





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
    __lowerCamelCase       :     Optional[Any]   =     argparse.ArgumentParser(
        description=(
            """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
            """ Distillation"""
        )
    )
    parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
    parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
    parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
    parser.add_argument("""--vocab_transform""", action="""store_true""")
    __lowerCamelCase       :     Any   =     parser.parse_args()

    if args.model_type == "roberta":
        __lowerCamelCase       :     Dict   =     RobertaForMaskedLM.from_pretrained(args.model_name)
        __lowerCamelCase       :     List[str]   =     """roberta"""
    elif args.model_type == "gpt2":
        __lowerCamelCase       :     Tuple   =     GPTaLMHeadModel.from_pretrained(args.model_name)
        __lowerCamelCase       :     Any   =     """transformer"""

    __lowerCamelCase       :     Union[str, Any]   =     model.state_dict()
    __lowerCamelCase       :     Optional[int]   =     {}

    # Embeddings #
    if args.model_type == "gpt2":
        for param_name in ["wte.weight", "wpe.weight"]:
            __lowerCamelCase       :     List[Any]   =     state_dict[f"""{prefix}.{param_name}"""]
    else:
        for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
            __lowerCamelCase       :     Tuple   =     f"""{prefix}.embeddings.{w}.weight"""
            __lowerCamelCase       :     Optional[Any]   =     state_dict[param_name]
        for w in ["weight", "bias"]:
            __lowerCamelCase       :     Union[str, Any]   =     f"""{prefix}.embeddings.LayerNorm.{w}"""
            __lowerCamelCase       :     Tuple   =     state_dict[param_name]

    # Transformer Blocks #
    __lowerCamelCase       :     List[str]   =     0
    for teacher_idx in [0, 2, 4, 7, 9, 11]:
        if args.model_type == "gpt2":
            for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                for w in ["weight", "bias"]:
                    __lowerCamelCase       :     Union[str, Any]   =     state_dict[
                        f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                    ]
            __lowerCamelCase       :     Optional[Any]   =     state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
        else:
            for layer in [
                "attention.self.query",
                "attention.self.key",
                "attention.self.value",
                "attention.output.dense",
                "attention.output.LayerNorm",
                "intermediate.dense",
                "output.dense",
                "output.LayerNorm",
            ]:
                for w in ["weight", "bias"]:
                    __lowerCamelCase       :     str   =     state_dict[
                        f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
                    ]
        std_idx += 1

    # Language Modeling Head ###s
    if args.model_type == "roberta":
        for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
            __lowerCamelCase       :     Optional[Any]   =     state_dict[f"""{layer}"""]
        if args.vocab_transform:
            for w in ["weight", "bias"]:
                __lowerCamelCase       :     Any   =     state_dict[f"""lm_head.dense.{w}"""]
                __lowerCamelCase       :     Union[str, Any]   =     state_dict[f"""lm_head.layer_norm.{w}"""]
    elif args.model_type == "gpt2":
        for w in ["weight", "bias"]:
            __lowerCamelCase       :     Optional[Any]   =     state_dict[f"""{prefix}.ln_f.{w}"""]
        __lowerCamelCase       :     Dict   =     state_dict["""lm_head.weight"""]

    print(f"""N layers selected for distillation: {std_idx}""")
    print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

    print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
    torch.save(compressed_sd, args.dump_checkpoint)





'''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


__lowerCAmelCase									=   {
    '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
    '''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''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

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




