidden_dropout_prob
          lowerCamelCase__   :						int					   =							attention_probs_dropout_prob
          lowerCamelCase__   :						Tuple					   =							max_position_embeddings
          lowerCamelCase__   :						Union[str, Any]					   =							initializer_range
          lowerCamelCase__   :						List[Any]					   =							layer_norm_eps
          lowerCamelCase__   :						Tuple					   =							classifier_dropout
          lowerCamelCase__   :						Union[str, Any]					   =							is_decoder
          lowerCamelCase__   :						List[Any]					   =							act_dropout









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DonutImageProcessor







class 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a=7		,     _a=3		,     _a=18		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=False		,     _a=True		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     )						->							Dict:
           _A							:     str			=						parent
           _A							:     int			=						batch_size
           _A							:     Optional[int]			=						num_channels
           _A							:     List[Any]			=						image_size
           _A							:     int			=						min_resolution
           _A							:     Optional[int]			=						max_resolution
           _A							:     Any			=						do_resize
           _A							:     List[str]			=						size if size is not None else {"""height""": 18, """width""": 20}
           _A							:     Optional[int]			=						do_thumbnail
           _A							:     str			=						do_align_axis
           _A							:     List[Any]			=						do_pad
           _A							:     Optional[Any]			=						do_normalize
           _A							:     Tuple			=						image_mean
           _A							:     List[str]			=						image_std



     def 					a__	(  self				)						->							Optional[int]:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_thumbnail": self.do_thumbnail,
               "do_align_long_axis": self.do_align_axis,
               "do_pad": self.do_pad,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
           }







@require_torch
@require_vision
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						DonutImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     List[str]			=						DonutImageProcessingTester(self				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)
           self.assertTrue(hasattr(_a		,     """do_thumbnail"""				)				)
           self.assertTrue(hasattr(_a		,     """do_align_long_axis"""				)				)
           self.assertTrue(hasattr(_a		,     """do_pad"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size		,     {"""height""": 18, """width""": 20}				)

           _A							:     Optional[int]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=42				)
           self.assertEqual(image_processor.size		,     {"""height""": 42, """width""": 42}				)

           # Previous config had dimensions in (width, height) order
           _A							:     List[str]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=(42, 84)				)
           self.assertEqual(image_processor.size		,     {"""height""": 84, """width""": 42}				)

     def 					a__	(  self				)						->							Union[str, Any]:
           pass

     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Dict			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     Union[str, Any]			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     Any			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

     @is_flaky()
     def 					a__	(  self				)						->							Dict:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     Tuple			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

           # Test not batched input
           _A							:     int			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     List[str]			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )



     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     Optional[int]			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

           # Test not batched input
           _A							:     Any			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     str			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

