



import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				def get_masked_lm_array(__UpperCamelCase ):
								a__	:							Tuple      						= F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								a__	:							List[str]      						= tf.train.load_variable(__UpperCamelCase	,				__UpperCamelCase )

								if "kernel" in name:
												a__	:							int      						= array.transpose()

								return torch.from_numpy(__UpperCamelCase )

				def get_encoder_array(__UpperCamelCase ):
								a__	:							Dict      						= F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								a__	:							Any      						= tf.train.load_variable(__UpperCamelCase	,				__UpperCamelCase )

								if "kernel" in name:
												a__	:							Dict      						= array.transpose()

								return torch.from_numpy(__UpperCamelCase )

				def get_encoder_layer_array(__UpperCamelCase	,				__UpperCamelCase ):
								a__	:							Union[str, Any]      						= F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								a__	:							Optional[int]      						= tf.train.load_variable(__UpperCamelCase	,				__UpperCamelCase )

								if "kernel" in name:
												a__	:							Optional[Any]      						= array.transpose()

								return torch.from_numpy(__UpperCamelCase )

				def get_encoder_attention_layer_array(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase ):
								a__	:							List[str]      						= F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								a__	:							str      						= tf.train.load_variable(__UpperCamelCase	,				__UpperCamelCase )
								a__	:							Dict      						= array.reshape(__UpperCamelCase )

								if "kernel" in name:
												a__	:							Dict      						= array.transpose()

								return torch.from_numpy(__UpperCamelCase )

				print(F'Loading model based on config from {config_path}...' )
				a__	:							Union[str, Any]      						= BertConfig.from_json_file(__UpperCamelCase )
				a__	:							Optional[Any]      						= BertForMaskedLM(__UpperCamelCase )

				# Layers
				for layer_index in range(0	,				config.num_hidden_layers ):
								a__	:							BertLayer      						= model.bert.encoder.layer[layer_index]

								# Self-attention
								a__	:							BertSelfAttention      						= layer.attention.self

								a__	:							str      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_query_dense/kernel"	,				self_attn.query.weight.data.shape )
								a__	:							int      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_query_dense/bias"	,				self_attn.query.bias.data.shape )
								a__	:							str      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_key_dense/kernel"	,				self_attn.key.weight.data.shape )
								a__	:							Any      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_key_dense/bias"	,				self_attn.key.bias.data.shape )
								a__	:							Optional[int]      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_value_dense/kernel"	,				self_attn.value.weight.data.shape )
								a__	:							Tuple      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_value_dense/bias"	,				self_attn.value.bias.data.shape )

								# Self-attention Output
								a__	:							BertSelfOutput      						= layer.attention.output

								a__	:							Optional[Any]      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_output_dense/kernel"	,				self_output.dense.weight.data.shape )
								a__	:							int      						= get_encoder_attention_layer_array(
								    __UpperCamelCase	,				"_output_dense/bias"	,				self_output.dense.bias.data.shape )

								a__	:							Dict      						= get_encoder_layer_array(__UpperCamelCase	,				"_attention_layer_norm/gamma" )
								a__	:							List[str]      						= get_encoder_layer_array(__UpperCamelCase	,				"_attention_layer_norm/beta" )

								# Intermediate
								a__	:							BertIntermediate      						= layer.intermediate

								a__	:							Union[str, Any]      						= get_encoder_layer_array(__UpperCamelCase	,				"_intermediate_dense/kernel" )
								a__	:							Tuple      						= get_encoder_layer_array(__UpperCamelCase	,				"_intermediate_dense/bias" )

								# Output
								a__	:							BertOutput      						= layer.output

								a__	:							str      						= get_encoder_layer_array(__UpperCamelCase	,				"_output_dense/kernel" )
								a__	:							List[Any]      						= get_encoder_layer_array(__UpperCamelCase	,				"_output_dense/bias" )

								a__	:							Optional[int]      						= get_encoder_layer_array(__UpperCamelCase	,				"_output_layer_norm/gamma" )
								a__	:							Dict      						= get_encoder_layer_array(__UpperCamelCase	,				"_output_layer_norm/beta" )

