




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
					UpperCamelCase__					    =    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""")
					UpperCamelCase__					    =    parser.parse_args()

					if args.model_type == "roberta":
										UpperCamelCase__					    =    RobertaForMaskedLM.from_pretrained(args.model_name)
										UpperCamelCase__					    =    """roberta"""
					elif args.model_type == "gpt2":
										UpperCamelCase__					    =    GPTaLMHeadModel.from_pretrained(args.model_name)
										UpperCamelCase__					    =    """transformer"""

					UpperCamelCase__					    =    model.state_dict()
					UpperCamelCase__					    =    {}

					# Embeddings #
					if args.model_type == "gpt2":
										for param_name in ["wte.weight", "wpe.weight"]:
															UpperCamelCase__					    =    state_dict[f'''{prefix}.{param_name}''']
					else:
										for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
															UpperCamelCase__					    =    f'''{prefix}.embeddings.{w}.weight'''
															UpperCamelCase__					    =    state_dict[param_name]
										for w in ["weight", "bias"]:
															UpperCamelCase__					    =    f'''{prefix}.embeddings.LayerNorm.{w}'''
															UpperCamelCase__					    =    state_dict[param_name]

    # Transformer Blocks #
					UpperCamelCase__					    =    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"]:
																									UpperCamelCase__					    =    state_dict[
																									    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																									]
															UpperCamelCase__					    =    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"]:
																									UpperCamelCase__					    =    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"]:
															UpperCamelCase__					    =    state_dict[f'''{layer}''']
										if args.vocab_transform:
															for w in ["weight", "bias"]:
																				UpperCamelCase__					    =    state_dict[f'''lm_head.dense.{w}''']
																				UpperCamelCase__					    =    state_dict[f'''lm_head.layer_norm.{w}''']
					elif args.model_type == "gpt2":
										for w in ["weight", "bias"]:
															UpperCamelCase__					    =    state_dict[f'''{prefix}.ln_f.{w}''']
										UpperCamelCase__					    =    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)










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

UpperCamelCase__					    =    logging.get_logger(__name__)
@add_end_docstrings(snake_case__   )
class     a__					(      snake_case__   ):






						def __init__(     self	,    *_A	,    **_A    ):




											"""simple docstring"""



											super().__init__(*_A	,    **_A    )
											requires_backends(self	,    "vision"    )
											self.check_model_type(
											    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
											    if self.framework == "tf"
											    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A=None    ):




											"""simple docstring"""



											__lowerCAmelCase            = {}
											if top_k is not None:
																__lowerCAmelCase            = top_k
											return {}, {}, postprocess_params






						def __call__(     self	,    _A	,    **_A    ):




											"""simple docstring"""



											return super().__call__(_A	,    **_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = load_image(_A    )
											__lowerCAmelCase            = self.image_processor(images=_A	,    return_tensors=self.framework    )
											return model_inputs






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model(**_A    )
											return model_outputs






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A=5    ):




											"""simple docstring"""



											if top_k > self.model.config.num_labels:
																__lowerCAmelCase            = self.model.config.num_labels

											if self.framework == "pt":
																__lowerCAmelCase            = model_outputs.logits.softmax(-1    )[0]
																__lowerCAmelCase     ,							__lowerCAmelCase            = probs.topk(_A    )
											elif self.framework == "tf":
																__lowerCAmelCase            = stable_softmax(model_outputs.logits	,    axis=-1    )[0]
																__lowerCAmelCase            = tf.math.top_k(_A	,    k=_A    )
																__lowerCAmelCase     ,							__lowerCAmelCase            = topk.values.numpy(), topk.indices.numpy()
											else:
																raise ValueError(f"""Unsupported framework: {self.framework}"""    )

											__lowerCAmelCase            = scores.tolist()
											__lowerCAmelCase            = ids.tolist()
											return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_A	,    _A    )]




