


"""simple docstring"""







import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class  SCREAMING_SNAKE_CASE__							(						__snake_case  ,       __snake_case  ,       unittest.TestCase							):
       """simple docstring"""


       a    :							Optional[int]  =VQModel
       a    :							Any  ="sample"

       @property
       def 			lowercase__				(		self						,	snake_case__=(32, 32)		):






         """simple docstring"""





         lowerCAmelCase	:	Any								=				4
         lowerCAmelCase	:	int								=				3

         lowerCAmelCase	:	Tuple								=				floats_tensor((batch_size, num_channels) + sizes		).to(a__		)

         return {"sample": image}

       @property
       def 			lowercase__				(		self		):






         """simple docstring"""





         return (3, 32, 32)

       @property
       def 			lowercase__				(		self		):






         """simple docstring"""





         return (3, 32, 32)

       def 			lowercase__				(		self		):






         """simple docstring"""





         lowerCAmelCase	:	List[str]								=				{
             "block_out_channels": [32, 64],
             "in_channels": 3,
             "out_channels": 3,
             "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
             "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
             "latent_channels": 3,
         }
         lowerCAmelCase	:	Tuple								=				self.dummy_input
         return init_dict, inputs_dict

       def 			lowercase__				(		self		):






         """simple docstring"""





         pass

       def 			lowercase__				(		self		):






         """simple docstring"""





         pass

       def 			lowercase__				(		self		):






         """simple docstring"""





         lowerCAmelCase		,    lowerCAmelCase	:	Any								=				VQModel.from_pretrained("fusing/vqgan-dummy"						,	output_loading_info=a__		)
         self.assertIsNotNone(a__		)
         self.assertEqual(len(loading_info["missing_keys"]		)						,	0		)

         model.to(a__		)
         lowerCAmelCase	:	List[Any]								=				model(**self.dummy_input		)

         assert image is not None, "Make sure output is not None"







       def 			lowercase__				(		self		):






         """simple docstring"""





         lowerCAmelCase	:	Union[str, Any]								=				VQModel.from_pretrained("fusing/vqgan-dummy"		)
         model.to(a__		).eval()

         torch.manual_seed(0		)
         if torch.cuda.is_available():
           torch.cuda.manual_seed_all(0		)

         lowerCAmelCase	:	List[str]								=				torch.randn(1						,	model.config.in_channels						,	model.config.sample_size						,	model.config.sample_size		)
         lowerCAmelCase	:	Optional[int]								=				image.to(a__		)
         with torch.no_grad():
           lowerCAmelCase	:	Union[str, Any]								=				model(a__		).sample

         lowerCAmelCase	:	Tuple								=				output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         lowerCAmelCase	:	Union[str, Any]								=				torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]		)
         # fmt: on
         self.assertTrue(torch.allclose(a__						,	a__						,	atol=1e-3		)		)









import argparse

import torch

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


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["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 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	if k.startswith("""encoder"""    ):
		A             =					k.replace(""".attn"""				,       """.self_attn"""    )
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """final_layer_norm"""    )
	elif k.startswith("""decoder"""    ):
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """encoder_attn_layer_norm"""    )
		A             =					k.replace("""norm3"""				,       """final_layer_norm"""    )
	return k





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

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


if __name__ == "__main__":
				_lowercase							:   str						=						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"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



