

import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_            = False

class 			a					(		unittest.TestCase			):
						pass







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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
										pipe.to(A_		)
										pipe.set_progress_bar_config(disable=A_		)

										_UpperCAmelCase    :						List[Any]							=      load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
										_UpperCAmelCase    :						str							=      torch.manual_seed(0		)
										_UpperCAmelCase    :						Any							=      pipe(
										    image=A_  ,   generator=A_  ,   guidance_scale=7.5  ,   num_inference_steps=50  ,   output_type="numpy"  ,   ).images

										_UpperCAmelCase    :						Optional[int]							=      image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										_UpperCAmelCase    :						int							=      np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45]		)

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





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


