






import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser






def    UpperCamelCase__  (   UpperCAmelCase=None    ,	UpperCAmelCase=None     )     ->		int:







							"""simple docstring"""





							return field(default_factory=lambda: default    ,	metadata=_snake_case     )

@dataclass
class      UpperCamelCase_							:
						lowercase									=  field(
						    metadata={'''help''': '''The csv file to plot.'''} ,       )
						lowercase									=  field(
						    default=__UpperCAmelCase ,       metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''} ,       )
						lowercase									=  field(
						    default=__UpperCAmelCase ,       metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''} ,       )
						lowercase									=  field(
						    default=__UpperCAmelCase ,       metadata={'''help''': '''Disable logarithmic scale when plotting'''} ,       )
						lowercase									=  field(
						    default=__UpperCAmelCase ,       metadata={
						        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
						    } ,       )
						lowercase									=  field(
						    default=__UpperCAmelCase ,       metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''} ,       )
						lowercase									=  list_field(
						    default=__UpperCAmelCase ,       metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}       )






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Any:







							"""simple docstring"""





							try:
														int(_snake_case     )
														return True
							except ValueError:
														return False






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		List[str]:







							"""simple docstring"""





							try:
														float(_snake_case     )
														return True
							except ValueError:
														return False

class      UpperCamelCase_							:




						def __init__(    self		,	lowercase       )			->						int:
													_a  :    List[Any]			=						args
													_a  :    str			=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

													with open(self.args.csv_file		,	newline=''''''       ) as csv_file:
																				_a  :    Optional[Any]			=						csv.DictReader(UpperCAmelCase_       )
																				for row in reader:
																											_a  :    Any			=						row['''model''']
																											self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']       )       )
																											self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']       )       )
																											if can_convert_to_int(row['''result''']       ):
																																		# value is not None
																																		_a  :    int			=						int(row['''result''']       )
																											elif can_convert_to_float(row['''result''']       ):
																																		# value is not None
																																		_a  :    Optional[Any]			=						float(row['''result''']       )







						def      snake_case__(    self       )			->						Any:
													_a		,					_a  :    List[Any]			=						plt.subplots()
													_a  :    Union[str, Any]			=						'''Time usage''' if self.args.is_time else '''Memory usage'''
													_a  :    Dict			=						title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

													if not self.args.no_log_scale:
																				# set logarithm scales
																				ax.set_xscale('''log'''       )
																				ax.set_yscale('''log'''       )

													for axis in [ax.xaxis, ax.yaxis]:
																				axis.set_major_formatter(ScalarFormatter()       )

													for model_name_idx, model_name in enumerate(self.result_dict.keys()       ):
																				_a  :    int			=						sorted(set(self.result_dict[model_name]['''bsz''']       )       )
																				_a  :    Any			=						sorted(set(self.result_dict[model_name]['''seq_len''']       )       )
																				_a  :    List[Any]			=						self.result_dict[model_name]['''result''']

																				((_a)		,					(_a))  :    List[str]			=						(
																				    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																				)

																				_a  :    str			=						(
																				    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																				)

																				for inner_loop_value in inner_loop_array:
																											if self.args.plot_along_batch:
																																		_a  :    List[Any]			=						np.asarray(
																																		    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,	dtype=UpperCAmelCase_		,	)
																											else:
																																		_a  :    List[Any]			=						np.asarray(
																																		    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,	dtype=np.floataa		,	)

																											((_a)		,					(_a))  :    Union[str, Any]			=						(
																											    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																											)

																											_a  :    List[str]			=						np.asarray(UpperCAmelCase_		,	UpperCAmelCase_       )[: len(UpperCAmelCase_       )]
																											plt.scatter(
																											    UpperCAmelCase_		,	UpperCAmelCase_		,	label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}'       )
																											plt.plot(UpperCAmelCase_		,	UpperCAmelCase_		,	'''--'''       )

																				title_str += F' {label_model_name} vs.'

													_a  :    List[str]			=						title_str[:-4]
													_a  :    List[str]			=						'''Time in s''' if self.args.is_time else '''Memory in MB'''

													# plot
													plt.title(UpperCAmelCase_       )
													plt.xlabel(UpperCAmelCase_       )
													plt.ylabel(UpperCAmelCase_       )
													plt.legend()

													if self.args.figure_png_file is not None:
																				plt.savefig(self.args.figure_png_file       )
													else:
																				plt.show()






def    UpperCamelCase__  (   )     ->		Optional[Any]:







							"""simple docstring"""





							_a  :    List[Any]			=						HfArgumentParser(_snake_case     )
							_a  :    Optional[Any]			=						parser.parse_args_into_dataclasses()[0]
							_a  :    Dict			=						Plot(args=_snake_case     )
							plot.plot()


if __name__ == "__main__":
						main()





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# Initialize PyTorch model
							lowerCAmelCase =		CanineConfig()
							lowerCAmelCase =		CanineModel(_snake_case				)
							model.eval()

							print(F"""Building PyTorch model from configuration: {config}"""				)

							# Load weights from tf checkpoint
							load_tf_weights_in_canine(_snake_case    ,			_snake_case    ,			_snake_case				)

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
							model.save_pretrained(_snake_case				)

							# Save tokenizer files
							lowerCAmelCase =		CanineTokenizer()
							print(F"""Save tokenizer files to {pytorch_dump_path}"""				)
							tokenizer.save_pretrained(_snake_case				)


if __name__ == "__main__":
		UpperCAmelCase_    =argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to a folder where the PyTorch model will be placed.""",
		)
		UpperCAmelCase_    =parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)



