

'''simple docstring'''
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       a__    (  _SCREAMING_SNAKE_CASE					:    Tuple=None     ,							_SCREAMING_SNAKE_CASE					:    Optional[int]=None							)			-> Union[str, Any]:


	"""simple docstring"""





	return field(default_factory=lambda: default     ,							metadata=_SCREAMING_SNAKE_CASE							)
@dataclass
class  _snake_case						:
			__A   :     str          =field(
			    metadata={"help": "The csv file to plot."}       ,       )
			__A   :     bool          =field(
			    default=__SCREAMING_SNAKE_CASE       ,       metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}       ,       )
			__A   :     bool          =field(
			    default=__SCREAMING_SNAKE_CASE       ,       metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}       ,       )
			__A   :     bool          =field(
			    default=__SCREAMING_SNAKE_CASE       ,       metadata={"help": "Disable logarithmic scale when plotting"}       ,       )
			__A   :     bool          =field(
			    default=__SCREAMING_SNAKE_CASE       ,       metadata={
			        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
			    }       ,       )
			__A   :     Optional[str]          =field(
			    default=__SCREAMING_SNAKE_CASE       ,       metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}       ,       )
			__A   :     Optional[List[str]]          =list_field(
			    default=__SCREAMING_SNAKE_CASE       ,       metadata={"help": "List of model names that are used instead of the ones in the csv file."})




def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> Optional[Any]:


	"""simple docstring"""





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


def       a__    (  _SCREAMING_SNAKE_CASE					:    Union[str, Any]							)			-> List[str]:


	"""simple docstring"""





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






			def __init__(   self       ,_snake_case			):
				UpperCAmelCase_			:	int      =   args
				UpperCAmelCase_			:	Tuple      =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

				with open(self.args.csv_file       ,newline=""			) as csv_file:
					UpperCAmelCase_			:	List[str]      =   csv.DictReader(_snake_case			)
					for row in reader:
						UpperCAmelCase_			:	Dict      =   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
							UpperCAmelCase_			:	str      =   int(row["result"]			)
						elif can_convert_to_float(row["result"]			):
							# value is not None
							UpperCAmelCase_			:	Optional[Any]      =   float(row["result"]			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	Any      =   plt.subplots()
				UpperCAmelCase_			:	str      =   "Time usage" if self.args.is_time else "Memory usage"
				UpperCAmelCase_			:	Union[str, Any]      =   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()			):
					UpperCAmelCase_			:	int      =   sorted(set(self.result_dict[model_name]["bsz"]			)			)
					UpperCAmelCase_			:	Any      =   sorted(set(self.result_dict[model_name]["seq_len"]			)			)
					UpperCAmelCase_			:	Optional[int]      =   self.result_dict[model_name]["result"]

					((UpperCAmelCase_) , (UpperCAmelCase_))			:	Dict      =   (
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

					UpperCAmelCase_			:	Union[str, Any]      =   (
					    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:
							UpperCAmelCase_			:	int      =   np.asarray(
							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,dtype=_snake_case       ,)
						else:
							UpperCAmelCase_			:	Optional[Any]      =   np.asarray(
							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,dtype=np.floataa       ,)

						((UpperCAmelCase_) , (UpperCAmelCase_))			:	List[Any]      =   (
						    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
						)

						UpperCAmelCase_			:	Tuple      =   np.asarray(_snake_case       ,_snake_case			)[: len(_snake_case			)]
						plt.scatter(
						    _snake_case       ,_snake_case       ,label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
						plt.plot(_snake_case       ,_snake_case       ,"--"			)

					title_str += f''' {label_model_name} vs.'''

				UpperCAmelCase_			:	List[Any]      =   title_str[:-4]
				UpperCAmelCase_			:	Union[str, Any]      =   "Time in s" if self.args.is_time else "Memory in MB"

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

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




def       a__    (  )			-> Dict:


	"""simple docstring"""





	UpperCAmelCase_			:	str      =   HfArgumentParser(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Optional[int]      =   parser.parse_args_into_dataclasses()[0]
	UpperCAmelCase_			:	Optional[int]      =   Plot(args=_SCREAMING_SNAKE_CASE							)
	plot.plot()


if __name__ == "__main__":
					main()







'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    str							)			-> Optional[Any]:


	"""simple docstring"""





	if name != "training_function" and name != "main":
		raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''							)
	UpperCAmelCase_ , UpperCAmelCase_			:	int      =   [], False
	for line in lines:
		if not found_start and F'''def {name}''' in line:
			UpperCAmelCase_			:	List[str]      =   True
			good_lines.append(_SCREAMING_SNAKE_CASE							)
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Optional[Any]:


	"""simple docstring"""





	return [line for line in lines if not line.lstrip().startswith("#"							) and line != "\n"]





def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    bool     ,							_SCREAMING_SNAKE_CASE					:    str = None							)			-> int:


	"""simple docstring"""





	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Union[str, Any]      =   f.readlines()
	with open(os.path.abspath(os.path.join("examples"     ,							"nlp_example.py"							)							)     ,							"r"							) as f:
		UpperCAmelCase_			:	Dict      =   f.readlines()
	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Any      =   f.readlines()
	if secondary_filename is not None:
		with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
			UpperCAmelCase_			:	Tuple      =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		UpperCAmelCase_			:	List[str]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
	else:
		UpperCAmelCase_			:	Union[str, Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	str      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	Tuple      =   clean_lines(
			    get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)

	UpperCAmelCase_			:	List[Any]      =   "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

	# Specific code in our script that differs from the full version, aka what is new
	UpperCAmelCase_			:	Dict      =   []
	UpperCAmelCase_			:	Any      =   []  # We keep track of the idxs just in case it's a repeated statement
	UpperCAmelCase_			:	Optional[int]      =   iter(_SCREAMING_SNAKE_CASE							)
	for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
		if i not in passed_idxs:
			UpperCAmelCase_			:	str      =   next(_SCREAMING_SNAKE_CASE							)
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					UpperCAmelCase_			:	List[Any]      =   next(_SCREAMING_SNAKE_CASE							)

    # Extract out just the new parts from the full_file_training_func
	UpperCAmelCase_			:	Tuple      =   []
	UpperCAmelCase_			:	Optional[Any]      =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(_SCREAMING_SNAKE_CASE							):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)

    # Finally, get the overall diff
	UpperCAmelCase_			:	Union[str, Any]      =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		UpperCAmelCase_			:	int      =   [line for line in full_file_contents if line not in secondary_file_func]
		UpperCAmelCase_			:	str      =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example




