






import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=1_3       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=2       ,    __UpperCamelCase=3       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=[3_2, 6_4, 1_2_8]       ,    __UpperCamelCase=[1, 2, 1]       ,    __UpperCamelCase=[2, 2, 4]       ,    __UpperCamelCase=2       ,    __UpperCamelCase=2.0       ,    __UpperCamelCase=True       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=False       ,    __UpperCamelCase=True       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase=1e-5       ,    __UpperCamelCase=True       ,    __UpperCamelCase=None       ,    __UpperCamelCase=True       ,    __UpperCamelCase=1_0       ,    __UpperCamelCase=8       ,    __UpperCamelCase=["stage1", "stage2"]       ,    __UpperCamelCase=[1, 2]       ,    )->						str:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       image_size
           __lowerCAmelCase             =       patch_size
           __lowerCAmelCase             =       num_channels
           __lowerCAmelCase             =       embed_dim
           __lowerCAmelCase             =       hidden_sizes
           __lowerCAmelCase             =       depths
           __lowerCAmelCase             =       num_heads
           __lowerCAmelCase             =       window_size
           __lowerCAmelCase             =       mlp_ratio
           __lowerCAmelCase             =       qkv_bias
           __lowerCAmelCase             =       hidden_dropout_prob
           __lowerCAmelCase             =       attention_probs_dropout_prob
           __lowerCAmelCase             =       drop_path_rate
           __lowerCAmelCase             =       hidden_act
           __lowerCAmelCase             =       use_absolute_embeddings
           __lowerCAmelCase             =       patch_norm
           __lowerCAmelCase             =       layer_norm_eps
           __lowerCAmelCase             =       initializer_range
           __lowerCAmelCase             =       is_training
           __lowerCAmelCase             =       scope
           __lowerCAmelCase             =       use_labels
           __lowerCAmelCase             =       type_sequence_label_size
           __lowerCAmelCase             =       encoder_stride
           __lowerCAmelCase             =       out_features
           __lowerCAmelCase             =       out_indices


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

           __lowerCAmelCase             =       None
           if self.use_labels:
                __lowerCAmelCase             =       ids_tensor([self.batch_size]       ,    self.type_sequence_label_size						)

           __lowerCAmelCase             =       self.get_config()

           return config, pixel_values, labels


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           return FocalNetConfig(
               image_size=self.image_size       ,    patch_size=self.patch_size       ,    num_channels=self.num_channels       ,    embed_dim=self.embed_dim       ,    hidden_sizes=self.hidden_sizes       ,    depths=self.depths       ,    num_heads=self.num_heads       ,    window_size=self.window_size       ,    mlp_ratio=self.mlp_ratio       ,    qkv_bias=self.qkv_bias       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    drop_path_rate=self.drop_path_rate       ,    hidden_act=self.hidden_act       ,    use_absolute_embeddings=self.use_absolute_embeddings       ,    path_norm=self.patch_norm       ,    layer_norm_eps=self.layer_norm_eps       ,    initializer_range=self.initializer_range       ,    encoder_stride=self.encoder_stride       ,    out_features=self.out_features       ,    out_indices=self.out_indices       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       FocalNetModel(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)

           __lowerCAmelCase             =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
           __lowerCAmelCase             =       int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

           self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, expected_seq_len, expected_dim)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       FocalNetBackbone(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps						)       ,    len(config.out_features						)						)
           self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,    [self.batch_size, self.image_size, 8, 8]						)

           # verify channels
           self.parent.assertEqual(len(model.channels						)       ,    len(config.out_features						)						)
           self.parent.assertListEqual(model.channels       ,    config.hidden_sizes[:-1]						)

           # verify backbone works with out_features=None
           __lowerCAmelCase             =       None
           __lowerCAmelCase             =       FocalNetBackbone(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps						)       ,    1						)
           self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,    [self.batch_size, self.image_size * 2, 4, 4]						)

           # verify channels
           self.parent.assertEqual(len(model.channels						)       ,    1						)
           self.parent.assertListEqual(model.channels       ,    [config.hidden_sizes[-1]]						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Any:
           __lowerCAmelCase             =       FocalNetForMaskedImageModeling(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)
           self.parent.assertEqual(
               result.reconstruction.shape       ,    (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

           # test greyscale images
           __lowerCAmelCase             =       1
           __lowerCAmelCase             =       FocalNetForMaskedImageModeling(__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()

           __lowerCAmelCase             =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
           __lowerCAmelCase             =       model(__UpperCamelCase						)
           self.parent.assertEqual(result.reconstruction.shape       ,    (self.batch_size, 1, self.image_size, self.image_size)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       self.type_sequence_label_size
           __lowerCAmelCase             =       FocalNetForImageClassification(__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase       ,    labels=__UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.type_sequence_label_size)						)

           # test greyscale images
           __lowerCAmelCase             =       1
           __lowerCAmelCase             =       FocalNetForImageClassification(__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()

           __lowerCAmelCase             =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
           __lowerCAmelCase             =       model(__UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.type_sequence_label_size)						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       self.prepare_config_and_inputs()

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       config_and_inputs
           __lowerCAmelCase             =       {"pixel_values": pixel_values}
           return config, inputs_dict



@require_torch
class 				_UpperCamelCase      (a_				,		a_				,		unittest.TestCase      ):
      snake_case_	=				(
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      snake_case_	=				(
          {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      snake_case_	=				False

      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       FocalNetModelTester(self						)
           __lowerCAmelCase             =       ConfigTester(self       ,    config_class=__UpperCamelCase       ,    embed_dim=3_7       ,    has_text_modality=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           return


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)


      @unittest.skip(reason="FocalNet does not use inputs_embeds"						)
      def 			__UpperCAmelCase		(						self						)->						List[str]:
           pass


