





"""simple docstring"""






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A	     =					'''src/transformers'''
A	     =					'''docs/source/en/tasks'''

def       __A (     a_  :List[Any]				,    a_  :List[Any]				,    a_  :List[str])						->						List[str]:
				with open(a_				,    '''r'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
								__a	:  List[str]         =					f.readlines()
				# Find the start prompt.
				__a	:  Optional[Any]         =					0
				while not lines[start_index].startswith(a_):
								start_index += 1
				start_index += 1

				__a	:  int         =					start_index
				while not lines[end_index].startswith(a_):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]) <= 1:
								start_index += 1
				while len(lines[end_index]) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A	     =					direct_transformers_import(TRANSFORMERS_PATH)

A	     =					{
    '''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    '''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    '''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    '''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    '''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    '''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    '''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    '''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    '''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    '''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    '''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    '''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    '''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    '''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A	     =					{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}

def       __A (     a_  :Optional[Any])						->						Any:
				__a	:  List[Any]         =					TASK_GUIDE_TO_MODELS[task_guide]
				__a	:  List[Any]         =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_				,    set())
				__a	:  Any         =					{
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]) + "\n"

def       __A (     a_  :Optional[int]				,    a_  :Dict=False)						->						Any:
				__a    ,  __a    ,  __a    ,  __a	:  Any         =					_find_text_in_file(
				    filename=os.path.join(a_				,    a_)				,    start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''				,    end_prompt='''<!--End of the generated tip-->'''				,    )

				__a	:  Optional[Any]         =					get_model_list_for_task(a_)

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(a_				,    a_)				,    '''w'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
								else:
												raise ValueError(
												    F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
												    ''' to fix this.''')


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		A	     =					parser.parse_args()

		for task_guide in TASK_GUIDE_TO_MODELS.keys():
				check_model_list_for_task(task_guide, args.fix_and_overwrite)






"""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 (     a_  :List[Any]=None				,    a_  :Tuple=None)						->						List[Any]:
				return field(default_factory=lambda: default				,    metadata=a_)




@dataclass
class 			__lowercase     :







					'''simple docstring'''




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






def       __A (     a_  :Optional[Any])						->						Any:
				try:
								int(a_)
								return True
				except ValueError:
								return False

def       __A (     a_  :List[Any])						->						Any:
				try:
								float(a_)
								return True
				except ValueError:
								return False




class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Dict         =					args
									__a	:  Tuple         =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

									with open(self.args.csv_file		,     newline=''''''       ) as csv_file:
													__a	:  int         =					csv.DictReader(_UpperCAmelCase       )
													for row in reader:
																	__a	:  Union[str, 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	:  Optional[int]         =					int(row['''result''']       )
																	elif can_convert_to_float(row['''result''']       ):
																					# value is not None
																					__a	:  Optional[Any]         =					float(row['''result''']       )







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  Optional[int]         =					plt.subplots()
									__a	:  str         =					'''Time usage''' if self.args.is_time else '''Memory usage'''
									__a	:  str         =					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	:  str         =					sorted(set(self.result_dict[model_name]['''bsz''']       )       )
													__a	:  Dict         =					sorted(set(self.result_dict[model_name]['''seq_len''']       )       )
													__a	:  Dict         =					self.result_dict[model_name]['''result''']

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

													__a	:  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:
																					__a	:  Optional[int]         =					np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,     dtype=_UpperCAmelCase		,     )
																	else:
																					__a	:  Dict         =					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	:  Any         =					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	:  Optional[Any]         =					title_str[:-4]
									__a	:  Optional[int]         =					'''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       __A (     )						->						List[str]:
				__a	:  List[str]         =					HfArgumentParser(a_)
				__a	:  Optional[int]         =					parser.parse_args_into_dataclasses()[0]
				__a	:  Tuple         =					Plot(args=a_)
				plot.plot()


if __name__ == "__main__":
		main()