





import argparse

import torch

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


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

UpperCamelCase__											= [
    ["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     _UpperCamelCase     (a__   :str       ):






							"""simple docstring"""





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

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

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





def     _UpperCamelCase     (a__   :Any       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[
							    """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__				     =			sd.pop(a__       )
														UpperCamelCase__				     =			k.replace("""layernorm_embedding"""   ,			"""layer_norm"""       )
														assert new_k not in sd
														UpperCamelCase__				     =			v


UpperCamelCase__											= ["START"]





@torch.no_grad()
def     _UpperCamelCase     (a__   :List[Any]   ,			a__   :Dict   ,			a__   :str       ):






							"""simple docstring"""





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

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


if __name__ == "__main__":
					UpperCamelCase__											= 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"
					)
					UpperCamelCase__											= parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__											= {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= ["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= [
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					UpperCamelCase__											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




