




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				StableDiffusionDiffEditPipeline
    lowerCAmelCase        =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
    lowerCAmelCase        =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
    lowerCAmelCase        =				frozenset(
        []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowerCAmelCase        =				frozenset([]       )





    def 							a__				(				self  )			->		Union[str, Any]:
         torch.manual_seed(0  )
         UpperCAmelCase_      :							Optional[int]       =      UNetaDConditionModel(
             block_out_channels=(32, 64)       ,layers_per_block=2       ,sample_size=32       ,in_channels=4       ,out_channels=4       ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,cross_attention_dim=32       ,attention_head_dim=(2, 4)       ,use_linear_projection=_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							Dict       =      DDIMScheduler(
             beta_start=0.0_00_85       ,beta_end=0.0_12       ,beta_schedule='''scaled_linear'''       ,clip_sample=_SCREAMING_SNAKE_CASE       ,set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							List[Any]       =      DDIMInverseScheduler(
             beta_start=0.0_00_85       ,beta_end=0.0_12       ,beta_schedule='''scaled_linear'''       ,clip_sample=_SCREAMING_SNAKE_CASE       ,set_alpha_to_zero=_SCREAMING_SNAKE_CASE       ,)
         torch.manual_seed(0  )
         UpperCAmelCase_      :							Optional[int]       =      AutoencoderKL(
             block_out_channels=[32, 64]       ,in_channels=3       ,out_channels=3       ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,latent_channels=4       ,sample_size=128       ,)
         torch.manual_seed(0  )
         UpperCAmelCase_      :							Optional[Any]       =      CLIPTextConfig(
             bos_token_id=0       ,eos_token_id=2       ,hidden_size=32       ,intermediate_size=37       ,layer_norm_eps=1e-05       ,num_attention_heads=4       ,num_hidden_layers=5       ,pad_token_id=1       ,vocab_size=1_000       ,hidden_act='''gelu'''       ,projection_dim=512       ,)
         UpperCAmelCase_      :							Union[str, Any]       =      CLIPTextModel(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

         UpperCAmelCase_      :							Optional[int]       =      {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''inverse_scheduler''': inverse_scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }

         return components





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=0  )			->		str:
         UpperCAmelCase_      :							Optional[int]       =      floats_tensor((1, 16, 16)       ,rng=random.Random(_SCREAMING_SNAKE_CASE  )  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      floats_tensor((1, 2, 4, 16, 16)       ,rng=random.Random(_SCREAMING_SNAKE_CASE  )  ).to(_SCREAMING_SNAKE_CASE  )
         if str(_SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
              UpperCAmelCase_      :							Optional[Any]       =      torch.manual_seed(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							Union[str, Any]       =      torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      {
             '''prompt''': '''a dog and a newt''',
             '''mask_image''': mask,
             '''image_latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''inpaint_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }

         return inputs





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=0  )			->		Any:
         UpperCAmelCase_      :							Union[str, Any]       =      floats_tensor((1, 3, 32, 32)       ,rng=random.Random(_SCREAMING_SNAKE_CASE  )  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      image.cpu().permute(0       ,2       ,3       ,1  )[0]
         UpperCAmelCase_      :							Any       =      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE  )  ).convert('''RGB'''  )
         if str(_SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
              UpperCAmelCase_      :							Tuple       =      torch.manual_seed(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							int       =      torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      {
             '''image''': image,
             '''source_prompt''': '''a cat and a frog''',
             '''target_prompt''': '''a dog and a newt''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''num_maps_per_mask''': 2,
             '''mask_encode_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }

         return inputs





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=0  )			->		Any:
         UpperCAmelCase_      :							str       =      floats_tensor((1, 3, 32, 32)       ,rng=random.Random(_SCREAMING_SNAKE_CASE  )  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      image.cpu().permute(0       ,2       ,3       ,1  )[0]
         UpperCAmelCase_      :							Any       =      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE  )  ).convert('''RGB'''  )
         if str(_SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
              UpperCAmelCase_      :							str       =      torch.manual_seed(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							Optional[int]       =      torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      {
             '''image''': image,
             '''prompt''': '''a cat and a frog''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''inpaint_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''decode_latents''': True,
             '''output_type''': '''numpy''',
         }
         return inputs





    def 							a__				(				self  )			->		Any:
         if not hasattr(self.pipeline_class       ,'''_optional_components'''  ):
              return

