

import unittest

import numpy as np

from transformers.testing_utils import 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 MobileViTImageProcessor



class SCREAMING_SNAKE_CASE      (    unittest.TestCase					):
  """simple docstring"""






  def __init__(     self					:       Optional[int]			,				lowerCAmelCase					:       List[Any]			,				lowerCAmelCase					:       int=7			,				lowerCAmelCase					:       List[Any]=3			,				lowerCAmelCase					:       str=18			,				lowerCAmelCase					:       Dict=30			,				lowerCAmelCase					:       List[Any]=4_00			,				lowerCAmelCase					:       str=True			,				lowerCAmelCase					:       Any=None			,				lowerCAmelCase					:       str=True			,				lowerCAmelCase					:       List[Any]=None			,				lowerCAmelCase					:       List[str]=True			,				)				->      List[str]:






     """simple docstring"""







     __lowerCAmelCase    :     List[str]          =		size if size is not None else {"shortest_edge": 20}
     __lowerCAmelCase    :     List[Any]          =		crop_size if crop_size is not None else {"height": 18, "width": 18}
     __lowerCAmelCase    :     Any          =		parent
     __lowerCAmelCase    :     List[str]          =		batch_size
     __lowerCAmelCase    :     Optional[int]          =		num_channels
     __lowerCAmelCase    :     Optional[Any]          =		image_size
     __lowerCAmelCase    :     str          =		min_resolution
     __lowerCAmelCase    :     List[Any]          =		max_resolution
     __lowerCAmelCase    :     int          =		do_resize
     __lowerCAmelCase    :     Optional[int]          =		size
     __lowerCAmelCase    :     Any          =		do_center_crop
     __lowerCAmelCase    :     int          =		crop_size
     __lowerCAmelCase    :     int          =		do_flip_channel_order







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[Any]       )				->      Union[str, Any]:






     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_flip_channel_order": self.do_flip_channel_order,
     }






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE      (    __A  ,				unittest.TestCase					):
  """simple docstring"""






  lowerCamelCase       :     Tuple		 =MobileViTImageProcessor if is_vision_available() else None







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]       )				->      int:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[Any]          =		MobileViTImageProcessingTester(self       )







  @property
  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[int]       )				->      List[str]:






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      Optional[int]:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[int]          =		self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(lowerCAmelCase			,				"""do_resize"""       )       )
     self.assertTrue(hasattr(lowerCAmelCase			,				"""size"""       )       )
     self.assertTrue(hasattr(lowerCAmelCase			,				"""do_center_crop"""       )       )
     self.assertTrue(hasattr(lowerCAmelCase			,				"""center_crop"""       )       )
     self.assertTrue(hasattr(lowerCAmelCase			,				"""do_flip_channel_order"""       )       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase    :     List[str]          =		self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size			,				{"""shortest_edge""": 20}       )
     self.assertEqual(image_processor.crop_size			,				{"""height""": 18, """width""": 18}       )

     __lowerCAmelCase    :     List[Any]          =		self.image_processing_class.from_dict(self.image_processor_dict			,				size=42			,				crop_size=84       )
     self.assertEqual(image_processor.size			,				{"""shortest_edge""": 42}       )
     self.assertEqual(image_processor.crop_size			,				{"""height""": 84, """width""": 84}       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Dict       )				->      int:






     """simple docstring"""







     pass







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      List[str]:






     """simple docstring"""







     __lowerCAmelCase    :     List[Any]          =		self.image_processing_class(**self.image_processor_dict       )
     # create random PIL images
     __lowerCAmelCase    :     Optional[int]          =		prepare_image_inputs(self.image_processor_tester			,				equal_resolution=lowerCAmelCase       )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase			,				Image.Image       )

     # Test not batched input
     __lowerCAmelCase    :     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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )			,				)

     # Test batched
     __lowerCAmelCase    :     str          =		image_processing(lowerCAmelCase			,				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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )			,				)







  def 			SCREAMING_SNAKE_CASE       (     self					:       Dict       )				->      int:






     """simple docstring"""







     __lowerCAmelCase    :     str          =		self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     __lowerCAmelCase    :     Tuple          =		prepare_image_inputs(self.image_processor_tester			,				equal_resolution=lowerCAmelCase			,				numpify=lowerCAmelCase       )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase			,				np.ndarray       )

     # Test not batched input
     __lowerCAmelCase    :     Tuple          =		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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )			,				)

     # Test batched
     __lowerCAmelCase    :     Any          =		image_processing(lowerCAmelCase			,				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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )			,				)







  def 			SCREAMING_SNAKE_CASE       (     self					:       int       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     Any          =		self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     __lowerCAmelCase    :     Dict          =		prepare_image_inputs(self.image_processor_tester			,				equal_resolution=lowerCAmelCase			,				torchify=lowerCAmelCase       )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase			,				torch.Tensor       )

     # Test not batched input
     __lowerCAmelCase    :     str          =		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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )			,				)

     # Test batched
     __lowerCAmelCase    :     List[Any]          =		image_processing(lowerCAmelCase			,				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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )			,				)





from collections import OrderedDict
from typing import Mapping

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







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

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)