





'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = tempfile.mkdtemp()

				# fmt: off
				a__     = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
				# fmt: on
				a__     = dict(zip(lowerCamelCase , range(len(lowerCamelCase		)		)		)		)
				a__     = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
				a__     = {"unk_token": "<unk>"}

				a__     = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"]		)
				a__     = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"]		)
				with open(self.vocab_file , "w" , encoding="utf-8"		) as fp:
							fp.write(json.dumps(lowerCamelCase		) + "\n"		)
				with open(self.merges_file , "w" , encoding="utf-8"		) as fp:
							fp.write("\n".join(lowerCamelCase		)		)

				a__     = {
				    "do_resize": True,
				    "size": 2_0,
				    "do_center_crop": True,
				    "crop_size": 1_8,
				    "do_normalize": True,
				    "image_mean": [0.48145466, 0.4578275, 0.40821073],
				    "image_std": [0.26862954, 0.26130258, 0.27577711],
				}
				a__     = os.path.join(self.tmpdirname , lowerCamelCase		)
				with open(self.image_processor_file , "w" , encoding="utf-8"		) as fp:
							json.dump(lowerCamelCase , lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any] , **lowerCamelCase			:   int		):




				'''simple docstring'''
				return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase		)







	def 						__a   (				self			:   Optional[int] , **lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCamelCase		)







	def 						__a   (				self			:   Union[str, Any] , **lowerCamelCase			:   int		):




				'''simple docstring'''
				return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				shutil.rmtree(self.tmpdirname		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta		)]

				a__     = [Image.fromarray(np.moveaxis(lowerCamelCase , 0 , -1		)		) for x in image_inputs]

				return image_inputs







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = self.get_tokenizer()
				a__     = self.get_rust_tokenizer()
				a__     = self.get_image_processor()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)
				processor_slow.save_pretrained(self.tmpdirname		)
				a__     = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase		)

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)
				processor_fast.save_pretrained(self.tmpdirname		)
				a__     = CLIPSegProcessor.from_pretrained(self.tmpdirname		)

				self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab()		)
				self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab()		)
				self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab()		)
				self.assertIsInstance(processor_slow.tokenizer , lowerCamelCase		)
				self.assertIsInstance(processor_fast.tokenizer , lowerCamelCase		)

				self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string()		)
				self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string()		)
				self.assertIsInstance(processor_slow.image_processor , lowerCamelCase		)
				self.assertIsInstance(processor_fast.image_processor , lowerCamelCase		)







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				a__     = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)"		)
				a__     = self.get_image_processor(do_normalize=lowerCamelCase , padding_value=1.0		)

				a__     = CLIPSegProcessor.from_pretrained(
				    self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowerCamelCase , padding_value=1.0		)

				self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.tokenizer , lowerCamelCase		)

				self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor , lowerCamelCase		)







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

				a__     = self.prepare_image_inputs()

				a__     = image_processor(lowerCamelCase , return_tensors="np"		)
				a__     = processor(images=lowerCamelCase , return_tensors="np"		)

				for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

				a__     = "lower newer"

				a__     = processor(text=lowerCamelCase		)

				a__     = tokenizer(lowerCamelCase		)

				for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key] , encoded_processor[key]		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

				a__     = "lower newer"
				a__     = self.prepare_image_inputs()

				a__     = processor(text=lowerCamelCase , images=lowerCamelCase		)

				self.assertListEqual(list(inputs.keys()		) , ["input_ids", "attention_mask", "pixel_values"]		)

				# test if it raises when no input is passed
				with pytest.raises(lowerCamelCase		):
							processor()







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

				a__     = self.prepare_image_inputs()
				a__     = self.prepare_image_inputs()

				a__     = processor(images=lowerCamelCase , visual_prompt=lowerCamelCase		)

				self.assertListEqual(list(inputs.keys()		) , ["pixel_values", "conditional_pixel_values"]		)

				# test if it raises when no input is passed
				with pytest.raises(lowerCamelCase		):
							processor()







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

				a__     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				a__     = processor.batch_decode(lowerCamelCase		)
				a__     = tokenizer.batch_decode(lowerCamelCase		)

				self.assertListEqual(lowerCamelCase , lowerCamelCase		)










'''simple docstring'''


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


lowerCAmelCase_   :   Union[str, Any]       =					logging.get_logger(__name__)







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Union[str, Any]=False     )   ->    Dict:
			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    "Loading a PyTorch model in Flax, 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

			if not is_sharded:
						a__     = os.path.abspath(__lowerCamelCase     )
						logger.info(f'''Loading PyTorch weights from {pt_path}'''     )

						a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
						logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()     ):,} parameters.'''     )

						a__     = convert_pytorch_state_dict_to_flax(__lowerCamelCase					,  __lowerCamelCase     )
			else:
						# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
						a__     = convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase					,  __lowerCamelCase     )
			return flax_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple[str]					,  __lowerCamelCase  :     np.ndarray					,  __lowerCamelCase  :     Dict[str, jnp.ndarray]					,  __lowerCamelCase  :     str					,  )   ->    (Tuple[str], np.ndarray):

			def is_key_or_prefix_key_in_dict(__lowerCamelCase  :     Tuple[str]     ) -> bool:
						return len(set(__lowerCamelCase     ) & {key, (model_prefix,) + key}     ) > 0

