






'''simple docstring'''





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 (			A			:	List[Any]	,   A			:	Dict	,   A			:	Tuple						)  -> List[Any]:
			# Initialise PyTorch model
			UpperCAmelCase_     :   Any       =   json.load(open(A	,   '''r'''						)						)
			UpperCAmelCase_     :   int       =   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}"						)
			UpperCAmelCase_     :   Union[str, Any]       =   GPTNeoForCausalLM(A						)

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

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


if __name__ == "__main__":
						_UpperCamelCase		:		List[str]								=			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.'
						)
						_UpperCamelCase		:		str								=			parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference


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




					def 						A     (       self       :						Optional[Any]     )  ->    List[str]:
								UpperCAmelCase_     :   Optional[Any]       =   '''runwayml/stable-diffusion-v1-5'''
								UpperCAmelCase_     :   Optional[int]       =   TextToVideoZeroPipeline.from_pretrained(_A							,				torch_dtype=torch.floataa     ).to('''cuda'''     )
								UpperCAmelCase_     :   Tuple       =   DDIMScheduler.from_config(pipe.scheduler.config     )
								UpperCAmelCase_     :   Tuple       =   torch.Generator(device='''cuda'''     ).manual_seed(0     )

								UpperCAmelCase_     :   List[Any]       =   '''A bear is playing a guitar on Times Square'''
								UpperCAmelCase_     :   List[Any]       =   pipe(prompt=_A							,				generator=_A     ).images

								UpperCAmelCase_     :   Any       =   load_pt(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'''     )

								assert_mean_pixel_difference(_A							,				_A     )


