import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
      import onnxruntime as ort


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}





class      A							:


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						int=None       ,  **__UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."		)
         UpperCAmelCase__								=  model
         UpperCAmelCase__								=  kwargs.get("model_save_dir"       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  kwargs.get("latest_model_name"       ,  __UpperCAmelCase		)


   def __call__(self     :						Tuple       ,  **__UpperCAmelCase     :						int		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  {k: np.array(__UpperCAmelCase		) for k, v in kwargs.items()}
         return self.model.run(__UpperCAmelCase       ,  __UpperCAmelCase		)


   @staticmethod
   def 			lowercase_      (__UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						Dict=None		)						->				List[str]:
         """simple docstring"""



         if provider is None:
               logger.info("No onnxruntime provider specified, using CPUExecutionProvider"		)
               UpperCAmelCase__								=  "CPUExecutionProvider"

         return ort.InferenceSession(__UpperCAmelCase       ,  providers=[provider]       ,  sess_options=__UpperCAmelCase		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						Optional[str] = None       ,  **__UpperCAmelCase     :						List[str]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  file_name if file_name is not None else ONNX_WEIGHTS_NAME

         UpperCAmelCase__								=  self.model_save_dir.joinpath(self.latest_model_name		)
         UpperCAmelCase__								=  Path(__UpperCAmelCase		).joinpath(__UpperCAmelCase		)
         try:
               shutil.copyfile(__UpperCAmelCase       ,  __UpperCAmelCase		)
         except shutil.SameFileError:
               pass

         # copy external weights (for models >2GB)
         UpperCAmelCase__								=  self.model_save_dir.joinpath(__UpperCAmelCase		)
         if src_path.exists():
               UpperCAmelCase__								=  Path(__UpperCAmelCase		).joinpath(__UpperCAmelCase		)
               try:
                     shutil.copyfile(__UpperCAmelCase       ,  __UpperCAmelCase		)
               except shutil.SameFileError:
                     pass


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						Union[str, os.PathLike]       ,  **__UpperCAmelCase     :						Tuple       ,  )						->				Union[str, Any]:
         """simple docstring"""



         if os.path.isfile(__UpperCAmelCase		):
               logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
               return

         os.makedirs(__UpperCAmelCase       ,  exist_ok=__UpperCAmelCase		)

         # saving model weights/files
         self._save_pretrained(__UpperCAmelCase       ,  **__UpperCAmelCase		)


   @classmethod
   def 			lowercase_      (cls     :						List[Any]       ,  __UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						Optional[Union[bool, str, None]] = None       ,  __UpperCAmelCase     :						Optional[Union[str, None]] = None       ,  __UpperCAmelCase     :						bool = False       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional["ort.SessionOptions"] = None       ,  **__UpperCAmelCase     :						Union[str, Any]       ,  )						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(__UpperCAmelCase		):
               UpperCAmelCase__								=  OnnxRuntimeModel.load_model(
                   os.path.join(__UpperCAmelCase       ,  __UpperCAmelCase		)       ,  provider=__UpperCAmelCase       ,  sess_options=__UpperCAmelCase		)
               UpperCAmelCase__								=  Path(__UpperCAmelCase		)
         # load model from hub
         else:
               # download model
               UpperCAmelCase__								=  hf_hub_download(
                   repo_id=__UpperCAmelCase       ,  filename=__UpperCAmelCase       ,  use_auth_token=__UpperCAmelCase       ,  revision=__UpperCAmelCase       ,  cache_dir=__UpperCAmelCase       ,  force_download=__UpperCAmelCase       ,  )
               UpperCAmelCase__								=  Path(__UpperCAmelCase		).parent
               UpperCAmelCase__								=  Path(__UpperCAmelCase		).name
               UpperCAmelCase__								=  OnnxRuntimeModel.load_model(__UpperCAmelCase       ,  provider=__UpperCAmelCase       ,  sess_options=__UpperCAmelCase		)
         return cls(model=__UpperCAmelCase       ,  **__UpperCAmelCase		)





   @classmethod
   def 			lowercase_      (cls     :						Union[str, Any]       ,  __UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[str] = None       ,  **__UpperCAmelCase     :						int       ,  )						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  None
         if len(str(__UpperCAmelCase		).split("@"		)		) == 2:
               UpperCAmelCase__		,      UpperCAmelCase__								=  model_id.split("@"		)

         return cls._from_pretrained(
             model_id=__UpperCAmelCase       ,  revision=__UpperCAmelCase       ,  cache_dir=__UpperCAmelCase       ,  force_download=__UpperCAmelCase       ,  use_auth_token=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )





"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a														=		parser.parse_args()

				if args.model_type == "bert":
								_a														=		BertForMaskedLM.from_pretrained(args.model_name)
								_a														=		"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a														=		model.state_dict()
				_a														=		{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a														=		state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a														=		state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a														=		0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a														=		state_dict["""cls.predictions.decoder.weight"""]
				_a														=		state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a														=		state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a														=		state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				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)






