





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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"bart"
							lowerCamelCase_           =					["past_key_values"]
							lowerCamelCase_           =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


							def __init__(							self    :List[str]							,				__A    :Tuple=5_0265							,				__A    :str=1024							,				__A    :int=12							,				__A    :Optional[Any]=4096							,				__A    :List[str]=16							,				__A    :Dict=12							,				__A    :Optional[Any]=4096							,				__A    :Union[str, Any]=16							,				__A    :Dict=0.0							,				__A    :Optional[int]=0.0							,				__A    :List[str]="gelu"							,				__A    :Dict=1024							,				__A    :List[str]=0.1							,				__A    :List[Any]=0.0							,				__A    :Optional[Any]=0.0							,				__A    :Dict=0.0_2							,				__A    :Optional[int]=0.0							,				__A    :int=False							,				__A    :Any=True							,				__A    :Union[str, Any]=3							,				__A    :int=1							,				__A    :List[Any]=0							,				__A    :Tuple=2							,				__A    :List[Any]=True							,				__A    :List[str]=2							,				__A    :Any=2							,				**__A    :Dict							,				)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      max_position_embeddings
									SCREAMING_SNAKE_CASE__			=      d_model
									SCREAMING_SNAKE_CASE__			=      encoder_ffn_dim
									SCREAMING_SNAKE_CASE__			=      encoder_layers
									SCREAMING_SNAKE_CASE__			=      encoder_attention_heads
									SCREAMING_SNAKE_CASE__			=      decoder_ffn_dim
									SCREAMING_SNAKE_CASE__			=      decoder_layers
									SCREAMING_SNAKE_CASE__			=      decoder_attention_heads
									SCREAMING_SNAKE_CASE__			=      dropout
									SCREAMING_SNAKE_CASE__			=      attention_dropout
									SCREAMING_SNAKE_CASE__			=      activation_dropout
									SCREAMING_SNAKE_CASE__			=      activation_function
									SCREAMING_SNAKE_CASE__			=      init_std
									SCREAMING_SNAKE_CASE__			=      encoder_layerdrop
									SCREAMING_SNAKE_CASE__			=      decoder_layerdrop
									SCREAMING_SNAKE_CASE__			=      classifier_dropout
									SCREAMING_SNAKE_CASE__			=      use_cache
									SCREAMING_SNAKE_CASE__			=      encoder_layers
									SCREAMING_SNAKE_CASE__			=      scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    num_labels=__A							,				pad_token_id=__A							,				bos_token_id=__A							,				eos_token_id=__A							,				is_encoder_decoder=__A							,				decoder_start_token_id=__A							,				forced_eos_token_id=__A							,				**__A							,				)

									# ensure backward compatibility for BART CNN models
									if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,				__A		):
											SCREAMING_SNAKE_CASE__			=      self.bos_token_id
											warnings.warn(
											    f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
											    """The config can simply be saved and uploaded again to be fixed."""		)




class   UpperCamelCase_       (     UpperCamelCase__    ):


							@property
							def      _snake_case  (							self    :Tuple		)   ->   Mapping[str, Mapping[int, str]]:



									"""simple docstring"""



									if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__			=      OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]		)

											if self.use_past:
													SCREAMING_SNAKE_CASE__			=      {0: """batch"""}
													SCREAMING_SNAKE_CASE__			=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
													SCREAMING_SNAKE_CASE__			=      {0: """batch""", 1: """decoder_sequence"""}
													SCREAMING_SNAKE_CASE__			=      {0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
													self.fill_with_past_key_values_(__A							,				direction="""inputs"""		)
									elif self.task == "causal-lm":
											# TODO: figure this case out.
											SCREAMING_SNAKE_CASE__			=      OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]		)
											if self.use_past:
													SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.num_layers
													for i in range(__A		):
															SCREAMING_SNAKE_CASE__			=      {0: """batch""", 2: """past_sequence + sequence"""}
															SCREAMING_SNAKE_CASE__			=      {0: """batch""", 2: """past_sequence + sequence"""}
									else:
											SCREAMING_SNAKE_CASE__			=      OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
											        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
											    ]		)

									return common_inputs


							@property
							def      _snake_case  (							self    :Any		)   ->   Mapping[str, Mapping[int, str]]:



