





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"t5"
							lowerCamelCase_           =					["past_key_values"]
							lowerCamelCase_           =					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


							def __init__(							self    :List[str]							,				__A    :str=3_2128							,				__A    :Any=512							,				__A    :List[str]=64							,				__A    :Dict=2048							,				__A    :Optional[Any]=6							,				__A    :int=None							,				__A    :List[Any]=8							,				__A    :int=32							,				__A    :Optional[Any]=128							,				__A    :str=0.1							,				__A    :str=1E-6							,				__A    :List[str]=1.0							,				__A    :Optional[int]="relu"							,				__A    :Optional[Any]=True							,				__A    :int=True							,				__A    :Any=0							,				__A    :Union[str, Any]=1							,				**__A    :Optional[int]							,				)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      d_model
									SCREAMING_SNAKE_CASE__			=      d_kv
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      num_layers
									SCREAMING_SNAKE_CASE__			=      (
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      layer_norm_epsilon
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      feed_forward_proj
									SCREAMING_SNAKE_CASE__			=      use_cache

									SCREAMING_SNAKE_CASE__			=      self.feed_forward_proj.split("""-"""		)
									SCREAMING_SNAKE_CASE__			=      act_info[-1]
									SCREAMING_SNAKE_CASE__			=      act_info[0] == """gated"""

									if len(__A		) > 1 and act_info[0] != "gated" or len(__A		) > 2:
											raise ValueError(
											    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
											    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
											    """'gated-gelu' or 'relu'"""		)

									# for backwards compatibility
									if feed_forward_proj == "gated-gelu":
											SCREAMING_SNAKE_CASE__			=      """gelu_new"""

									super().__init__(
									    pad_token_id=__A							,				eos_token_id=__A							,				is_encoder_decoder=__A							,				**__A							,				)




class   UpperCamelCase_       (     UpperCamelCase__    ):


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      {
									    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
									    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
									}
									if self.use_past:
											SCREAMING_SNAKE_CASE__			=      """past_encoder_sequence + sequence"""
											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"""		)

									return common_inputs


							@property
							def      _snake_case  (							self    :int		)   ->   int:



									"""simple docstring"""



									return 13






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)