


'''simple docstring'''

import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						# Initialise PyTorch model
						__lowerCAmelCase							       =				FunnelConfig.from_json_file(lowerCamelCase)
						print(F"""Building PyTorch model from configuration: {config}""")
						__lowerCAmelCase							       =				FunnelBaseModel(lowerCamelCase) if base_model else FunnelModel(lowerCamelCase)

						# Load weights from tf checkpoint
						load_tf_weights_in_funnel(lowerCamelCase,				lowerCamelCase,				lowerCamelCase)

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""")
						torch.save(model.state_dict(),				lowerCamelCase)


if __name__ == "__main__":
	_UpperCAmelCase				: str							    =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
	)
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
	)





'''simple docstring'''

import json
import sys

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						with open(lowerCamelCase,				encoding='''utf-8''') as f:
												__lowerCAmelCase							       =				json.load(lowerCamelCase)

						__lowerCAmelCase							       =				['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

						for benchmark_name in sorted(lowerCamelCase):
												__lowerCAmelCase							       =				results[benchmark_name]

												__lowerCAmelCase							       =				benchmark_name.split('''/''')[-1]
												output_md.append(F"""### Benchmark: {benchmark_file_name}""")

												__lowerCAmelCase							       =				'''| metric |'''
												__lowerCAmelCase							       =				'''|--------|'''
												__lowerCAmelCase							       =				'''| new / old (diff) |'''
												for metric_name in sorted(lowerCamelCase):
																		__lowerCAmelCase							       =				benchmark_res[metric_name]
																		__lowerCAmelCase							       =				metric_vals['''new''']
																		__lowerCAmelCase							       =				metric_vals.get('''old''',				lowerCamelCase)
																		__lowerCAmelCase							       =				metric_vals.get('''diff''',				lowerCamelCase)

																		__lowerCAmelCase							       =				F""" {new_val:f}""" if isinstance(lowerCamelCase,				(int, float)) else '''None'''

																		if old_val is not None:
																								val_str += F""" / {old_val:f}""" if isinstance(lowerCamelCase,				(int, float)) else "None"
																		if dif_val is not None:
																								val_str += F""" ({dif_val:f})""" if isinstance(lowerCamelCase,				(int, float)) else "None"

																		title += " " + metric_name + " |"
																		lines += "---|"
																		value += val_str + " |"

												output_md += [title, lines, value, " "]

						output_md.append('''</details>''')

						with open(lowerCamelCase,				'''w''',				encoding='''utf-8''') as f:
												f.writelines('''\n'''.join(lowerCamelCase))


if __name__ == "__main__":
	_UpperCAmelCase				: str							    =				sys.argv[1]
	_UpperCAmelCase				: Optional[Any]							    =				sys.argv[2]

	format_json_to_md(input_json_file, output_md_file)

