

'''simple docstring'''







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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor


class        A__   (   unittest.TestCase      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=7		,					UpperCamelCase__=3		,					UpperCamelCase__=18		,					UpperCamelCase__=30		,					UpperCamelCase__=400		,					UpperCamelCase__=True		,					UpperCamelCase__=None		,					UpperCamelCase__=True		,					UpperCamelCase__=None		,					UpperCamelCase__=True		,					UpperCamelCase__=[0.48145466, 0.4578275, 0.40821073]		,					UpperCamelCase__=[0.26862954, 0.26130258, 0.27577711]		,					UpperCamelCase__=True		,					)    ->   int:





           '''simple docstring'''
           A_						       =   size if size is not None else {"""height""": 224, """width""": 224}
           A_						       =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   num_channels
           A_						       =   image_size
           A_						       =   min_resolution
           A_						       =   max_resolution
           A_						       =   do_resize
           A_						       =   size
           A_						       =   do_center_crop
           A_						       =   crop_size
           A_						       =   do_normalize
           A_						       =   image_mean
           A_						       =   image_std
           A_						       =   do_convert_rgb



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_convert_rgb": self.do_convert_rgb,
           }







    def        snake_case_      (       self		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					UpperCamelCase__=False							)    ->   Optional[Any]:





           '''simple docstring'''
           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                  A_						       =   []
                  for i in range(self.batch_size							):
                         image_inputs.append(
                             np.random.randint(
                                 255		,					size=(self.num_channels, self.max_resolution, self.max_resolution)		,					dtype=np.uinta							)							)
           else:
                  A_						       =   []
                  for i in range(self.batch_size							):
                         A_       ,      A_						       =   np.random.choice(np.arange(self.min_resolution		,					self.max_resolution							)		,					2							)
                         image_inputs.append(np.random.randint(255		,					size=(self.num_channels, width, height)		,					dtype=np.uinta							)							)

           if not numpify and not torchify:
                  # PIL expects the channel dimension as last dimension
                  A_						       =   [Image.fromarray(np.moveaxis(UpperCamelCase__		,					0		,					-1							)							) for x in image_inputs]

           if torchify:
                  A_						       =   [torch.from_numpy(UpperCamelCase__							) for x in image_inputs]

           return image_inputs


@require_torch
@require_vision
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		ChineseCLIPImageProcessor if is_vision_available() else None



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





           '''simple docstring'''
           A_						       =   ChineseCLIPImageProcessingTester(self		,					do_center_crop=UpperCamelCase__							)



    @property
    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           return self.image_processor_tester.prepare_image_processor_dict()



    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   self.image_processing_class(**self.image_processor_dict							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_resize"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""size"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_center_crop"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""center_crop"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_normalize"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""image_mean"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""image_std"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_convert_rgb"""							)							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   self.image_processing_class.from_dict(self.image_processor_dict							)
           self.assertEqual(image_processor.size		,					{"""height""": 224, """width""": 224}							)
           self.assertEqual(image_processor.crop_size		,					{"""height""": 18, """width""": 18}							)

           A_						       =   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        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           pass



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           # Initialize image_processing
           A_						       =   self.image_processing_class(**self.image_processor_dict							)
           # create random PIL images
           A_						       =   self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__							)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__		,					Image.Image							)

           # Test not batched input
           A_						       =   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
           A_						       =   image_processing(UpperCamelCase__		,					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        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           # Initialize image_processing
           A_						       =   self.image_processing_class(**self.image_processor_dict							)
           # create random numpy tensors
           A_						       =   self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__		,					numpify=UpperCamelCase__							)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__		,					np.ndarray							)

           # Test not batched input
           A_						       =   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
           A_						       =   image_processing(UpperCamelCase__		,					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        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           # Initialize image_processing
           A_						       =   self.image_processing_class(**self.image_processor_dict							)
           # create random PyTorch tensors
           A_						       =   self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__		,					torchify=UpperCamelCase__							)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__		,					torch.Tensor							)

           # Test not batched input
           A_						       =   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
           A_						       =   image_processing(UpperCamelCase__		,					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"""],
               )		,					)


@require_torch
@require_vision
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		ChineseCLIPImageProcessor if is_vision_available() else None



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   ChineseCLIPImageProcessingTester(self		,					num_channels=4		,					do_center_crop=UpperCamelCase__							)
           A_						       =   3



    @property
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           return self.image_processor_tester.prepare_image_processor_dict()



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   self.image_processing_class(**self.image_processor_dict							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_resize"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""size"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_center_crop"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""center_crop"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_normalize"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""image_mean"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""image_std"""							)							)
           self.assertTrue(hasattr(UpperCamelCase__		,					"""do_convert_rgb"""							)							)



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           pass







    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           # Initialize image_processing
           A_						       =   self.image_processing_class(**self.image_processor_dict							)
           # create random PIL images
           A_						       =   self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__							)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__		,					Image.Image							)

           # Test not batched input
           A_						       =   image_processing(image_inputs[0]		,					return_tensors="""pt"""							).pixel_values
           self.assertEqual(
               encoded_images.shape		,					(
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,					)

           # Test batched
           A_						       =   image_processing(UpperCamelCase__		,					return_tensors="""pt"""							).pixel_values
           self.assertEqual(
               encoded_images.shape		,					(
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,					)



'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   hidden_act
           A_						       =   intermediate_size
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