				# Embeddings
				a__	:							str      						= get_encoder_array("_position_embedding_layer/embeddings" )
				a__	:							List[str]      						= get_encoder_array("_type_embedding_layer/embeddings" )
				a__	:							Optional[Any]      						= get_encoder_array("_embedding_norm_layer/gamma" )
				a__	:							Dict      						= get_encoder_array("_embedding_norm_layer/beta" )

				# LM Head
				a__	:							List[Any]      						= model.cls.predictions.transform

				a__	:							str      						= get_masked_lm_array("dense/kernel" )
				a__	:							Any      						= get_masked_lm_array("dense/bias" )

				a__	:							List[str]      						= get_masked_lm_array("layer_norm/gamma" )
				a__	:							Optional[Any]      						= get_masked_lm_array("layer_norm/beta" )

				a__	:							str      						= get_masked_lm_array("embedding_table" )

				# Pooling
				a__	:							Dict      						= BertPooler(config=__UpperCamelCase )
				a__	:							BertPooler      						= get_encoder_array("_pooler_layer/kernel" )
				a__	:							BertPooler      						= get_encoder_array("_pooler_layer/bias" )

				# Export final model
				model.save_pretrained(__UpperCamelCase )

				# Integration test - should load without any errors ;)
				a__	:							List[str]      						= BertForMaskedLM.from_pretrained(__UpperCamelCase )
				print(new_model.eval() )

				print("Model conversion was done sucessfully!" )


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						parser.add_argument(
						    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
						)
						parser.add_argument(
						    """--bert_config_file""",
						    type=str,
						    required=True,
						    help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""",
						    type=str,
						    required=True,
						    help="""Path to the output PyTorch model.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







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


lowerCamelCase		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {
    """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 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:str =       "codegen"
							A				:Tuple =       {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}



							def __init__(							self ,					__UpperCAmelCase=5_0400 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=4096 ,					__UpperCAmelCase=28 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase=None ,					__UpperCAmelCase="gelu_new" ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=1E-5 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=False ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Dict      						= vocab_size
											a__	:							int      						= n_ctx
											a__	:							str      						= n_positions
											a__	:							Union[str, Any]      						= n_embd
											a__	:							Union[str, Any]      						= n_layer
											a__	:							Optional[int]      						= n_head
											a__	:							Dict      						= n_inner
											a__	:							Optional[Any]      						= rotary_dim
											a__	:							str      						= activation_function
											a__	:							List[Any]      						= resid_pdrop
											a__	:							str      						= embd_pdrop
											a__	:							List[str]      						= attn_pdrop
											a__	:							str      						= layer_norm_epsilon
											a__	:							Any      						= initializer_range
											a__	:							List[str]      						= use_cache

											a__	:							Tuple      						= bos_token_id
											a__	:							List[Any]      						= eos_token_id

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





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = "default" ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase ,					task=__UpperCAmelCase ,					patching_specs=__UpperCAmelCase ,					use_past=__UpperCAmelCase				)
											if not getattr(self._config ,					"pad_token_id" ,					__UpperCAmelCase				):
															# TODO: how to do that better?
															a__	:							Union[str, Any]      						= 0



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
											if self.use_past:
															self.fill_with_past_key_values_(__UpperCAmelCase ,					direction="inputs"				)
															a__	:							Any      						= {0: "batch", 1: "past_sequence + sequence"}
											else:
															a__	:							List[Any]      						= {0: "batch", 1: "sequence"}

											return common_inputs



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_layer



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_head



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							Dict      						= super(__UpperCAmelCase ,					self				).generate_dummy_inputs(
											    __UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					seq_length=__UpperCAmelCase ,					is_pair=__UpperCAmelCase ,					framework=__UpperCAmelCase				)

											# We need to order the input in the way they appears in the forward()
											a__	:							Union[str, 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

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

											a__	:							Union[str, Any]      						= common_inputs["attention_mask"]
											if self.use_past:
															a__	:							str      						= ordered_inputs["attention_mask"].dtype
															a__	:							Union[str, Any]      						= torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase ,					__UpperCAmelCase ,					dtype=__UpperCAmelCase				)] ,					dim=1				)

											return ordered_inputs




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return 13