									"""simple docstring"""



									if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__			=      super().outputs
									else:
											SCREAMING_SNAKE_CASE__			=      super(__A							,				self		).outputs
											if self.use_past:
													SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.num_layers
													for i in range(__A		):
															SCREAMING_SNAKE_CASE__			=      {0: """batch""", 2: """past_sequence + sequence"""}
															SCREAMING_SNAKE_CASE__			=      {0: """batch""", 2: """past_sequence + sequence"""}
									return common_outputs


							def      _snake_case  (							self    :List[Any]							,				__A    :PreTrainedTokenizer							,				__A    :int = -1							,				__A    :int = -1							,				__A    :bool = False							,				__A    :Optional[TensorType] = None							,				)   ->   Mapping[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __A							,				__A							,				__A							,				__A							,				__A		)

									# Generate decoder inputs
									SCREAMING_SNAKE_CASE__			=      seq_length if not self.use_past else 1
									SCREAMING_SNAKE_CASE__			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __A							,				__A							,				__A							,				__A							,				__A		)
									SCREAMING_SNAKE_CASE__			=      {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									SCREAMING_SNAKE_CASE__			=      dict(**__A							,				**__A		)

									if self.use_past:
											if not is_torch_available():
													raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
											else:
													import torch
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      common_inputs["""input_ids"""].shape
											SCREAMING_SNAKE_CASE__			=      common_inputs["""decoder_input_ids"""].shape[1]
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.num_attention_heads
											SCREAMING_SNAKE_CASE__			=      (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											SCREAMING_SNAKE_CASE__			=      decoder_seq_length + 3
											SCREAMING_SNAKE_CASE__			=      (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											SCREAMING_SNAKE_CASE__			=      torch.cat(
											    [common_inputs["""decoder_attention_mask"""], torch.ones(__A							,				__A		)]							,				dim=1		)

											SCREAMING_SNAKE_CASE__			=      []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.num_layers
											SCREAMING_SNAKE_CASE__			=      min(__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      max(__A							,				__A		) - min_num_layers
											SCREAMING_SNAKE_CASE__			=      """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

											for _ in range(__A		):
													common_inputs["past_key_values"].append(
													    (
													        torch.zeros(__A		),
													        torch.zeros(__A		),
													        torch.zeros(__A		),
													        torch.zeros(__A		),
													    )		)
											# TODO: test this.
											SCREAMING_SNAKE_CASE__			=      encoder_shape if remaining_side_name == """encoder""" else decoder_shape
											for _ in range(__A							,				__A		):
													common_inputs["past_key_values"].append((torch.zeros(__A		), torch.zeros(__A		))		)
									return common_inputs


							def      _snake_case  (							self    :int							,				__A    :PreTrainedTokenizer							,				__A    :int = -1							,				__A    :int = -1							,				__A    :bool = False							,				__A    :Optional[TensorType] = None							,				)   ->   Mapping[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __A							,				__A							,				__A							,				__A							,				__A		)

									if self.use_past:
											if not is_torch_available():
													raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
											else:
													import torch
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      common_inputs["""input_ids"""].shape
											# Not using the same length for past_key_values
											SCREAMING_SNAKE_CASE__			=      seqlen + 2
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.num_layers
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.num_attention_heads
											SCREAMING_SNAKE_CASE__			=      (
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											SCREAMING_SNAKE_CASE__			=      common_inputs["""attention_mask"""].dtype
											SCREAMING_SNAKE_CASE__			=      torch.cat(
											    [common_inputs["""attention_mask"""], torch.ones(__A							,				__A							,				dtype=__A		)]							,				dim=1		)
											SCREAMING_SNAKE_CASE__			=      [
											    (torch.zeros(__A		), torch.zeros(__A		)) for _ in range(__A		)
											]
									return common_inputs