			# layer norm
			a__     = pt_tuple_key[:-1] + ("scale",)
			if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCamelCase     ):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer mean
			a__     = pt_tuple_key[:-1] + ("mean",)
			if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCamelCase     ):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer var
			a__     = pt_tuple_key[:-1] + ("var",)
			if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCamelCase     ):
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			a__     = pt_tuple_key[:-1] + ("embedding",)
			if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCamelCase     ):
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			a__     = pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCamelCase     ):
						a__     = pt_tensor.transpose(2					,  3					,  1					,  0     )
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			a__     = pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCamelCase     ):
						a__     = pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

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

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

			# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
			a__     = None
			if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
						a__     = pt_tuple_key[-2] + "_g"
			elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
						a__     = pt_tuple_key[-2] + "_v"
			if name is not None:
						a__     = pt_tuple_key[:-3] + (name,)
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     List[Any]     )   ->    Any:
			# convert pytorch tensor to numpy
			a__     = {k: v.numpy() for k, v in pt_state_dict.items()}

			a__     = flax_model.base_model_prefix

			# use params dict if the model contains batch norm layers
			if "params" in flax_model.params:
						a__     = flax_model.params["params"]
			else:
						a__     = flax_model.params
			a__     = flatten_dict(__lowerCamelCase     )

			# add batch_stats keys,values to dict
			if "batch_stats" in flax_model.params:
						a__     = flatten_dict(flax_model.params["batch_stats"]     )
						random_flax_state_dict.update(__lowerCamelCase     )

			a__     = {}

			a__     = (model_prefix not in flax_model_params) and (
			    model_prefix in {k.split("."     )[0] for k in pt_state_dict.keys()}
			)
			a__     = (model_prefix in flax_model_params) and (
			    model_prefix not in {k.split("."     )[0] for k in pt_state_dict.keys()}
			)

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						a__     = tuple(pt_key.split("."     )     )

						# remove base model prefix if necessary
						a__     = pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
									a__     = pt_tuple_key[1:]

						# Correctly rename weight parameters
						a__   ,				a__     = rename_key_and_reshape_tensor(
						    __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

						# add model prefix if necessary
						a__     = (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									a__     = (model_prefix,) + flax_key

						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}.'''     )

        # add batch stats if the model contains batchnorm layers
						if "batch_stats" in flax_model.params:
									if "mean" in flax_key[-1] or "var" in flax_key[-1]:
												a__     = jnp.asarray(__lowerCamelCase     )
												continue
									# remove num_batches_tracked key
									if "num_batches_tracked" in flax_key[-1]:
												flax_state_dict.pop(__lowerCamelCase					,  __lowerCamelCase     )
												continue

									# also add unexpected weight so that warning is thrown
									a__     = jnp.asarray(__lowerCamelCase     )

						else:
									# also add unexpected weight so that warning is thrown
									a__     = jnp.asarray(__lowerCamelCase     )

			return unflatten_dict(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Any     )   ->    Union[str, Any]:
			import torch

			# Load the index
			a__     = {}
			for shard_file in shard_filenames:
						# load using msgpack utils
						a__     = torch.load(__lowerCamelCase     )
						a__     = {k: v.numpy() for k, v in pt_state_dict.items()}

						a__     = flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
									a__     = flax_model.params["params"]

									a__     = flatten_dict(__lowerCamelCase     )
									random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]     )     )
						else:
									a__     = flax_model.params
									a__     = flatten_dict(__lowerCamelCase     )

						a__     = (model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("."     )[0] for k in pt_state_dict.keys()}
						)
						a__     = (model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("."     )[0] for k in pt_state_dict.keys()}
						)
						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
									a__     = tuple(pt_key.split("."     )     )

									# remove base model prefix if necessary
									a__     = pt_tuple_key[0] == model_prefix
									if load_model_with_head_into_base_model and has_base_model_prefix:
												a__     = pt_tuple_key[1:]

									# Correctly rename weight parameters
									a__   ,				a__     = rename_key_and_reshape_tensor(
									    __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
									# add model prefix if necessary
									a__     = (model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												a__     = (model_prefix,) + flax_key

									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}.'''     )

