





"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard






@slow
@require_flax
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         super().tearDown()
         gc.collect()






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							FlaxStableDiffusionPipeline.from_pretrained(
             'stabilityai/stable-diffusion-2'    , revision='bf16'    , dtype=jnp.bfloataa    , )

         lowerCAmelCase__			:Dict											=							'A painting of a squirrel eating a burger'
         lowerCAmelCase__			:List[Any]											=							jax.device_count()
         lowerCAmelCase__			:Dict											=							num_samples * [prompt]
         lowerCAmelCase__			:Tuple											=							sd_pipe.prepare_inputs(__UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							replicate(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							shard(__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[int]											=							jax.random.PRNGKey(0      )
         lowerCAmelCase__			:Optional[Any]											=							jax.random.split(__UpperCAmelCase    , jax.device_count()      )

         lowerCAmelCase__			:List[Any]											=							sd_pipe(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , num_inference_steps=2_5    , jit=__UpperCAmelCase      )[0]
         assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

         lowerCAmelCase__			:str											=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
         lowerCAmelCase__			:Any											=							images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         lowerCAmelCase__			:Tuple											=							jnp.asarray(jax.device_get(image_slice.flatten()      )      )
         lowerCAmelCase__			:Any											=							jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]      )
         print(F"output_slice: {output_slice}"      )
         assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							'stabilityai/stable-diffusion-2'
         lowerCAmelCase__							,				lowerCAmelCase__			:int											=							FlaxDPMSolverMultistepScheduler.from_pretrained(__UpperCAmelCase    , subfolder='scheduler'      )
         lowerCAmelCase__							,				lowerCAmelCase__			:Optional[int]											=							FlaxStableDiffusionPipeline.from_pretrained(
             __UpperCAmelCase    , scheduler=__UpperCAmelCase    , revision='bf16'    , dtype=jnp.bfloataa    , )
         lowerCAmelCase__			:Union[str, Any]											=							scheduler_params

         lowerCAmelCase__			:Dict											=							'A painting of a squirrel eating a burger'
         lowerCAmelCase__			:Optional[Any]											=							jax.device_count()
         lowerCAmelCase__			:Dict											=							num_samples * [prompt]
         lowerCAmelCase__			:Dict											=							sd_pipe.prepare_inputs(__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							replicate(__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							shard(__UpperCAmelCase      )

         lowerCAmelCase__			:Dict											=							jax.random.PRNGKey(0      )
         lowerCAmelCase__			:Optional[int]											=							jax.random.split(__UpperCAmelCase    , jax.device_count()      )

         lowerCAmelCase__			:str											=							sd_pipe(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , num_inference_steps=2_5    , jit=__UpperCAmelCase      )[0]
         assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

         lowerCAmelCase__			:Union[str, Any]											=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
         lowerCAmelCase__			:Dict											=							images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         lowerCAmelCase__			:List[Any]											=							jnp.asarray(jax.device_get(image_slice.flatten()      )      )
         lowerCAmelCase__			:Optional[int]											=							jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]      )
         print(F"output_slice: {output_slice}"      )
         assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2







"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
