
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


lowercase						:      Tuple									= logging.get_logger(__name__)


lowercase						:      int									= {
    '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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'codegen'
	_A			=  {
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}







	def __init__(      self	:Union[str, Any]  ,     a	:str=5_0_4_0_0  ,     a	:Dict=2_0_4_8  ,     a	:str=2_0_4_8  ,     a	:List[str]=4_0_9_6  ,     a	:List[Any]=2_8  ,     a	:List[Any]=1_6  ,     a	:int=6_4  ,     a	:Optional[int]=None  ,     a	:List[Any]="gelu_new"  ,     a	:Any=0.0  ,     a	:str=0.0  ,     a	:Optional[int]=0.0  ,     a	:Optional[int]=1E-5  ,     a	:Tuple=0.02  ,     a	:Optional[Any]=True  ,     a	:Optional[int]=5_0_2_5_6  ,     a	:Any=5_0_2_5_6  ,     a	:str=False  ,     **a	:Any  ,     )	->				Optional[Any]:
								__UpperCamelCase			:     Tuple         =      vocab_size
								__UpperCamelCase			:     str         =      n_ctx
								__UpperCamelCase			:     List[Any]         =      n_positions
								__UpperCamelCase			:     str         =      n_embd
								__UpperCamelCase			:     Optional[Any]         =      n_layer
								__UpperCamelCase			:     Tuple         =      n_head
								__UpperCamelCase			:     Dict         =      n_inner
								__UpperCamelCase			:     Union[str, Any]         =      rotary_dim
								__UpperCamelCase			:     List[str]         =      activation_function
								__UpperCamelCase			:     List[str]         =      resid_pdrop
								__UpperCamelCase			:     List[str]         =      embd_pdrop
								__UpperCamelCase			:     Any         =      attn_pdrop
								__UpperCamelCase			:     Optional[int]         =      layer_norm_epsilon
								__UpperCamelCase			:     int         =      initializer_range
								__UpperCamelCase			:     Tuple         =      use_cache

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

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




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Dict  ,     a	:PretrainedConfig  ,     a	:str = "default"  ,     a	:List[PatchingSpec] = None  ,     a	:bool = False  ,     )	->				Optional[Any]:
								super().__init__(a  ,     task=a  ,     patching_specs=a  ,     use_past=a		)
								if not getattr(self._config  ,     "pad_token_id"  ,     a		):
															# TODO: how to do that better?
															__UpperCamelCase			:     Tuple         =      0







	@property
	def      _lowerCamelCase       (      self	:int		)	->				Mapping[str, Mapping[int, str]]:
								__UpperCamelCase			:     int         =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
								if self.use_past:
															self.fill_with_past_key_values_(a  ,     direction="inputs"		)
															__UpperCamelCase			:     Any         =      {0: "batch", 1: "past_sequence + sequence"}
								else:
															__UpperCamelCase			:     Tuple         =      {0: "batch", 1: "sequence"}

								return common_inputs







	@property
	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				int:
								return self._config.n_layer







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				int:
								return self._config.n_head







	def      _lowerCamelCase       (      self	:str  ,     a	:PreTrainedTokenizer  ,     a	:int = -1  ,     a	:int = -1  ,     a	:bool = False  ,     a	:Optional[TensorType] = None  ,     )	->				Mapping[str, Any]:
								__UpperCamelCase			:     int         =      super(a  ,     self		).generate_dummy_inputs(
								    a  ,     batch_size=a  ,     seq_length=a  ,     is_pair=a  ,     framework=a		)

								# We need to order the input in the way they appears in the forward()
								__UpperCamelCase			:     str         =      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

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

								__UpperCamelCase			:     List[Any]         =      common_inputs["attention_mask"]
								if self.use_past:
															__UpperCamelCase			:     int         =      ordered_inputs["attention_mask"].dtype
															__UpperCamelCase			:     Any         =      torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(a  ,     a  ,     dtype=a		)]  ,     dim=1		)

								return ordered_inputs







	@property
	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				int:
								return 1_3

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      Any									= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)