



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

lowercase_						=       logging.get_logger(__name__)
@add_end_docstrings(lowercase__)
class 			_snake_case     (   lowercase__):




				def __init__(  self   :  Optional[Any],   *__lowercase   :  Optional[int],   **__lowercase   :  Dict  ):
							super().__init__(*__lowercase,   **__lowercase  )
							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 							A__ (  self   :  Dict,   __lowercase   :  List[str]=None  ):
							lowercase__   					=			{}
							if top_k is not None:
										lowercase__   					=			top_k
							return {}, {}, postprocess_params




				def __call__(  self   :  Union[str, Any],   __lowercase   :  Union[str, List[str], "Image.Image", List["Image.Image"]],   **__lowercase   :  List[str]  ):
							return super().__call__(__lowercase,   **__lowercase  )




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  Tuple  ):
							lowercase__   					=			load_image(__lowercase  )
							lowercase__   					=			self.image_processor(images=__lowercase,   return_tensors=self.framework  )
							return model_inputs




				def 							A__ (  self   :  int,   __lowercase   :  Tuple  ):
							lowercase__   					=			self.model(**__lowercase  )
							return model_outputs




				def 							A__ (  self   :  List[Any],   __lowercase   :  Tuple,   __lowercase   :  Optional[int]=5  ):
							if top_k > self.model.config.num_labels:
										lowercase__   					=			self.model.config.num_labels

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

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









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

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

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




