

'''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, 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      ):







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		str=7		,					UpperCAmelCase__     :		Tuple=3		,					UpperCAmelCase__     :		Optional[Any]=18		,					UpperCAmelCase__     :		Union[str, Any]=30		,					UpperCAmelCase__     :		List[Any]=400		,					UpperCAmelCase__     :		List[Any]=True		,					UpperCAmelCase__     :		Optional[Any]=None		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		List[str]=None		,					UpperCAmelCase__     :		Dict=True		,					):
										'''simple docstring'''
										lowercase :							int    		=size if size is not None else {'''shortest_edge''': 20}
										lowercase :							List[Any]    		=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										lowercase :							Optional[Any]    		=parent
										lowercase :							List[Any]    		=batch_size
										lowercase :							str    		=num_channels
										lowercase :							Dict    		=image_size
										lowercase :							List[Any]    		=min_resolution
										lowercase :							Dict    		=max_resolution
										lowercase :							Dict    		=do_resize
										lowercase :							Union[str, Any]    		=size
										lowercase :							Dict    		=do_center_crop
										lowercase :							Optional[Any]    		=crop_size
										lowercase :							Any    		=do_flip_channel_order







				def  lowerCamelCase_	(       self     :		List[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							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    MobileViTImageProcessor if is_vision_available() else None







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=MobileViTImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=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_flip_channel_order'''   )   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=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}   )

										lowercase :							str    		=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  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Union[str, Any]    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowercase :							List[str]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					Image.Image   )

										# 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 :							Optional[int]    		=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  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Union[str, Any]    		=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowercase :							Optional[Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					numpify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					np.ndarray   )

										# Test not batched input
										lowercase :							Optional[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
										lowercase :							Tuple    		=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  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Tuple    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowercase :							Any    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					torchify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					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 :							Optional[int]    		=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'''],
										    )		,					)








'''simple docstring'''





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_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Dict:
						lowercase :							List[str]    		=R'''\w+[.]\d+'''
						lowercase :							List[str]    		=re.findall(__magic_name__   , __magic_name__						)
						for pat in pats:
												lowercase :							Optional[int]    		=key.replace(__magic_name__   , '''_'''.join(pat.split('''.'''						)						)						)
						return key






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Dict						)				->  str:
						lowercase :							Dict    		=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 :							str    		=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 :							str    		=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 :							Dict    		=pt_tuple_key[:-1] + ('''embedding''',)
												return renamed_pt_tuple_key, pt_tensor

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

						# linear layer
						lowercase :							str    		=pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight":
												lowercase :							Optional[Any]    		=pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

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

						# old PyTorch layer norm bias
						lowercase :							Union[str, Any]    		=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 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Union[str, Any]=42						)				->  List[str]:
						# Step 1: Convert pytorch tensor to numpy
						lowercase :							Optional[Any]    		={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(__magic_name__						)						)

						lowercase :							Dict    		=flatten_dict(__magic_name__						)
						lowercase :							Dict    		={}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowercase :							Dict    		=rename_key(__magic_name__						)
												lowercase :							Optional[int]    		=tuple(renamed_pt_key.split('''.'''						)						)

												# Correctly rename weight parameters
												lowercase		,	lowercase :							Any    		=rename_key_and_reshape_tensor(__magic_name__   , __magic_name__   , __magic_name__						)

												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 :							Tuple    		=jnp.asarray(__magic_name__						)

						return unflatten_dict(__magic_name__						)





