



"""simple docstring"""







import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger(__name__)

def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       str     )	-> List[str]:






	"""simple docstring"""





	A__											=					os.path.abspath(UpperCAmelCase_     )
	logger.info(F"""Converting TensorFlow checkpoint from {tf_path}"""     )
	# Load weights from TF model
	A__											=					tf.train.list_variables(UpperCAmelCase_     )
	A__											=					[]
	A__											=					[]
	A__											=					[]
	for full_name, shape in init_vars:
		# logger.info(f"Loading TF weight {name} with shape {shape}")
		A__											=					full_name.split("/"     )
		if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
			logger.info(F"""Skipping non-model layer {full_name}"""     )
			continue
		if "optimizer" in full_name:
			logger.info(F"""Skipping optimization layer {full_name}"""     )
			continue
		if name[0] == "model":
			# ignore initial 'model'
			A__											=					name[1:]
		# figure out how many levels deep the name is
		A__											=					0
		for _name in name:
			if _name.startswith("layer_with_weights"     ):
				depth += 1
			else:
				break
		layer_depth.append(UpperCAmelCase_     )
		# read data
		A__											=					tf.train.load_variable(UpperCAmelCase_,	UpperCAmelCase_     )
		names.append("/".join(UpperCAmelCase_     )     )
		arrays.append(UpperCAmelCase_     )
	logger.info(F"""Read a total of {len(UpperCAmelCase_     ):,} layers"""     )

	# Sanity check
	if len(set(UpperCAmelCase_     )     ) != 1:
		raise ValueError(F"""Found layer names with different depths (layer depth {list(set(UpperCAmelCase_     )     )})"""     )
	A__											=					list(set(UpperCAmelCase_     )     )[0]
	if layer_depth != 1:
		raise ValueError(
		    "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
		    " heads."     )

	# convert layers
	logger.info("Converting weights..."     )
	for full_name, array in zip(UpperCAmelCase_,	UpperCAmelCase_     ):
		A__											=					full_name.split("/"     )
		A__											=					model
		A__											=					[]
		for i, m_name in enumerate(UpperCAmelCase_     ):
			if m_name == ".ATTRIBUTES":
				# variable names end with .ATTRIBUTES/VARIABLE_VALUE
				break
			if m_name.startswith("layer_with_weights"     ):
				A__											=					int(m_name.split("-"     )[-1]     )
				if layer_num <= 2:
					# embedding layers
					# layer_num 0: word_embeddings
					# layer_num 1: position_embeddings
					# layer_num 2: token_type_embeddings
					continue
				elif layer_num == 3:
					# embedding LayerNorm
					trace.extend(["embeddings", "LayerNorm"]     )
					A__											=					getattr(UpperCAmelCase_,	"embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"LayerNorm"     )
				elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
					# encoder layers
					trace.extend(["encoder", "layer", str(layer_num - 4     )]     )
					A__											=					getattr(UpperCAmelCase_,	"encoder"     )
					A__											=					getattr(UpperCAmelCase_,	"layer"     )
					A__											=					pointer[layer_num - 4]
				elif layer_num == config.num_hidden_layers + 4:
					# pooler layer
					trace.extend(["pooler", "dense"]     )
					A__											=					getattr(UpperCAmelCase_,	"pooler"     )
					A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "embeddings":
				trace.append("embeddings"     )
				A__											=					getattr(UpperCAmelCase_,	"embeddings"     )
				if layer_num == 0:
					trace.append("word_embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"word_embeddings"     )
				elif layer_num == 1:
					trace.append("position_embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"position_embeddings"     )
				elif layer_num == 2:
					trace.append("token_type_embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"token_type_embeddings"     )
				else:
					raise ValueError(F"""Unknown embedding layer with name {full_name}"""     )
				trace.append("weight"     )
				A__											=					getattr(UpperCAmelCase_,	"weight"     )
			elif m_name == "_attention_layer":
				# self-attention layer
				trace.extend(["attention", "self"]     )
				A__											=					getattr(UpperCAmelCase_,	"attention"     )
				A__											=					getattr(UpperCAmelCase_,	"self"     )
			elif m_name == "_attention_layer_norm":
				# output attention norm
				trace.extend(["attention", "output", "LayerNorm"]     )
				A__											=					getattr(UpperCAmelCase_,	"attention"     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"LayerNorm"     )
			elif m_name == "_attention_output_dense":
				# output attention dense
				trace.extend(["attention", "output", "dense"]     )
				A__											=					getattr(UpperCAmelCase_,	"attention"     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "_output_dense":
				# output dense
				trace.extend(["output", "dense"]     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "_output_layer_norm":
				# output dense
				trace.extend(["output", "LayerNorm"]     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"LayerNorm"     )
			elif m_name == "_key_dense":
				# attention key
				trace.append("key"     )
				A__											=					getattr(UpperCAmelCase_,	"key"     )
			elif m_name == "_query_dense":
				# attention query
				trace.append("query"     )
				A__											=					getattr(UpperCAmelCase_,	"query"     )
			elif m_name == "_value_dense":
				# attention value
				trace.append("value"     )
				A__											=					getattr(UpperCAmelCase_,	"value"     )
			elif m_name == "_intermediate_dense":
				# attention intermediate dense
				trace.extend(["intermediate", "dense"]     )
				A__											=					getattr(UpperCAmelCase_,	"intermediate"     )
				A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "_output_layer_norm":
				# output layer norm
				trace.append("output"     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
			# weights & biases
			elif m_name in ["bias", "beta"]:
				trace.append("bias"     )
				A__											=					getattr(UpperCAmelCase_,	"bias"     )
			elif m_name in ["kernel", "gamma"]:
				trace.append("weight"     )
				A__											=					getattr(UpperCAmelCase_,	"weight"     )
			else:
				logger.warning(F"""Ignored {m_name}"""     )
        # for certain layers reshape is necessary
		A__											=					".".join(UpperCAmelCase_     )
		if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)",	UpperCAmelCase_     ) or re.match(
		    r"(\S+)\.attention\.output\.dense\.weight",	UpperCAmelCase_     ):
			A__											=					array.reshape(pointer.data.shape     )
		if "kernel" in full_name:
			A__											=					array.transpose()
		if pointer.shape == array.shape:
			A__											=					torch.from_numpy(UpperCAmelCase_     )
		else:
			raise ValueError(
			    F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
			    F""" {array.shape}"""     )
		logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}"""     )
	return model