         UpperCAmelCase_      :							Tuple       =      self.get_dummy_components()
         UpperCAmelCase_      :							Any       =      self.pipeline_class(**_SCREAMING_SNAKE_CASE  )
         pipe.to(_SCREAMING_SNAKE_CASE  )
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         # set all optional components to None and update pipeline config accordingly
         for optional_component in pipe._optional_components:
              setattr(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}  )

         UpperCAmelCase_      :							str       =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      pipe(**_SCREAMING_SNAKE_CASE  )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							List[str]       =      self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
              pipe_loaded.to(_SCREAMING_SNAKE_CASE  )
              pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) is None       ,f'''`{optional_component}` did not stay set to None after loading.'''       ,)

         UpperCAmelCase_      :							List[str]       =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[Any]       =      pipe_loaded(**_SCREAMING_SNAKE_CASE  )[0]

         UpperCAmelCase_      :							List[Any]       =      np.abs(output - output_loaded  ).max()
         self.assertLess(_SCREAMING_SNAKE_CASE       ,1e-4  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Tuple       =      '''cpu'''

         UpperCAmelCase_      :							int       =      self.get_dummy_components()
         UpperCAmelCase_      :							Dict       =      self.pipeline_class(**_SCREAMING_SNAKE_CASE  )
         pipe.to(_SCREAMING_SNAKE_CASE  )
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      self.get_dummy_mask_inputs(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      pipe.generate_mask(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      mask[0, -3:, -3:]

         self.assertEqual(mask.shape       ,(1, 16, 16)  )
         UpperCAmelCase_      :							Tuple       =      np.array([0] * 9  )
         UpperCAmelCase_      :							str       =      np.abs(mask_slice.flatten() - expected_slice  ).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE       ,1e-3  )
         self.assertEqual(mask[0, -3, -4]       ,0  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							str       =      '''cpu'''

         UpperCAmelCase_      :							List[Any]       =      self.get_dummy_components()
         UpperCAmelCase_      :							Optional[Any]       =      self.pipeline_class(**_SCREAMING_SNAKE_CASE  )
         pipe.to(_SCREAMING_SNAKE_CASE  )
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      self.get_dummy_inversion_inputs(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      pipe.invert(**_SCREAMING_SNAKE_CASE  ).images
         UpperCAmelCase_      :							Tuple       =      image[0, -1, -3:, -3:]

         self.assertEqual(image.shape       ,(2, 32, 32, 3)  )
         UpperCAmelCase_      :							Any       =      np.array(
             [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]       ,)
         UpperCAmelCase_      :							Optional[Any]       =      np.abs(image_slice.flatten() - expected_slice  ).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE       ,1e-3  )





    def 							a__				(				self  )			->		str:
         super().test_inference_batch_single_identical(expected_max_diff=5e-3  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      '''cpu'''

         UpperCAmelCase_      :							Optional[int]       =      self.get_dummy_components()

         UpperCAmelCase_      :							str       =      {'''beta_start''': 0.0_00_85, '''beta_end''': 0.0_12, '''beta_schedule''': '''scaled_linear'''}
         UpperCAmelCase_      :							str       =      DPMSolverMultistepScheduler(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      DPMSolverMultistepInverseScheduler(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      self.pipeline_class(**_SCREAMING_SNAKE_CASE  )
         pipe.to(_SCREAMING_SNAKE_CASE  )
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.get_dummy_inversion_inputs(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      pipe.invert(**_SCREAMING_SNAKE_CASE  ).images
         UpperCAmelCase_      :							Union[str, Any]       =      image[0, -1, -3:, -3:]

         self.assertEqual(image.shape       ,(2, 32, 32, 3)  )
         UpperCAmelCase_      :							Union[str, Any]       =      np.array(
             [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]       ,)
         UpperCAmelCase_      :							Dict       =      np.abs(image_slice.flatten() - expected_slice  ).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE       ,1e-3  )


@require_torch_gpu
@slow
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Union[str, Any]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





    @classmethod
    def 							a__				(				cls  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''  )

         UpperCAmelCase_      :							List[Any]       =      raw_image.convert('''RGB'''  ).resize((768, 768)  )

         UpperCAmelCase_      :							Any       =      raw_image





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							int       =      torch.manual_seed(0  )

         UpperCAmelCase_      :							Optional[int]       =      StableDiffusionDiffEditPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-1'''       ,safety_checker=_SCREAMING_SNAKE_CASE       ,torch_dtype=torch.floataa  )
         UpperCAmelCase_      :							List[str]       =      DDIMScheduler.from_config(pipe.scheduler.config  )
         UpperCAmelCase_      :							Optional[Any]       =      DDIMInverseScheduler.from_config(pipe.scheduler.config  )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							List[Any]       =      '''a bowl of fruit'''
         UpperCAmelCase_      :							List[str]       =      '''a bowl of pears'''

