





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  :Dict						,    __lowercase  :Optional[Any]						,    __lowercase  :Union[str, Any]=7						,    __lowercase  :Any=3						,    __lowercase  :List[str]=18						,    __lowercase  :Any=30						,    __lowercase  :Tuple=400						,    __lowercase  :Any=True						,    __lowercase  :Tuple=None						,    __lowercase  :Optional[Any]=True						,    __lowercase  :str=None						,    __lowercase  :str=True						,    ):
											__lowerCamelCase      :      Optional[int]         =size if size is not None else {"shortest_edge": 20}
											__lowerCamelCase      :      int         =crop_size if crop_size is not None else {"height": 18, "width": 18}
											__lowerCamelCase      :      Union[str, Any]         =parent
											__lowerCamelCase      :      Any         =batch_size
											__lowerCamelCase      :      Dict         =num_channels
											__lowerCamelCase      :      List[str]         =image_size
											__lowerCamelCase      :      Optional[int]         =min_resolution
											__lowerCamelCase      :      List[str]         =max_resolution
											__lowerCamelCase      :      str         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Optional[int]         =do_center_crop
											__lowerCamelCase      :      List[Any]         =crop_size
											__lowerCamelCase      :      Optional[Any]         =do_flip_channel_order




				def 					__lowercase  (     self  :Union[str, Any]			):
											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_							(    UpperCamelCase_       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       str	 =		MobileViTImageProcessor if is_vision_available() else None




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      Dict         =MobileViTImageProcessingTester(self			)




				@property
				def 					__lowercase  (     self  :List[str]			):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      Optional[int]         =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_flip_channel_order'''			)			)




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =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      :      Dict         =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 					__lowercase  (     self  :Union[str, Any]			):
											pass




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

											# Test not batched input
											__lowerCamelCase      :      List[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
											__lowerCamelCase      :      Optional[int]         =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 					__lowercase  (     self  :str			):
											# Initialize image_processing
											__lowerCamelCase      :      List[str]         =self.image_processing_class(**self.image_processor_dict			)
											# create random numpy tensors
											__lowerCamelCase      :      Tuple         =prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__A						,    numpify=__A			)
											for image in image_inputs:
																		self.assertIsInstance(__A						,    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      :      Union[str, 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 					__lowercase  (     self  :int			):
											# Initialize image_processing
											__lowerCamelCase      :      Optional[Any]         =self.image_processing_class(**self.image_processor_dict			)
											# create random PyTorch tensors
											__lowerCamelCase      :      Any         =prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__A						,    torchify=__A			)
											for image in image_inputs:
																		self.assertIsInstance(__A						,    torch.Tensor			)

											# 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      :      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'''],
											    )						,    )











"""simple docstring"""





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							try:
														with open(SCREAMING_SNAKE_CASE    ,						'''rb'''   ) as flax_state_f:
																					__lowerCamelCase      :      int         =from_bytes(SCREAMING_SNAKE_CASE    ,						flax_state_f.read()   )
							except UnpicklingError as e:
														try:
																					with open(SCREAMING_SNAKE_CASE   ) as f:
																												if f.read().startswith('''version'''   ):
																																			raise OSError(
																																			    '''You seem to have cloned a repository without having git-lfs installed. Please'''
																																			    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
																																			    ''' folder you cloned.'''   )
																												else:
																																			raise ValueError from e
														except (UnicodeDecodeError, ValueError):
																					raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '   )

							return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
														    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
														    ''' instructions.'''   )
														raise

							# check if we have bf16 weights
							__lowerCamelCase      :      str         =flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE   : x.dtype == jnp.bfloataa    ,						SCREAMING_SNAKE_CASE   )   ).values()
							if any(SCREAMING_SNAKE_CASE   ):
														# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

														# and bf16 is not fully supported in PT yet.
														logger.warning(
														    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
														    '''before loading those in PyTorch model.'''   )
														__lowerCamelCase      :      List[str]         =jax.tree_util.tree_map(
														    lambda SCREAMING_SNAKE_CASE   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      int         =''''''

							__lowerCamelCase      :      List[str]         =flatten_dict(SCREAMING_SNAKE_CASE    ,						sep='''.'''   )
							__lowerCamelCase      :      List[str]         =pt_model.state_dict()

							# keep track of unexpected & missing keys
							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      int         =set(pt_model_dict.keys()   )

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														__lowerCamelCase      :      Tuple         =flax_key_tuple.split('''.'''   )

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					__lowerCamelCase      :      Optional[int]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Union[str, Any]         =jnp.transpose(SCREAMING_SNAKE_CASE    ,						(3, 2, 0, 1)   )
														elif flax_key_tuple_array[-1] == "kernel":
																					__lowerCamelCase      :      Optional[Any]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Dict         =flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					__lowerCamelCase      :      str         =flax_key_tuple_array[:-1] + ['''weight''']

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE   ):
																												__lowerCamelCase      :      Union[str, Any]         =(
																												    flax_key_tuple_string.replace('''_0'''    ,						'''.0'''   )
																												    .replace('''_1'''    ,						'''.1'''   )
																												    .replace('''_2'''    ,						'''.2'''   )
																												    .replace('''_3'''    ,						'''.3'''   )
																												    .replace('''_4'''    ,						'''.4'''   )
																												    .replace('''_5'''    ,						'''.5'''   )
																												    .replace('''_6'''    ,						'''.6'''   )
																												    .replace('''_7'''    ,						'''.7'''   )
																												    .replace('''_8'''    ,						'''.8'''   )
																												    .replace('''_9'''    ,						'''.9'''   )
																												)

														__lowerCamelCase      :      Optional[int]         ='''.'''.join(SCREAMING_SNAKE_CASE   )

														if flax_key in pt_model_dict:
																					if flax_tensor.shape != pt_model_dict[flax_key].shape:
																												raise ValueError(
																												    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
																												    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'   )
																					else:
																												# add weight to pytorch dict
																												__lowerCamelCase      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   ) if not isinstance(SCREAMING_SNAKE_CASE    ,						np.ndarray   ) else flax_tensor
																												__lowerCamelCase      :      Any         =torch.from_numpy(SCREAMING_SNAKE_CASE   )
																												# remove from missing keys
																												missing_keys.remove(SCREAMING_SNAKE_CASE   )
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(SCREAMING_SNAKE_CASE   )

							pt_model.load_state_dict(SCREAMING_SNAKE_CASE   )

							# re-transform missing_keys to list
							__lowerCamelCase      :      Optional[Any]         =list(SCREAMING_SNAKE_CASE   )

							if len(SCREAMING_SNAKE_CASE   ) > 0:
														logger.warning(
														    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
														    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
														    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
														    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
														    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
														    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
														    ''' FlaxBertForSequenceClassification model).'''   )
							if len(SCREAMING_SNAKE_CASE   ) > 0:
														logger.warning(
														    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
														    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
														    ''' use it for predictions and inference.'''   )

							return pt_model




