



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

from packaging import version


if TYPE_CHECKING:
		from ... import PreTrainedTokenizer, TensorType

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


snake_case    :						Optional[int]  =						logging.get_logger(__name__)

snake_case    :						Tuple  =						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    int           =						'bloom'
					__A     :    Union[str, Any]           =						['past_key_values']
					__A     :    Any           =						{
					    'num_hidden_layers': 'n_layer',
					    'num_attention_heads': 'n_head',
					}







					def __init__(       self      :     Union[str, Any]			,		_A      :     Dict=25_0880			,		_A      :     Any=64			,		_A      :     Tuple=2			,		_A      :     int=8			,		_A      :     List[str]=1e-5			,		_A      :     str=0.02			,		_A      :     Any=True			,		_A      :     Union[str, Any]=1			,		_A      :     Tuple=2			,		_A      :     Dict=False			,		_A      :     int=0.0			,		_A      :     List[Any]=0.0			,		_A      :     str=1			,		_A      :     Union[str, Any]=False			,		**_A      :     Any			,		):
											A__	:							str						    = vocab_size
											# Backward compatibility with n_embed kwarg
											A__	:							Tuple						    = kwargs.pop("n_embed"			,		_A)
											A__	:							Union[str, Any]						    = hidden_size if n_embed is None else n_embed
											A__	:							Optional[Any]						    = n_layer
											A__	:							Any						    = n_head
											A__	:							List[str]						    = layer_norm_epsilon
											A__	:							Any						    = initializer_range
											A__	:							List[Any]						    = use_cache
											A__	:							Tuple						    = pretraining_tp
											A__	:							List[Any]						    = apply_residual_connection_post_layernorm
											A__	:							Optional[int]						    = hidden_dropout
											A__	:							Tuple						    = attention_dropout

											A__	:							int						    = bos_token_id
											A__	:							List[str]						    = eos_token_id
											A__	:							str						    = slow_but_exact

											super().__init__(bos_token_id=_A			,		eos_token_id=_A			,		**_A)




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    str           =						version.parse('1.12'						)







					def __init__(       self      :     str			,		_A      :     PretrainedConfig			,		_A      :     str = "default"			,		_A      :     List[PatchingSpec] = None			,		_A      :     bool = False			,		):
											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?
																	A__	:							Tuple						    = 0







					@property
					def  _lowercase			(       self      :     int):
											A__	:							Optional[Any]						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
											if self.use_past:
																	# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
																	self.fill_with_past_key_values_(_A			,		direction="inputs"			,		inverted_values_shape=_A)
																	A__	:							int						    = {0: "batch", 1: "past_sequence + sequence"}
											else:
																	A__	:							List[Any]						    = {0: "batch", 1: "sequence"}

											return common_inputs







					@property
					def  _lowercase			(       self      :     str):
											return self._config.n_layer







					@property
					def  _lowercase			(       self      :     List[Any]):
											return self._config.n_head







					@property
					def  _lowercase			(       self      :     int):
											return 1e-3







					def  _lowercase			(       self      :     Tuple			,		_A      :     "PreTrainedTokenizer"			,		_A      :     int = -1			,		_A      :     int = -1			,		_A      :     bool = False			,		_A      :     Optional["TensorType"] = None			,		):
											A__	:							str						    = 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()
											A__	:							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

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

											A__	:							str						    = common_inputs["attention_mask"]
											if self.use_past:
																	A__	:							Optional[Any]						    = ordered_inputs["attention_mask"].dtype
																	A__	:							List[str]						    = torch.cat(
																	    [ordered_inputs["attention_mask"], torch.ones(_A			,		_A			,		dtype=_A)]			,		dim=1)

											return ordered_inputs







					@property
					def  _lowercase			(       self      :     Dict):
											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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

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

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

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

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

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

												return torch.from_numpy(__lowercase       )

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

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# 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__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

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

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_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__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						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.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)