
# Copyright 2023 The HuggingFace Inc. 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.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_UpperCAmelCase   :					List[Any]    =			{
    "agents": ["Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent"],
    "base": ["PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Dict    =			["DocumentQuestionAnsweringTool"]
					_UpperCAmelCase   :					Union[str, Any]    =			["ImageCaptioningTool"]
					_UpperCAmelCase   :					Optional[int]    =			["ImageQuestionAnsweringTool"]
					_UpperCAmelCase   :					int    =			["ImageSegmentationTool"]
					_UpperCAmelCase   :					Any    =			["SpeechToTextTool"]
					_UpperCAmelCase   :					Tuple    =			["TextClassificationTool"]
					_UpperCAmelCase   :					List[str]    =			["TextQuestionAnsweringTool"]
					_UpperCAmelCase   :					Tuple    =			["TextSummarizationTool"]
					_UpperCAmelCase   :					Tuple    =			["TextToSpeechTool"]
					_UpperCAmelCase   :					int    =			["TranslationTool"]

if TYPE_CHECKING:
					from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
					from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .document_question_answering import DocumentQuestionAnsweringTool
										from .image_captioning import ImageCaptioningTool
										from .image_question_answering import ImageQuestionAnsweringTool
										from .image_segmentation import ImageSegmentationTool
										from .speech_to_text import SpeechToTextTool
										from .text_classification import TextClassificationTool
										from .text_question_answering import TextQuestionAnsweringTool
										from .text_summarization import TextSummarizationTool
										from .text_to_speech import TextToSpeechTool
										from .translation import TranslationTool
else:
					import sys

					_UpperCAmelCase   :					Optional[Any]    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


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			(   lowercase=None       ,			lowercase=None					)  ->    Tuple:
		'''simple docstring'''

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


@dataclass
class 							lowercase   :
		__lowercase			:			str      	=							field(
		    metadata={"help": "The csv file to plot."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Disable logarithmic scale when plotting"}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={
		        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
		    }    ,  )
		__lowercase			:			Optional[str]      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,  )
		__lowercase			:			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			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	args
				UpperCamelCase            =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

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



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	plt.subplots()
				UpperCamelCase            =	'Time usage' if self.args.is_time else 'Memory usage'
				UpperCamelCase            =	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            =	sorted(set(self.result_dict[model_name]['bsz']			)			)
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['seq_len']			)			)
						UpperCamelCase            =	self.result_dict[model_name]['result']

						((UpperCamelCase)      ,    (UpperCamelCase))            =	(
						    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
						)

						UpperCamelCase            =	(
						    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            =	np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							, dtype=A_							, )
								else:
										UpperCamelCase            =	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))            =	(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								UpperCamelCase            =	np.asarray(A_							, A_			)[: len(A_			)]
								plt.scatter(
								    A_							, A_							, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
								plt.plot(A_							, A_							, '--'			)

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

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

				# plot
				plt.title(A_			)
				plt.xlabel(A_			)
				plt.ylabel(A_			)
				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            =	HfArgumentParser(lowercase					)
		UpperCamelCase            =	parser.parse_args_into_dataclasses()[0]
		UpperCamelCase            =	Plot(args=lowercase					)
		plot.plot()


if __name__ == "__main__":
					main()
