


'''simple docstring'''


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=True    ,					__snake_case=None    ,					__snake_case=True    ,					__snake_case=1 / 255    ,					__snake_case=True    ,					__snake_case=[0.5, 0.5, 0.5]    ,					__snake_case=[0.5, 0.5, 0.5]    ,					__snake_case=True    ,					):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
											_SCREAMING_SNAKE_CASE :       int     		=      parent
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      num_channels
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      do_rescale
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      rescale_factor
											_SCREAMING_SNAKE_CASE :       List[str]     		=      do_normalize
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      image_mean
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      image_std
											_SCREAMING_SNAKE_CASE :       Dict     		=      do_pad






							def UpperCAmelCase_	(    self  ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_pad": self.do_pad,
											}






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case=False  ):
											if not batched:
															_SCREAMING_SNAKE_CASE :       int     		=      image_inputs[0]
															if isinstance(__snake_case    ,					Image.Image  ):
																			_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      image.size
															else:
																			_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      image.shape[1], image.shape[2]
															if w < h:
																			_SCREAMING_SNAKE_CASE :       List[Any]     		=      int(self.size["""shortest_edge"""] * h / w  )
																			_SCREAMING_SNAKE_CASE :       List[str]     		=      self.size["""shortest_edge"""]
															elif w > h:
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.size["""shortest_edge"""]
																			_SCREAMING_SNAKE_CASE :       Optional[int]     		=      int(self.size["""shortest_edge"""] * w / h  )
															else:
																			_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.size["""shortest_edge"""]
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.size["""shortest_edge"""]

											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      []
															for image in image_inputs:
																			_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      self.get_expected_values([image]  )
																			expected_values.append((expected_height, expected_width)  )
															_SCREAMING_SNAKE_CASE :       Tuple     		=      max(__snake_case    ,					key=lambda __snake_case  : item[0]  )[0]
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      max(__snake_case    ,					key=lambda __snake_case  : item[1]  )[1]

											return expected_height, expected_width





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Union[str, Any]      	=  DetrImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      DetrImageProcessingTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""image_mean"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""image_std"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_normalize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_rescale"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""rescale_factor"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_pad"""  )  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 18, """longest_edge""": 1333}  )
											self.assertEqual(image_processor.do_pad    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.image_processing_class.from_dict(
											    self.image_processor_dict    ,					size=42    ,					max_size=84    ,					pad_and_return_pixel_mask=__snake_case  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42, """longest_edge""": 84}  )
											self.assertEqual(image_processor.do_pad    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											pass






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

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       List[str]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      self.image_processor_tester.get_expected_values(__snake_case  )

											self.assertEqual(
											    encoded_images.shape    ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Any     		=      self.image_processor_tester.get_expected_values(__snake_case    ,					batched=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values
											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Any     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE :       List[str]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					numpify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					np.ndarray  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       List[str]     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      self.image_processor_tester.get_expected_values(__snake_case  )

											self.assertEqual(
											    encoded_images.shape    ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       Any     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      self.image_processor_tester.get_expected_values(__snake_case    ,					batched=__snake_case  )

											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       List[str]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					torchify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					torch.Tensor  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       str     		=      image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      self.image_processor_tester.get_expected_values(__snake_case  )

											self.assertEqual(
											    encoded_images.shape    ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  ).pixel_values

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Dict     		=      self.image_processor_tester.get_expected_values(__snake_case    ,					batched=__snake_case  )

											self.assertEqual(
											    encoded_images.shape    ,					(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )    ,					)






							@slow
							def UpperCAmelCase_	(    self  ):
											# prepare image and target
											_SCREAMING_SNAKE_CASE :       str     		=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
											with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,					"""r"""  ) as f:
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      json.loads(f.read()  )

											_SCREAMING_SNAKE_CASE :       str     		=      {"""image_id""": 3_9769, """annotations""": target}

											# encode them
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""  )
											_SCREAMING_SNAKE_CASE :       int     		=      image_processing(images=__snake_case    ,					annotations=__snake_case    ,					return_tensors="""pt"""  )

											# verify pixel values
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.Size([1, 3, 800, 1066]  )
											self.assertEqual(encoding["""pixel_values"""].shape    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       List[str]     		=      torch.tensor([0.2796, 0.3138, 0.3481]  )
											self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,					__snake_case    ,					atol=1e-4  )  )

