from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE						:   Any         =			{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'codegen'
							_lowerCamelCase						=		{
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}



							def __init__(						self				,    lowercase_=50_400				,    lowercase_=2_048				,    lowercase_=2_048				,    lowercase_=4_096				,    lowercase_=28				,    lowercase_=16				,    lowercase_=64				,    lowercase_=None				,    lowercase_="gelu_new"				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=1e-5				,    lowercase_=0.02				,    lowercase_=True				,    lowercase_=50_256				,    lowercase_=50_256				,    lowercase_=False				,    **lowercase_				,    ):
													_snake_case						:					Optional[int]    =   vocab_size
													_snake_case						:					List[str]    =   n_ctx
													_snake_case						:					Any    =   n_positions
													_snake_case						:					List[str]    =   n_embd
													_snake_case						:					List[str]    =   n_layer
													_snake_case						:					Dict    =   n_head
													_snake_case						:					Dict    =   n_inner
													_snake_case						:					Union[str, Any]    =   rotary_dim
													_snake_case						:					Tuple    =   activation_function
													_snake_case						:					Union[str, Any]    =   resid_pdrop
													_snake_case						:					List[Any]    =   embd_pdrop
													_snake_case						:					Dict    =   attn_pdrop
													_snake_case						:					Tuple    =   layer_norm_epsilon
													_snake_case						:					Optional[Any]    =   initializer_range
													_snake_case						:					Union[str, Any]    =   use_cache

													_snake_case						:					Dict    =   bos_token_id
													_snake_case						:					Union[str, Any]    =   eos_token_id

													super().__init__(
													    bos_token_id=lowercase_				,    eos_token_id=lowercase_				,    tie_word_embeddings=lowercase_				,    **lowercase_    )




class        lowercase_   (					__snake_case		):



							def __init__(						self				,    lowercase_				,    lowercase_ = "default"				,    lowercase_ = None				,    lowercase_ = False				,    ):
													super().__init__(lowercase_				,    task=lowercase_				,    patching_specs=lowercase_				,    use_past=lowercase_    )
													if not getattr(self._config				,    "pad_token_id"				,    lowercase_    ):
																			# TODO: how to do that better?
																			_snake_case						:					Optional[int]    =   0



							@property
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
													if self.use_past:
																			self.fill_with_past_key_values_(lowercase_				,    direction="inputs"    )
																			_snake_case						:					str    =   {0: "batch", 1: "past_sequence + sequence"}
													else:
																			_snake_case						:					List[str]    =   {0: "batch", 1: "sequence"}

													return common_inputs



							@property
							def 	UpperCamelCase	(						self    ):
													return self._config.n_layer



							@property
							def 	UpperCamelCase	(						self    ):
													return self._config.n_head



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    ):
													_snake_case						:					List[Any]    =   super(lowercase_				,    self    ).generate_dummy_inputs(
													    lowercase_				,    batch_size=lowercase_				,    seq_length=lowercase_				,    is_pair=lowercase_				,    framework=lowercase_    )

													# We need to order the input in the way they appears in the forward()
													_snake_case						:					List[Any]    =   OrderedDict({"input_ids": common_inputs["input_ids"]}    )

													# Need to add the past_keys
													if self.use_past:
																			if not is_torch_available():
																									raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
																			else:
																									import torch

																									_snake_case    ,_snake_case						:					str    =   common_inputs["input_ids"].shape
																									# Not using the same length for past_key_values
																									_snake_case						:					Tuple    =   seqlen + 2
																									_snake_case						:					str    =   (
																									    batch,
																									    self.num_attention_heads,
																									    past_key_values_length,
																									    self._config.hidden_size // self.num_attention_heads,
																									)
																									_snake_case						:					Tuple    =   [
																									    (torch.zeros(lowercase_    ), torch.zeros(lowercase_    )) for _ in range(self.num_layers    )
																									]

													_snake_case						:					str    =   common_inputs["attention_mask"]
													if self.use_past:
																			_snake_case						:					Optional[int]    =   ordered_inputs["attention_mask"].dtype
																			_snake_case						:					int    =   torch.cat(
																			    [ordered_inputs["attention_mask"], torch.ones(lowercase_				,    lowercase_				,    dtype=lowercase_    )]				,    dim=1    )

													return ordered_inputs



							@property
							def 	UpperCamelCase	(						self    ):
													return 13
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

						print(F"""Load fine-pruned model from {model_name_or_path}"""						)
						_snake_case						:					List[Any]    =   torch.load(os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						_snake_case						:					List[str]    =   {}

						for name, tensor in model.items():
												if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																		_snake_case						:					Tuple    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "classifier" in name or "qa_output" in name:
																		_snake_case						:					Optional[int]    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "bias" in name:
																		_snake_case						:					List[Any]    =   tensor
																		print(F"""Copied layer {name}"""						)
												else:
																		if pruning_method == "magnitude":
																								_snake_case						:					Tuple    =   MagnitudeBinarizer.apply(inputs=__lowercase ,      threshold=__lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "topK":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					Optional[Any]    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					Tuple    =   TopKBinarizer.apply(__lowercase ,      __lowercase						)
																								_snake_case						:					Optional[Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "sigmoied_threshold":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					List[Any]    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					List[str]    =   ThresholdBinarizer.apply(__lowercase ,      __lowercase ,      __lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "l0":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case    ,_snake_case						:					Union[str, Any]    =   -0.1, 1.1
																								_snake_case						:					Dict    =   torch.sigmoid(__lowercase						)
																								_snake_case						:					List[str]    =   s * (r - l) + l
																								_snake_case						:					Tuple    =   s_bar.clamp(min=0.0 ,      max=1.0						)
																								_snake_case						:					Union[str, Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		else:
																								raise ValueError("Unknown pruning method"						)

						if target_model_path is None:
												_snake_case						:					Any    =   os.path.join(
												    os.path.dirname(__lowercase						) ,      F"""bertarized_{os.path.basename(__lowercase						)}"""						)

						if not os.path.isdir(__lowercase						):
												shutil.copytree(__lowercase ,      __lowercase						)
												print(F"""\nCreated folder {target_model_path}"""						)

						torch.save(__lowercase ,      os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			argparse.ArgumentParser()

				parser.add_argument(
				    '--pruning_method',
				    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
				    type=str,
				    required=True,
				    help=(
				        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
				        ' sigmoied_threshold = Soft movement pruning)'
				    ),
				)
				parser.add_argument(
				    '--threshold',
				    type=float,
				    required=False,
				    help=(
				        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
				        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
				        'Not needed for `l0`'
				    ),
				)
				parser.add_argument(
				    '--model_name_or_path',
				    type=str,
				    required=True,
				    help='Folder containing the model that was previously fine-pruned',
				)
				parser.add_argument(
				    '--target_model_path',
				    default=None,
				    type=str,
				    required=False,
				    help='Folder containing the model that was previously fine-pruned',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)