def       _lowerCamelCase				(							UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Any     )	-> List[str]:






	"""simple docstring"""





	logger.info(F"""Loading model based on config from {config_path}..."""     )
	A__											=					BertConfig.from_json_file(UpperCAmelCase_     )
	A__											=					BertModel(UpperCAmelCase_     )

	# Load weights from checkpoint
	logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}..."""     )
	load_tfa_weights_in_bert(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )

	# Save pytorch-model
	logger.info(F"""Saving PyTorch model to {pytorch_dump_path}..."""     )
	torch.save(model.state_dict(),	UpperCAmelCase_     )


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			parser.add_argument(
			    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x 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 (must include filename).""",
			)
			UpperCamelCase     =							parser.parse_args()
			convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








'''simple docstring'''
def 	_A  (       A							)  ->				list:
	lowercase						:		str   					=		int(A							)
	if n_element < 1:
		lowercase						:		List[Any]   					=		ValueError("a should be a positive number"							)
		raise my_error

	lowercase						:		List[Any]   					=		[1]
	lowercase					,		lowercase					,		lowercase						:		Tuple   					=		(0, 0, 0)
	lowercase						:		Dict   					=		1
	while index < n_element:
		while hamming_list[i] * 2 <= hamming_list[-1]:
			i += 1
		while hamming_list[j] * 3 <= hamming_list[-1]:
			j += 1
		while hamming_list[k] * 5 <= hamming_list[-1]:
			k += 1
		hamming_list.append(
		    min(hamming_list[i] * 2							,hamming_list[j] * 3							,hamming_list[k] * 5							)							)
		index += 1
	return hamming_list


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
					print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
					lowerCAmelCase   : Tuple             =				hamming(int(n))
					print("""-----------------------------------------------------""")
					print(F'''The list with nth numbers is: {hamming_numbers}''')
					print("""-----------------------------------------------------""")