							def      _snake_case  (							self    :Dict							,				__A    :PreTrainedTokenizer							,				__A    :int = -1							,				__A    :int = -1							,				__A    :bool = False							,				__A    :Optional[TensorType] = None							,				)   ->   Mapping[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      compute_effective_axis_dimension(
									    __A							,				fixed_dimension=OnnxConfig.default_fixed_batch							,				num_token_to_add=0		)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE__			=      tokenizer.num_special_tokens_to_add(__A		)
									SCREAMING_SNAKE_CASE__			=      compute_effective_axis_dimension(
									    __A							,				fixed_dimension=OnnxConfig.default_fixed_sequence							,				num_token_to_add=__A		)

									# Generate dummy inputs according to compute batch and sequence
									SCREAMING_SNAKE_CASE__			=      [""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
									SCREAMING_SNAKE_CASE__			=      dict(tokenizer(__A							,				return_tensors=__A		)		)
									return common_inputs


							def      _snake_case  (							self    :Optional[Any]							,				__A    :PreTrainedTokenizer							,				__A    :int = -1							,				__A    :int = -1							,				__A    :bool = False							,				__A    :Optional[TensorType] = None							,				)   ->   Mapping[str, Any]:



									"""simple docstring"""



									if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__			=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    __A							,				batch_size=__A							,				seq_length=__A							,				is_pair=__A							,				framework=__A		)

									elif self.task == "causal-lm":
											SCREAMING_SNAKE_CASE__			=      self._generate_dummy_inputs_for_causal_lm(
											    __A							,				batch_size=__A							,				seq_length=__A							,				is_pair=__A							,				framework=__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __A							,				batch_size=__A							,				seq_length=__A							,				is_pair=__A							,				framework=__A		)

									return common_inputs


							def      _snake_case  (							self    :List[Any]							,				__A    :int							,				__A    :List[Any]							,				__A    :List[str]							,				__A    :Optional[Any]		)   ->   List[Any]:



									"""simple docstring"""



									if self.task in ["default", "seq2seq-lm"]:
											SCREAMING_SNAKE_CASE__			=      super()._flatten_past_key_values_(__A							,				__A							,				__A							,				__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      super(__A							,				self		)._flatten_past_key_values_(
											    __A							,				__A							,				__A							,				__A		)













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__:							str					,   UpperCamelCase__:							str					,   UpperCamelCase__:							str    ):
		def get_masked_lm_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

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

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

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

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

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

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_attention_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Any    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      array.reshape(UpperCamelCase__    )

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

				return torch.from_numpy(UpperCamelCase__    )

		print(f'''Loading model based on config from {config_path}...'''    )
		SCREAMING_SNAKE_CASE__			=      BertConfig.from_json_file(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM(UpperCamelCase__    )

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

				# Self-attention
				SCREAMING_SNAKE_CASE__			=      layer.attention.self

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/kernel"""					,   self_attn.query.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/bias"""					,   self_attn.query.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/kernel"""					,   self_attn.key.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/bias"""					,   self_attn.key.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/kernel"""					,   self_attn.value.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/bias"""					,   self_attn.value.bias.data.shape    )

				# Self-attention Output
				SCREAMING_SNAKE_CASE__			=      layer.attention.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/kernel"""					,   self_output.dense.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/bias"""					,   self_output.dense.bias.data.shape    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/beta"""    )

				# Intermediate
				SCREAMING_SNAKE_CASE__			=      layer.intermediate

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/bias"""    )

				# Output
				SCREAMING_SNAKE_CASE__			=      layer.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/bias"""    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/beta"""    )

		# Embeddings
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_position_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_type_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/beta"""    )

		# LM Head
		SCREAMING_SNAKE_CASE__			=      model.cls.predictions.transform

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/bias"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/beta"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""embedding_table"""    )

		# Pooling
		SCREAMING_SNAKE_CASE__			=      BertPooler(config=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/bias"""    )

		# Export final model
		model.save_pretrained(UpperCamelCase__    )

		# Integration test - should load without any errors ;)
		SCREAMING_SNAKE_CASE__			=      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)