



"""simple docstring"""


import argparse

import torch

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


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

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 			_lowerCAmelCase				(       lowercase_						):
						if k == "embeddings.weight":
												return "shared.weight"

						for parlai_name, hf_name in PATTERNS:
												UpperCAmelCase					=			k.replace(lowercase_		,     lowercase_						)

						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 			_lowerCAmelCase				(       lowercase_						):
						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(lowercase_						)
												UpperCAmelCase					=			k.replace('layernorm_embedding'		,     'layer_norm'						)
												assert new_k not in sd
												UpperCAmelCase					=			v


snake_case_    					=	["""START"""]

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			torch.load(lowercase_		,     map_location='cpu'						)
						UpperCAmelCase					=			model['model']
						UpperCAmelCase					=			BlenderbotConfig.from_json_file(lowercase_						)
						UpperCAmelCase					=			BlenderbotForConditionalGeneration(lowercase_						)
						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(lowercase_						)
												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(lowercase_						)
						m.model.load_state_dict(lowercase_		,     strict=lowercase_						)
						m.half()
						m.save_pretrained(lowercase_						)


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










"""simple docstring"""


# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_    					=	{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """CpmAntForCausalLM""",
				    """CpmAntModel""",
				    """CpmAntPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
				from .tokenization_cpmant import CpmAntTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_cpmant import (
								    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CpmAntForCausalLM,
								    CpmAntModel,
								    CpmAntPreTrainedModel,
								)


else:
				import sys

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





