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


_lowercase							:      List[str]      							=logging.get_logger(__name__)



def A__  (			lowercase:     Optional[Any],		lowercase:     int					)				->		str:
       try:
              with open(lowercase,		'rb'					) as flax_state_f:
                     A						:     int         =from_bytes(lowercase,		flax_state_f.read()					)
       except UnpicklingError as e:
              try:
                     with open(lowercase					) 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(lowercase,		lowercase					)



def A__  (			lowercase:     Optional[int],		lowercase:     Optional[Any]					)				->		Optional[Any]:

       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
       A						:     Optional[Any]         =flatten_dict(jax.tree_util.tree_map(lambda lowercase					: x.dtype == jnp.bfloataa,		lowercase					)					).values()
       if any(lowercase					):
              # 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.'					)
              A						:     Any         =jax.tree_util.tree_map(
                  lambda lowercase					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params,		lowercase					)

       A						:     int         =''

       A						:     Optional[int]         =flatten_dict(lowercase,		sep='.'					)
       A						:     Tuple         =pt_model.state_dict()

       # keep track of unexpected & missing keys
       A						:     Tuple         =[]
       A						:     List[str]         =set(pt_model_dict.keys()					)

       for flax_key_tuple, flax_tensor in flax_state_dict.items():
              A						:     Union[str, Any]         =flax_key_tuple.split('.'					)

              if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                     A						:     Any         =flax_key_tuple_array[:-1] + ['weight']
                     A						:     List[str]         =jnp.transpose(lowercase,		(3, 2, 0, 1)					)
              elif flax_key_tuple_array[-1] == "kernel":
                     A						:     List[Any]         =flax_key_tuple_array[:-1] + ['weight']
                     A						:     Optional[int]         =flax_tensor.T
              elif flax_key_tuple_array[-1] == "scale":
                     A						:     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(lowercase					):
                            A						:     Any         =(
                                flax_key_tuple_string.replace('_0',		'.0'					)
                                .replace('_1',		'.1'					)
                                .replace('_2',		'.2'					)
                                .replace('_3',		'.3'					)
                                .replace('_4',		'.4'					)
                                .replace('_5',		'.5'					)
                                .replace('_6',		'.6'					)
                                .replace('_7',		'.7'					)
                                .replace('_8',		'.8'					)
                                .replace('_9',		'.9'					)
                            )

              A						:     Optional[Any]         ='.'.join(lowercase					)

              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						:     List[str]         =np.asarray(lowercase					) if not isinstance(lowercase,		np.ndarray					) else flax_tensor
                            A						:     str         =torch.from_numpy(lowercase					)
                            # remove from missing keys
                            missing_keys.remove(lowercase					)
              else:
                     # weight is not expected by PyTorch model
                     unexpected_keys.append(lowercase					)

       pt_model.load_state_dict(lowercase					)

       # re-transform missing_keys to list
       A						:     Union[str, Any]         =list(lowercase					)

       if len(lowercase					) > 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(lowercase					) > 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







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				DDIMPipeline
  lowercase		:     int            =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowercase		:     Optional[Any]            =				PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  lowercase		:     Optional[Any]            =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  lowercase		:     Union[str, Any]            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         torch.manual_seed(0     )
         A						:     str         =UNetaDModel(
             block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,	up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,	)
         A						:     Optional[int]         =DDIMScheduler()
         A						:     Optional[Any]         ={'unet': unet, 'scheduler': scheduler}
         return components







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0     )    ->				Any:
         if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                A						:     List[Any]         =torch.manual_seed(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Union[str, Any]         =torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         ={
             'batch_size': 1,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         A						:     Union[str, Any]         ='cpu'

         A						:     Tuple         =self.get_dummy_components()
         A						:     Union[str, Any]         =self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
         pipe.to(SCREAMING_SNAKE_CASE__     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     str         =self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
         A						:     str         =pipe(**SCREAMING_SNAKE_CASE__     ).images
         A						:     Optional[Any]         =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape      ,	(1, 32, 32, 3)     )
         A						:     Optional[Any]         =np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]     )
         A						:     str         =np.abs(image_slice.flatten() - expected_slice     ).max()
         self.assertLessEqual(SCREAMING_SNAKE_CASE__      ,	1e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         super().test_save_load_local(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         super().test_save_load_optional_components(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Any         ='google/ddpm-cifar10-32'

         A						:     Optional[int]         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =DDIMScheduler()

         A						:     int         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddim.to(SCREAMING_SNAKE_CASE__     )
         ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =torch.manual_seed(0     )
         A						:     Optional[Any]         =ddim(generator=SCREAMING_SNAKE_CASE__      ,	eta=0.0      ,	output_type='numpy'     ).images

         A						:     str         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A						:     Tuple         =np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     Optional[int]         ='google/ddpm-ema-bedroom-256'

         A						:     str         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     str         =DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__     )

         A						:     Tuple         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddpm.to(SCREAMING_SNAKE_CASE__     )
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Any         =torch.manual_seed(0     )
         A						:     Optional[int]         =ddpm(generator=SCREAMING_SNAKE_CASE__      ,	output_type='numpy'     ).images

         A						:     List[Any]         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         A						:     Optional[int]         =np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2