											# verify area
											_SCREAMING_SNAKE_CASE :       Any     		=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,					__snake_case  )  )
											# verify boxes
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.Size([6, 4]  )
											self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       int     		=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,					__snake_case    ,					atol=1e-3  )  )
											# verify image_id
											_SCREAMING_SNAKE_CASE :       int     		=      torch.tensor([3_9769]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,					__snake_case  )  )
											# verify is_crowd
											_SCREAMING_SNAKE_CASE :       Tuple     		=      torch.tensor([0, 0, 0, 0, 0, 0]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,					__snake_case  )  )
											# verify class_labels
											_SCREAMING_SNAKE_CASE :       Tuple     		=      torch.tensor([75, 75, 63, 65, 17, 17]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,					__snake_case  )  )
											# verify orig_size
											_SCREAMING_SNAKE_CASE :       int     		=      torch.tensor([480, 640]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,					__snake_case  )  )
											# verify size
											_SCREAMING_SNAKE_CASE :       Any     		=      torch.tensor([800, 1066]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,					__snake_case  )  )






							@slow
							def UpperCAmelCase_	(    self  ):
											# prepare image, target and masks_path
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
											with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,					"""r"""  ) as f:
															_SCREAMING_SNAKE_CASE :       int     		=      json.loads(f.read()  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""  )

											# encode them
											_SCREAMING_SNAKE_CASE :       Tuple     		=      DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      image_processing(images=__snake_case    ,					annotations=__snake_case    ,					masks_path=__snake_case    ,					return_tensors="""pt"""  )

											# verify pixel values
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.Size([1, 3, 800, 1066]  )
											self.assertEqual(encoding["""pixel_values"""].shape    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.tensor([0.2796, 0.3138, 0.3481]  )
											self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,					__snake_case    ,					atol=1e-4  )  )

											# verify area
											_SCREAMING_SNAKE_CASE :       Tuple     		=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,					__snake_case  )  )
											# verify boxes
											_SCREAMING_SNAKE_CASE :       str     		=      torch.Size([6, 4]  )
											self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,					__snake_case    ,					atol=1e-3  )  )
											# verify image_id
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.tensor([3_9769]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,					__snake_case  )  )
											# verify is_crowd
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.tensor([0, 0, 0, 0, 0, 0]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,					__snake_case  )  )
											# verify class_labels
											_SCREAMING_SNAKE_CASE :       int     		=      torch.tensor([17, 17, 63, 75, 75, 93]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,					__snake_case  )  )
											# verify masks
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      82_2873
											self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,					__snake_case  )
											# verify orig_size
											_SCREAMING_SNAKE_CASE :       str     		=      torch.tensor([480, 640]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,					__snake_case  )  )
											# verify size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.tensor([800, 1066]  )
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,					__snake_case  )  )








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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				try:
								with open(SCREAMING_SNAKE_CASE__							,      """rb"""     ) as flax_state_f:
												_SCREAMING_SNAKE_CASE :       Dict     		=      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 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""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
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      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."""     )
								_SCREAMING_SNAKE_CASE :       Dict     		=      jax.tree_util.tree_map(
								    lambda SCREAMING_SNAKE_CASE__     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params							,      SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      """"""

				_SCREAMING_SNAKE_CASE :       str     		=      flatten_dict(SCREAMING_SNAKE_CASE__							,      sep="""."""     )
				_SCREAMING_SNAKE_CASE :       str     		=      pt_model.state_dict()

				# keep track of unexpected & missing keys
				_SCREAMING_SNAKE_CASE :       Tuple     		=      []
				_SCREAMING_SNAKE_CASE :       int     		=      set(pt_model_dict.keys()     )

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								_SCREAMING_SNAKE_CASE :       Any     		=      flax_key_tuple.split("""."""     )

								if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
												_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      flax_key_tuple_array[:-1] + ["""weight"""]
												_SCREAMING_SNAKE_CASE :       List[str]     		=      jnp.transpose(SCREAMING_SNAKE_CASE__							,      (3, 2, 0, 1)     )
								elif flax_key_tuple_array[-1] == "kernel":
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      flax_key_tuple_array[:-1] + ["""weight"""]
												_SCREAMING_SNAKE_CASE :       Any     		=      flax_tensor.T
								elif flax_key_tuple_array[-1] == "scale":
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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__     ):
																_SCREAMING_SNAKE_CASE :       Optional[int]     		=      (
																    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"""     )
																)

								_SCREAMING_SNAKE_CASE :       Tuple     		=      """.""".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
																_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      np.asarray(SCREAMING_SNAKE_CASE__     ) if not isinstance(SCREAMING_SNAKE_CASE__							,      np.ndarray     ) else flax_tensor
																_SCREAMING_SNAKE_CASE :       int     		=      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
				_SCREAMING_SNAKE_CASE :       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