      @unittest.skip(reason="FocalNet does not use feedforward chunking"						)
      def 			__UpperCAmelCase		(						self						)->						List[str]:
           pass


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                self.assertIsInstance(model.get_input_embeddings()       ,    (nn.Module)						)
                __lowerCAmelCase             =       model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(__UpperCamelCase       ,    nn.Linear						)						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                __lowerCAmelCase             =       inspect.signature(model.forward						)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                __lowerCAmelCase             =       [*signature.parameters.keys()]

                __lowerCAmelCase             =       ["pixel_values"]
                self.assertListEqual(arg_names[:1]       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						str:
           __lowerCAmelCase             =       model_class(__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()

           with torch.no_grad():
                __lowerCAmelCase             =       model(**self._prepare_for_class(__UpperCamelCase       ,    __UpperCamelCase						)						)

           __lowerCAmelCase             =       outputs.hidden_states

           __lowerCAmelCase             =       getattr(
               self.model_tester       ,    "expected_num_hidden_layers"       ,    len(self.model_tester.depths						) + 1						)
           self.assertEqual(len(__UpperCamelCase						)       ,    __UpperCamelCase						)

           # FocalNet has a different seq_length
           __lowerCAmelCase             =       (
               config.patch_size
               if isinstance(config.patch_size       ,    collections.abc.Iterable						)
               else (config.patch_size, config.patch_size)
           )

           __lowerCAmelCase             =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]						)       ,    [num_patches, self.model_tester.embed_dim]       ,    )

           __lowerCAmelCase             =       outputs.reshaped_hidden_states
           self.assertEqual(len(__UpperCamelCase						)       ,    __UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       reshaped_hidden_states[0].shape
           __lowerCAmelCase             =       (
               reshaped_hidden_states[0].view(__UpperCamelCase       ,    __UpperCamelCase       ,    height * width						).permute(0       ,    2       ,    1						)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]						)       ,    [num_patches, self.model_tester.embed_dim]       ,    )


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           __lowerCAmelCase             =       (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size       ,    collections.abc.Iterable						)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes[:-1]:
                __lowerCAmelCase             =       True
                self.check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                __lowerCAmelCase             =       True

                self.check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()
           __lowerCAmelCase             =       3

           __lowerCAmelCase             =       (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size       ,    collections.abc.Iterable						)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           __lowerCAmelCase             =       (
               config.patch_size
               if isinstance(config.patch_size       ,    collections.abc.Iterable						)
               else (config.patch_size, config.patch_size)
           )

           __lowerCAmelCase             =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __lowerCAmelCase             =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes[:-1]:
                __lowerCAmelCase             =       True
                self.check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    (padded_height, padded_width)						)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                __lowerCAmelCase             =       True
                self.check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    (padded_height, padded_width)						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						Tuple:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowerCAmelCase             =       FocalNetModel.from_pretrained(__UpperCamelCase						)
                self.assertIsNotNone(__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           __lowerCAmelCase             =       _config_zero_init(__UpperCamelCase						)
           for model_class in self.all_model_classes:
                __lowerCAmelCase             =       model_class(config=__UpperCamelCase						)
                for name, param in model.named_parameters():
                     if "embeddings" not in name and param.requires_grad:
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()       ,    [0.0, 1.0]       ,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,    )



@require_vision
@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      @cached_property
      def 			__UpperCAmelCase		(						self						)->						Tuple:
           # TODO update organization
           return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"						) if is_vision_available() else None


      @slow
      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"						).to(__UpperCamelCase						)
           __lowerCAmelCase             =       self.default_image_processor

           __lowerCAmelCase             =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
           __lowerCAmelCase             =       image_processor(images=__UpperCamelCase       ,    return_tensors="pt"						).to(__UpperCamelCase						)

           # forward pass
           with torch.no_grad():
                __lowerCAmelCase             =       model(**__UpperCamelCase						)

           # verify the logits
           __lowerCAmelCase             =       torch.Size((1, 1_0_0_0)						)
           self.assertEqual(outputs.logits.shape       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]						).to(__UpperCamelCase						)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,    __UpperCamelCase       ,    atol=1e-4						)						)
           self.assertTrue(outputs.logits.argmax(dim=-1						).item()       ,    2_8_1						)



@require_torch
class 				_UpperCamelCase      (a_				,		unittest.TestCase      ):
      snake_case_	=				(FocalNetBackbone,) if is_torch_available() else ()
      snake_case_	=				FocalNetConfig

      snake_case_	=				False


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __lowerCAmelCase             =       FocalNetModelTester(self						)














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


lowerCamelCase      :       int    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       r"\w+[.]\d+"
     __lowerCAmelCase             =       re.findall(__snake_case       ,			__snake_case							)
     for pat in pats:
          __lowerCAmelCase             =       key.replace(__snake_case       ,			"_".join(pat.split("."							)							)							)
     return key





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       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)
     ):
          __lowerCAmelCase             =       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:
          __lowerCAmelCase             =       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:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

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

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

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

     # old PyTorch layer norm bias
     __lowerCAmelCase             =       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   (    __snake_case       ,			__snake_case       ,			__snake_case=42							):
     # Step 1: Convert pytorch tensor to numpy
     __lowerCAmelCase             =       {k: v.numpy() for k, v in pt_state_dict.items()}

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

     __lowerCAmelCase             =       flatten_dict(__snake_case							)
     __lowerCAmelCase             =       {}

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

          # Correctly rename weight parameters
          __lowerCAmelCase      ,			__lowerCAmelCase             =       rename_key_and_reshape_tensor(__snake_case       ,			__snake_case       ,			__snake_case							)

          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
          __lowerCAmelCase             =       jnp.asarray(__snake_case							)

     return unflatten_dict(__snake_case							)