            # add batch stats if the model contains batchnorm layers
									if "batch_stats" in flax_model.params:
												if "mean" in flax_key[-1]:
															a__     = jnp.asarray(__lowerCamelCase     )
															continue
												if "var" in flax_key[-1]:
															a__     = jnp.asarray(__lowerCamelCase     )
															continue
												# remove num_batches_tracked key
												if "num_batches_tracked" in flax_key[-1]:
															flax_state_dict.pop(__lowerCamelCase					,  __lowerCamelCase     )
															continue

												# also add unexpected weight so that warning is thrown
												a__     = jnp.asarray(__lowerCamelCase     )

									else:
												# also add unexpected weight so that warning is thrown
												a__     = jnp.asarray(__lowerCamelCase     )
			return unflatten_dict(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple     )   ->    Any:
			a__     = os.path.abspath(__lowerCamelCase     )
			logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''     )

			# import correct flax class
			a__     = getattr(__lowerCamelCase					,  "Flax" + model.__class__.__name__     )

			# load flax weight dict
			with open(__lowerCamelCase					,  "rb"     ) as state_f:
						try:
									a__     = from_bytes(__lowerCamelCase					,  state_f.read()     )
						except UnpicklingError:
									raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''     )

			return load_flax_weights_in_pytorch_model(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     Tuple     )   ->    Dict:

			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    "Loading a 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
			a__     = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase     : x.dtype == jnp.bfloataa					,  __lowerCamelCase     )     ).values()
			if any(__lowerCamelCase     ):
						# convert all weights to fp32 if the 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."     )
						a__     = jax.tree_util.tree_map(
						    lambda __lowerCamelCase     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params					,  __lowerCamelCase     )

			a__     = flatten_dict(__lowerCamelCase     )
			a__     = pt_model.state_dict()

			a__     = (pt_model.base_model_prefix in flax_state) and (
			    pt_model.base_model_prefix not in {k.split("."     )[0] for k in pt_model_dict.keys()}
			)
			a__     = (pt_model.base_model_prefix not in flax_state) and (
			    pt_model.base_model_prefix in {k.split("."     )[0] for k in pt_model_dict.keys()}
			)

			# keep track of unexpected & missing keys
			a__     = []
			a__     = set(pt_model_dict.keys()     )

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						a__     = flax_key_tuple[0] == pt_model.base_model_prefix
						a__     = ".".join((pt_model.base_model_prefix,) + flax_key_tuple     ) in pt_model_dict

						# adapt flax_key to prepare for loading from/to base model only
						if load_model_with_head_into_base_model and has_base_model_prefix:
									a__     = flax_key_tuple[1:]
						elif load_base_model_into_model_with_head and require_base_model_prefix:
									a__     = (pt_model.base_model_prefix,) + flax_key_tuple

						# rename flax weights to PyTorch format
						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCamelCase     ) not in pt_model_dict:
									# conv layer
									a__     = flax_key_tuple[:-1] + ("weight",)
									a__     = jnp.transpose(__lowerCamelCase					,  (3, 2, 0, 1)     )
						elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase     ) not in pt_model_dict:
									# linear layer
									a__     = flax_key_tuple[:-1] + ("weight",)
									a__     = flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
									a__     = flax_key_tuple[:-1] + ("weight",)

						# adding batch stats from flax batch norm to pt
						elif "mean" in flax_key_tuple[-1]:
									a__     = flax_key_tuple[:-1] + ("running_mean",)
						elif "var" in flax_key_tuple[-1]:
									a__     = flax_key_tuple[:-1] + ("running_var",)

						if "batch_stats" in flax_state:
									a__     = ".".join(flax_key_tuple[1:]     )  # Remove the params/batch_stats header
						else:
									a__     = ".".join(__lowerCamelCase     )

						# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
						a__     = {}
						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						for key in pt_model_dict:
									a__     = key.split("."     )
									a__     = None
									if key_components[-3::2] == ["parametrizations", "original0"]:
												a__     = key_components[-2] + "_g"
									elif key_components[-3::2] == ["parametrizations", "original1"]:
												a__     = key_components[-2] + "_v"
									if name is not None:
												a__     = key_components[:-3] + [name]
												a__     = ".".join(__lowerCamelCase     )
												a__     = key

						if flax_key in special_pt_names:
									a__     = special_pt_names[flax_key]

						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
												a__     = np.asarray(__lowerCamelCase     ) if not isinstance(__lowerCamelCase					,  np.ndarray     ) else flax_tensor
												a__     = torch.from_numpy(__lowerCamelCase     )
												# remove from missing keys
												missing_keys.remove(__lowerCamelCase     )
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(__lowerCamelCase     )

			pt_model.load_state_dict(__lowerCamelCase     )

			# re-transform missing_keys to list
			a__     = list(__lowerCamelCase     )

			if len(__lowerCamelCase     ) > 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)."     )
			else:
						logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''     )
			if len(__lowerCamelCase     ) > 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."     )
			else:
						logger.warning(
						    f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
						    "If your task is similar to the task the model of the checkpoint was trained on, "
						    f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''     )

			return pt_model



