




'''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    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					int       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					List[str]=7       ,					_lowerCAmelCase:					List[Any]=3       ,					_lowerCAmelCase:					List[Any]=18       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					Tuple=True       ,					_lowerCAmelCase:					Any=None       ,					_lowerCAmelCase:					str=True       ,					_lowerCAmelCase:					Tuple=None       ,					_lowerCAmelCase:					Optional[Any]=True       ,					_lowerCAmelCase:					Union[str, Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,					_lowerCAmelCase:					Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,					_lowerCAmelCase:					int=True       ,					):
         lowercase    :List[Any]      	=						size if size is not None else {"height": 2_24, "width": 2_24}
         lowercase    :Optional[Any]      	=						crop_size if crop_size is not None else {"height": 18, "width": 18}
         lowercase    :Dict      	=						parent
         lowercase    :Tuple      	=						batch_size
         lowercase    :Union[str, Any]      	=						num_channels
         lowercase    :Any      	=						image_size
         lowercase    :List[str]      	=						min_resolution
         lowercase    :Optional[int]      	=						max_resolution
         lowercase    :Any      	=						do_resize
         lowercase    :str      	=						size
         lowercase    :int      	=						do_center_crop
         lowercase    :Union[str, Any]      	=						crop_size
         lowercase    :List[Any]      	=						do_normalize
         lowercase    :Tuple      	=						image_mean
         lowercase    :Optional[int]      	=						image_std
         lowercase    :List[Any]      	=						do_convert_rgb






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

         if equal_resolution:
           lowercase    :Any      	=						[]
           for i in range(self.batch_size				):
             image_inputs.append(
                 np.random.randint(
                     2_55       ,					size=(self.num_channels, self.max_resolution, self.max_resolution)       ,					dtype=np.uinta				)				)
         else:
           lowercase    :Any      	=						[]
           for i in range(self.batch_size				):
             lowercase    :Tuple      	=						np.random.choice(np.arange(self.min_resolution       ,					self.max_resolution				)       ,					2				)
             image_inputs.append(np.random.randint(2_55       ,					size=(self.num_channels, width, height)       ,					dtype=np.uinta				)				)

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

         if torchify:
           lowercase    :Any      	=						[torch.from_numpy(_lowerCAmelCase				) for x in image_inputs]

         return image_inputs







@require_torch
@require_vision
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						ChineseCLIPImageProcessor if is_vision_available() else None






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :int      	=						ChineseCLIPImageProcessingTester(self       ,					do_center_crop=_lowerCAmelCase				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         return self.image_processor_tester.prepare_image_processor_dict()






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Any      	=						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_normalize"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"image_mean"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"image_std"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_convert_rgb"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :List[str]      	=						self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size       ,					{"height": 2_24, "width": 2_24}				)
         self.assertEqual(image_processor.crop_size       ,					{"height": 18, "width": 18}				)

         lowercase    :Optional[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:					Optional[Any]				):
         pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         # Initialize image_processing
         lowercase    :Optional[Any]      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :Any      	=						self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					Image.Image				)

         # Test not batched input
         lowercase    :Dict      	=						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
         lowercase    :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"],
             )       ,					)






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

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

         # Test batched
         lowercase    :Optional[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:					List[Any]				):
         # Initialize image_processing
         lowercase    :Union[str, Any]      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         lowercase    :Optional[Any]      	=						self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase       ,					torchify=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					torch.Tensor				)

         # Test not batched input
         lowercase    :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
         lowercase    :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"],
             )       ,					)







@require_torch
@require_vision
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						ChineseCLIPImageProcessor if is_vision_available() else None






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Dict      	=						ChineseCLIPImageProcessingTester(self       ,					num_channels=4       ,					do_center_crop=_lowerCAmelCase				)
         lowercase    :str      	=						3






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         return self.image_processor_tester.prepare_image_processor_dict()






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :str      	=						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_normalize"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"image_mean"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"image_std"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_convert_rgb"				)				)






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






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize image_processing
         lowercase    :Any      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :Optional[Any]      	=						self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					Image.Image				)

         # Test not batched input
         lowercase    :Union[str, Any]      	=						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
         lowercase    :int      	=						image_processing(_lowerCAmelCase       ,					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"],
             )       ,					)









import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_UpperCAmelCase	:						List[Any]			=     logging.get_logger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :int      	=						r"\w+[.]\d+"
  lowercase    :Tuple      	=						re.findall(lowerCamelCase,			lowerCamelCase   )
  for pat in pats:
    lowercase    :List[str]      	=						key.replace(lowerCamelCase,			"_".join(pat.split("."   )   )   )
  return key



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[str]      	=						pt_tuple_key[:-1] + ("scale",)
  if (
      any("norm" in str_ for str_ in pt_tuple_key   )
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    lowercase    :Optional[int]      	=						pt_tuple_key[:-1] + ("scale",)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    lowercase    :int      	=						pt_tuple_key[:-1] + ("scale",)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    lowercase    :List[str]      	=						pt_tuple_key[:-1] + ("embedding",)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowercase    :Union[str, Any]      	=						pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    lowercase    :List[Any]      	=						pt_tensor.transpose(2,			3,			1,			0   )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowercase    :Optional[Any]      	=						pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight":
    lowercase    :Tuple      	=						pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  lowercase    :Tuple      	=						pt_tuple_key[:-1] + ("weight",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  lowercase    :int      	=						pt_tuple_key[:-1] + ("bias",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase=42   ):
  # Step 1: Convert pytorch tensor to numpy
  lowercase    :Optional[int]      	=						{k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  lowercase    :str      	=						flax_model.init_weights(PRNGKey(lowerCamelCase   )   )

  lowercase    :Tuple      	=						flatten_dict(lowerCamelCase   )
  lowercase    :Optional[Any]      	=						{}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowercase    :List[Any]      	=						rename_key(lowerCamelCase   )
    lowercase    :List[Any]      	=						tuple(renamed_pt_key.split("."   )   )

    # Correctly rename weight parameters
    lowercase	,    lowercase    :List[str]      	=						rename_key_and_reshape_tensor(lowerCamelCase,			lowerCamelCase,			lowerCamelCase   )

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

        # also add unexpected weight so that warning is thrown
    lowercase    :List[str]      	=						jnp.asarray(lowerCamelCase   )

  return unflatten_dict(lowerCamelCase   )



