

import logging
import os

from .state import PartialState





class  __UpperCAmelCase					(	logging.LoggerAdapter						):



   @staticmethod
   def 	__magic_name__   (							__A							:					str						):
     UpperCAmelCase	:							Dict						=			PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Union[str, Any],		*__A							:					Optional[int],		**__A							:					Tuple						):
     if PartialState._shared_state == {}:
       raise RuntimeError(
           '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''						)
     UpperCAmelCase	:							List[str]						=			kwargs.pop('''main_process_only''',		__A						)
     UpperCAmelCase	:							int						=			kwargs.pop('''in_order''',		__A						)

     if self.isEnabledFor(__A						):
       if self._should_log(__A						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.process(__A,		__A						)
         self.logger.log(__A,		__A,		*__A,		**__A						)

       elif in_order:
         UpperCAmelCase	:							Dict						=			PartialState()
         for i in range(state.num_processes						):
           if i == state.process_index:
             UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.process(__A,		__A						)
             self.logger.log(__A,		__A,		*__A,		**__A						)
           state.wait_for_everyone()






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str = None      )  ->						Dict:
  if log_level is None:
    UpperCAmelCase	:							Union[str, Any]						=			os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			logging.getLogger(UpperCAmelCase      )
  if log_level is not None:
    logger.setLevel(log_level.upper()      )
    logger.root.setLevel(log_level.upper()      )
  return MultiProcessAdapter(UpperCAmelCase    ,			{}      )








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  __UpperCAmelCase					(	unittest.TestCase						):



   def __init__(							self							:					List[Any],		__A							:					str,		__A							:					List[str]=7,		__A							:					List[str]=3,		__A							:					Optional[int]=1_8,		__A							:					List[Any]=3_0,		__A							:					Tuple=4_0_0,		__A							:					Tuple=True,		__A							:					List[Any]=None,		__A							:					str=True,		__A							:					int=None,		__A							:					Optional[Any]=True,		__A							:					List[Any]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],		__A							:					List[str]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],		__A							:					Tuple=True,		):
     UpperCAmelCase	:							int						=			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
     UpperCAmelCase	:							Optional[Any]						=			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
     UpperCAmelCase	:							int						=			parent
     UpperCAmelCase	:							Union[str, Any]						=			batch_size
     UpperCAmelCase	:							List[Any]						=			num_channels
     UpperCAmelCase	:							Dict						=			image_size
     UpperCAmelCase	:							List[str]						=			min_resolution
     UpperCAmelCase	:							Optional[Any]						=			max_resolution
     UpperCAmelCase	:							Union[str, Any]						=			do_resize
     UpperCAmelCase	:							Dict						=			size
     UpperCAmelCase	:							Any						=			do_center_crop
     UpperCAmelCase	:							Union[str, Any]						=			crop_size
     UpperCAmelCase	:							List[str]						=			do_normalize
     UpperCAmelCase	:							Optional[Any]						=			image_mean
     UpperCAmelCase	:							Optional[Any]						=			image_std
     UpperCAmelCase	:							List[Any]						=			do_convert_rgb



   def 	__magic_name__   (							self							:					int						):
     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 	__magic_name__   (							self							:					Optional[Any],		__A							:					Any=False,		__A							:					str=False,		__A							:					List[Any]=False						):
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       UpperCAmelCase	:							Dict						=			[]
       for i in range(self.batch_size						):
         image_inputs.append(
             np.random.randint(
                 2_5_5,		size=(self.num_channels, self.max_resolution, self.max_resolution),		dtype=np.uinta						)						)
     else:
       UpperCAmelCase	:							Tuple						=			[]
       for i in range(self.batch_size						):
         UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			np.random.choice(np.arange(self.min_resolution,		self.max_resolution						),		2						)
         image_inputs.append(np.random.randint(2_5_5,		size=(self.num_channels, width, height),		dtype=np.uinta						)						)

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

     if torchify:
       UpperCAmelCase	:							str						=			[torch.from_numpy(__A						) for x in image_inputs]

     return image_inputs






@require_torch
@require_vision
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					ChineseCLIPImageProcessor if is_vision_available() else None



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Optional[Any]						=			ChineseCLIPImageProcessingTester(self,		do_center_crop=__A						)



   @property
   def 	__magic_name__   (							self							:					Tuple						):
     return self.image_processor_tester.prepare_image_processor_dict()



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[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_center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__A,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__A,		'''image_std'''						)						)
     self.assertTrue(hasattr(__A,		'''do_convert_rgb'''						)						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size,		{'''height''': 2_2_4, '''width''': 2_2_4}						)
     self.assertEqual(image_processor.crop_size,		{'''height''': 1_8, '''width''': 1_8}						)

     UpperCAmelCase	:							int						=			self.image_processing_class.from_dict(self.image_processor_dict,		size=4_2,		crop_size=8_4						)
     self.assertEqual(image_processor.size,		{'''shortest_edge''': 4_2}						)
     self.assertEqual(image_processor.crop_size,		{'''height''': 8_4, '''width''': 8_4}						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     pass



   def 	__magic_name__   (							self							:					Tuple						):
     # Initialize image_processing
     UpperCAmelCase	:							Dict						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     UpperCAmelCase	:							Optional[int]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		Image.Image						)

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



   def 	__magic_name__   (							self							:					Optional[Any]						):
     # Initialize image_processing
     UpperCAmelCase	:							List[str]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     UpperCAmelCase	:							List[Any]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A,		numpify=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		np.ndarray						)

     # Test not batched input
     UpperCAmelCase	:							List[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
     UpperCAmelCase	:							Dict						=			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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ),		)





   def 	__magic_name__   (							self							:					Any						):
     # Initialize image_processing
     UpperCAmelCase	:							str						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     UpperCAmelCase	:							Optional[Any]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A,		torchify=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		torch.Tensor						)

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






@require_torch
@require_vision
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					ChineseCLIPImageProcessor if is_vision_available() else None



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							str						=			ChineseCLIPImageProcessingTester(self,		num_channels=4,		do_center_crop=__A						)
     UpperCAmelCase	:							Dict						=			3



   @property
   def 	__magic_name__   (							self							:					List[Any]						):
     return self.image_processor_tester.prepare_image_processor_dict()



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							List[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_center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''center_crop'''						)						)
     self.assertTrue(hasattr(__A,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__A,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__A,		'''image_std'''						)						)
     self.assertTrue(hasattr(__A,		'''do_convert_rgb'''						)						)



   def 	__magic_name__   (							self							:					List[Any]						):
     pass





   def 	__magic_name__   (							self							:					Tuple						):
     # Initialize image_processing
     UpperCAmelCase	:							Optional[Any]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     UpperCAmelCase	:							Dict						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		Image.Image						)

     # Test not batched input
     UpperCAmelCase	:							Tuple						=			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
     UpperCAmelCase	:							Tuple						=			image_processing(__A,		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'''],
         ),		)