         UpperCAmelCase_      :							Union[str, Any]       =      pipe.generate_mask(
             image=self.raw_image       ,source_prompt=_SCREAMING_SNAKE_CASE       ,target_prompt=_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE       ,)

         UpperCAmelCase_      :							str       =      pipe.invert(
             prompt=_SCREAMING_SNAKE_CASE       ,image=self.raw_image       ,inpaint_strength=0.7       ,generator=_SCREAMING_SNAKE_CASE  ).latents

         UpperCAmelCase_      :							str       =      pipe(
             prompt=_SCREAMING_SNAKE_CASE       ,mask_image=_SCREAMING_SNAKE_CASE       ,image_latents=_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE       ,negative_prompt=_SCREAMING_SNAKE_CASE       ,inpaint_strength=0.7       ,output_type='''numpy'''       ,).images[0]

         UpperCAmelCase_      :							List[str]       =      (
             np.array(
                 load_image(
                     '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                     '''/diffedit/pears.png'''  ).resize((768, 768)  )  )
             / 255
         )
         assert np.abs((expected_image - image).max()  ) < 5e-1





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							Optional[Any]       =      torch.manual_seed(0  )

         UpperCAmelCase_      :							Dict       =      StableDiffusionDiffEditPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-1'''       ,safety_checker=_SCREAMING_SNAKE_CASE       ,torch_dtype=torch.floataa  )
         UpperCAmelCase_      :							Optional[int]       =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
         UpperCAmelCase_      :							int       =      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      '''a bowl of fruit'''
         UpperCAmelCase_      :							Dict       =      '''a bowl of pears'''

         UpperCAmelCase_      :							Optional[Any]       =      pipe.generate_mask(
             image=self.raw_image       ,source_prompt=_SCREAMING_SNAKE_CASE       ,target_prompt=_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE       ,)

         UpperCAmelCase_      :							str       =      pipe.invert(
             prompt=_SCREAMING_SNAKE_CASE       ,image=self.raw_image       ,inpaint_strength=0.7       ,generator=_SCREAMING_SNAKE_CASE       ,num_inference_steps=25       ,).latents

         UpperCAmelCase_      :							List[str]       =      pipe(
             prompt=_SCREAMING_SNAKE_CASE       ,mask_image=_SCREAMING_SNAKE_CASE       ,image_latents=_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE       ,negative_prompt=_SCREAMING_SNAKE_CASE       ,inpaint_strength=0.7       ,num_inference_steps=25       ,output_type='''numpy'''       ,).images[0]

         UpperCAmelCase_      :							Optional[int]       =      (
             np.array(
                 load_image(
                     '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                     '''/diffedit/pears.png'''  ).resize((768, 768)  )  )
             / 255
         )
         assert np.abs((expected_image - image).max()  ) < 5e-1





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


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      r'''\w+[.]\d+'''
     UpperCAmelCase_      :							int       =      re.findall(_lowercase			,						_lowercase				)
     for pat in pats:
          UpperCAmelCase_      :							List[Any]       =      key.replace(_lowercase			,						'''_'''.join(pat.split('''.'''				)				)				)
     return key







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''scale''',)
     if (
         any('''norm''' in str_ for str_ in pt_tuple_key				)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCAmelCase_      :							List[Any]       =      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:
          UpperCAmelCase_      :							Any       =      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:
          UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''embedding''',)
          return renamed_pt_tuple_key, pt_tensor

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

     # linear layer
     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight":
          UpperCAmelCase_      :							Tuple       =      pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

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

     # old PyTorch layer norm bias
     UpperCAmelCase_      :							Tuple       =      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__       (      _lowercase			,						_lowercase			,						_lowercase=42				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      {k: v.numpy() for k, v in pt_state_dict.items()}

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

     UpperCAmelCase_      :							List[Any]       =      flatten_dict(_lowercase				)
     UpperCAmelCase_      :							int       =      {}

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

          # Correctly rename weight parameters
          UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      rename_key_and_reshape_tensor(_lowercase			,						_lowercase			,						_lowercase				)

          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
          UpperCAmelCase_      :							int       =      jnp.asarray(_lowercase				)

     return unflatten_dict(_lowercase				)