




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_   (UpperCamelCase	:  str			,						UpperCamelCase	:  str			,						UpperCamelCase	:  str				):





							'''simple docstring'''


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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														# Self-attention
														_a     					=    layer.attention.self

														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_query_dense/kernel'''			,						self_attn.query.weight.data.shape				)
														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_query_dense/bias'''			,						self_attn.query.bias.data.shape				)
														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_key_dense/kernel'''			,						self_attn.key.weight.data.shape				)
														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_key_dense/bias'''			,						self_attn.key.bias.data.shape				)
														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_value_dense/kernel'''			,						self_attn.value.weight.data.shape				)
														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_value_dense/bias'''			,						self_attn.value.bias.data.shape				)

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

														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_output_dense/kernel'''			,						self_output.dense.weight.data.shape				)
														_a     					=    get_encoder_attention_layer_array(
														    UpperCamelCase			,						'''_output_dense/bias'''			,						self_output.dense.bias.data.shape				)

														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_attention_layer_norm/gamma'''				)
														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_attention_layer_norm/beta'''				)

														# Intermediate
														_a     					=    layer.intermediate

														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_intermediate_dense/kernel'''				)
														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_intermediate_dense/bias'''				)

														# Output
														_a     					=    layer.output

														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_output_dense/kernel'''				)
														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_output_dense/bias'''				)

														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_output_layer_norm/gamma'''				)
														_a     					=    get_encoder_layer_array(UpperCamelCase			,						'''_output_layer_norm/beta'''				)

							# Embeddings
							_a     					=    get_encoder_array('''_position_embedding_layer/embeddings'''				)
							_a     					=    get_encoder_array('''_type_embedding_layer/embeddings'''				)
							_a     					=    get_encoder_array('''_embedding_norm_layer/gamma'''				)
							_a     					=    get_encoder_array('''_embedding_norm_layer/beta'''				)

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

							_a     					=    get_masked_lm_array('''dense/kernel'''				)
							_a     					=    get_masked_lm_array('''dense/bias'''				)

							_a     					=    get_masked_lm_array('''layer_norm/gamma'''				)
							_a     					=    get_masked_lm_array('''layer_norm/beta'''				)

							_a     					=    get_masked_lm_array('''embedding_table'''				)

							# Pooling
							_a     					=    BertPooler(config=UpperCamelCase				)
							_a     					=    get_encoder_array('''_pooler_layer/kernel'''				)
							_a     					=    get_encoder_array('''_pooler_layer/bias'''				)

							# Export final model
							model.save_pretrained(UpperCamelCase				)

							# Integration test - should load without any errors ;)
							_a     					=    BertForMaskedLM.from_pretrained(UpperCamelCase				)
							print(new_model.eval()				)

							print('''Model conversion was done sucessfully!'''				)


if __name__ == "__main__":
				_snake_case				:				List[Any]       							=		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				:				Optional[int]       							=		parser.parse_args()
				convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''simple docstring'''


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a     					=    {}

							_a     					=    job['''started_at''']
							_a     					=    job['''completed_at''']

							_a     					=    date_parser.parse(UpperCamelCase				)
							_a     					=    date_parser.parse(UpperCamelCase				)

							_a     					=    round((end_datetime - start_datetime).total_seconds() / 60.0				)

							_a     					=    start
							_a     					=    end
							_a     					=    duration_in_min

							return job_info
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  int=None				):





							'''simple docstring'''


							_a     					=    None
							if token is not None:
														_a     					=    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

							_a     					=    f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
							_a     					=    requests.get(UpperCamelCase			,						headers=UpperCamelCase				).json()
							_a     					=    {}

							try:
														job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)
														_a     					=    math.ceil((result['''total_count'''] - 100) / 100				)

														for i in range(UpperCamelCase				):
																					_a     					=    requests.get(url + f'&page={i + 2}'			,						headers=UpperCamelCase				).json()
																					job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)

														return job_time
							except Exception:
														print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'				)

							return {}


if __name__ == "__main__":
				_snake_case				:				int       							=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
				_snake_case				:				Tuple       							=		parser.parse_args()

				_snake_case				:				int       							=		get_job_time(args.workflow_run_id)
				_snake_case				:				int       							=		dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

				for k, v in job_time.items():
								print(F'''{k}: {v['duration']}''')

