


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @property
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             torch.manual_seed(0			)
             lowerCamelCase__						:							Dict       =    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")   , )
             return model





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    """cpu"""
             lowerCamelCase__						:							List[str]       =    self.dummy_uncond_unet
             lowerCamelCase__						:							Any       =    DDPMScheduler()

             lowerCamelCase__						:							Optional[Any]       =    DDPMPipeline(unet=lowercase_   , scheduler=lowercase_			)
             ddpm.to(lowercase_			)
             ddpm.set_progress_bar_config(disable=lowercase_			)

             lowerCamelCase__						:							Dict       =    torch.Generator(device=lowercase_			).manual_seed(0			)
             lowerCamelCase__						:							Tuple       =    ddpm(generator=lowercase_   , num_inference_steps=2   , output_type="numpy"			).images

             lowerCamelCase__						:							List[str]       =    torch.Generator(device=lowercase_			).manual_seed(0			)
             lowerCamelCase__						:							Tuple       =    ddpm(generator=lowercase_   , num_inference_steps=2   , output_type="numpy"   , return_dict=lowercase_			)[0]

             lowerCamelCase__						:							List[str]       =    image[0, -3:, -3:, -1]
             lowerCamelCase__						:							Optional[int]       =    image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             lowerCamelCase__						:							List[str]       =    np.array(
                 [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.0_0_0, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

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





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    self.dummy_uncond_unet
             lowerCamelCase__						:							Optional[int]       =    DDPMScheduler(prediction_type="sample"			)

             lowerCamelCase__						:							Dict       =    DDPMPipeline(unet=lowercase_   , scheduler=lowercase_			)
             ddpm.to(lowercase_			)
             ddpm.set_progress_bar_config(disable=lowercase_			)

             lowerCamelCase__						:							Optional[Any]       =    torch.manual_seed(0			)
             lowerCamelCase__						:							Union[str, Any]       =    ddpm(generator=lowercase_   , num_inference_steps=2   , output_type="numpy"			).images

             lowerCamelCase__						:							Any       =    torch.manual_seed(0			)
             lowerCamelCase__						:							Optional[Any]       =    ddpm(generator=lowercase_   , num_inference_steps=2   , output_type="numpy"			)[0]

             lowerCamelCase__						:							Any       =    image[0, -3:, -3:, -1]
             lowerCamelCase__						:							str       =    image_eps[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             lowerCamelCase__						:							int       =    1E-2 if torch_device != """mps""" else 3E-2
             assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance







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


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



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

             lowerCamelCase__						:							Union[str, Any]       =    UNetaDModel.from_pretrained(lowercase_			)
             lowerCamelCase__						:							Dict       =    DDPMScheduler.from_pretrained(lowercase_			)

             lowerCamelCase__						:							Tuple       =    DDPMPipeline(unet=lowercase_   , scheduler=lowercase_			)
             ddpm.to(lowercase_			)
             ddpm.set_progress_bar_config(disable=lowercase_			)

             lowerCamelCase__						:							Tuple       =    torch.manual_seed(0			)
             lowerCamelCase__						:							Tuple       =    ddpm(generator=lowercase_   , output_type="numpy"			).images

             lowerCamelCase__						:							Dict       =    image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             lowerCamelCase__						:							Optional[int]       =    np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]			)
             assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					GPTNeoConfig(
						    hidden_size=config_json["""n_embd"""]			,num_layers=config_json["""n_layer"""]			,num_heads=config_json["""n_head"""]			,attention_types=config_json["""attention_types"""]			,max_position_embeddings=config_json["""n_positions"""]			,resid_dropout=config_json["""res_dropout"""]			,embed_dropout=config_json["""embed_dropout"""]			,attention_dropout=config_json["""attn_dropout"""]			,)
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   str					     =					GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__  )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
		lowerCAmelCase					:				Optional[Any]   	=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(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf 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					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

