





'''simple docstring'''






# 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.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()










'''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_						:  Any=None      ,a_						:  List[Any]=None	):
		return field(default_factory=lambda: default      ,metadata=a_	)
@dataclass
class 		lowerCamelCase  :
	snake_case_      =   field(
	    metadata={"help": "The csv file to plot."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Disable logarithmic scale when plotting"}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,   )
	snake_case_      =   list_field(
	    default=_A      ,   metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )






def      __A						(							a_						:  Tuple	):
		try:
				int(a_	)
				return True
		except ValueError:
				return False






def      __A						(							a_						:  int	):
		try:
				float(a_	)
				return True
		except ValueError:
				return False
class 		lowerCamelCase  :



	def __init__(   self		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   args
			lowerCAmelCase     :    List[str]						=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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





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

					((lowerCAmelCase)	,		(lowerCAmelCase))     :    str						=   (
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

					lowerCAmelCase     :    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:
									lowerCAmelCase     :    int						=   np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,      dtype=a_		,      )
							else:
									lowerCAmelCase     :    Any						=   np.asarray(
									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,      dtype=np.floataa		,      )

							((lowerCAmelCase)	,		(lowerCAmelCase))     :    Any						=   (
							    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
							)

							lowerCAmelCase     :    Union[str, Any]						=   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.'''

			lowerCAmelCase     :    List[str]						=   title_str[:-4]
			lowerCAmelCase     :    List[Any]						=   "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						(							):
		lowerCAmelCase     :    Optional[Any]						=   HfArgumentParser(a_	)
		lowerCAmelCase     :    List[Any]						=   parser.parse_args_into_dataclasses()[0]
		lowerCAmelCase     :    str						=   Plot(args=a_	)
		plot.plot()


if __name__ == "__main__":
					main()



