
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     UpperCAmelCase						(		A_			,unittest.TestCase					):
   A__   :						List[str]			=	DDIMPipeline
   A__   :						List[str]			=	UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   A__   :						Optional[Any]			=	PipelineTesterMixin.required_optional_params - {
       "num_images_per_prompt",
       "latents",
       "callback",
       "callback_steps",
   }
   A__   :						List[Any]			=	UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   A__   :						Dict			=	False



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Dict:


          '''simple docstring'''


          torch.manual_seed(0					)
          snake_case			:  Optional[Any]     							= 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")       ,		)
          snake_case			:  List[str]     							= DDIMScheduler()
          snake_case			:  Any     							= {"unet": unet, "scheduler": scheduler}
          return components



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Optional[int]=0					)						->      Optional[Any]:


          '''simple docstring'''


          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  Any     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  List[str]     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  Optional[int]     							= {
              "batch_size": 1,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }
          return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Any     							= "cpu"

          snake_case			:  List[Any]     							= self.get_dummy_components()
          snake_case			:  Any     							= self.pipeline_class(**snake_case__					)
          pipe.to(snake_case__					)
          pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Dict     							= self.get_dummy_inputs(snake_case__					)
          snake_case			:  List[Any]     							= pipe(**snake_case__					).images
          snake_case			:  Union[str, Any]     							= image[0, -3:, -3:, -1]

          self.assertEqual(image.shape       ,		(1, 32, 32, 3)					)
          snake_case			:  Optional[int]     							= np.array(
              [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]					)
          snake_case			:  Any     							= np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(snake_case__       ,		1e-3					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Tuple:


          '''simple docstring'''


          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      str:


          '''simple docstring'''


          super().test_save_load_local(expected_max_difference=3e-3					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      List[str]:


          '''simple docstring'''


          super().test_save_load_optional_components(expected_max_difference=3e-3					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          super().test_inference_batch_single_identical(expected_max_diff=3e-3					)






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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      int:


          '''simple docstring'''


          snake_case			:  str     							= "google/ddpm-cifar10-32"

          snake_case			:  Tuple     							= UNetaDModel.from_pretrained(snake_case__					)
          snake_case			:  List[str]     							= DDIMScheduler()

          snake_case			:  List[str]     							= DDIMPipeline(unet=snake_case__       ,		scheduler=snake_case__					)
          ddim.to(snake_case__					)
          ddim.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Tuple     							= torch.manual_seed(0					)
          snake_case			:  Tuple     							= ddim(generator=snake_case__       ,		eta=0.0       ,		output_type="numpy"					).images

          snake_case			:  Any     							= image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          snake_case			:  Dict     							= np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]					)

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= "google/ddpm-ema-bedroom-256"

          snake_case			:  Optional[int]     							= UNetaDModel.from_pretrained(snake_case__					)
          snake_case			:  int     							= DDIMScheduler.from_pretrained(snake_case__					)

          snake_case			:  List[Any]     							= DDIMPipeline(unet=snake_case__       ,		scheduler=snake_case__					)
          ddpm.to(snake_case__					)
          ddpm.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  int     							= torch.manual_seed(0					)
          snake_case			:  Tuple     							= ddpm(generator=snake_case__       ,		output_type="numpy"					).images

          snake_case			:  Optional[Any]     							= image[0, -3:, -3:, -1]

          assert image.shape == (1, 2_56, 2_56, 3)
          snake_case			:  Dict     							= np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]					)

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







import argparse

import torch

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


logging.set_verbosity_info()


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

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

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


if __name__ == "__main__":
   __lowerCamelCase				=			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."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





