





'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase       		=      random.Random()

if is_torch_available():
					import torch






def      __A						(							a_						:  Optional[Any]      ,a_						:  str=1.0      ,a_						:  Dict=None      ,a_						:  Optional[Any]=None	):
		if rng is None:
				lowerCAmelCase     :    List[Any]						=   global_rng

		lowerCAmelCase     :    Tuple						=   []
		for batch_idx in range(shape[0]	):
				values.append([]	)
				for _ in range(shape[1]	):
						values[-1].append(rng.random() * scale	)

		return values
class 		lowerCamelCase  (      unittest.TestCase       ):



	def __init__(   self		,      a_		,      a_=7		,      a_=400		,      a_=2_000		,      a_=1		,      a_=0.0		,      a_=16_000		,      a_=True		,      a_=True		,      ):
			lowerCAmelCase     :    List[Any]						=   parent
			lowerCAmelCase     :    str						=   batch_size
			lowerCAmelCase     :    List[str]						=   min_seq_length
			lowerCAmelCase     :    Union[str, Any]						=   max_seq_length
			lowerCAmelCase     :    int						=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowerCAmelCase     :    str						=   feature_size
			lowerCAmelCase     :    Union[str, Any]						=   padding_value
			lowerCAmelCase     :    Optional[int]						=   sampling_rate
			lowerCAmelCase     :    Union[str, Any]						=   return_attention_mask
			lowerCAmelCase     :    List[Any]						=   do_normalize



	def   _lowerCamelCase    (   self	):
			return {
			    "feature_size": self.feature_size,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			    "do_normalize": self.do_normalize,
			}





	def   _lowerCamelCase    (   self		,      a_=False		,      a_=False	):
			def _flatten(a_	):
					return list(itertools.chain(*a_	)	)

			if equal_length:
					lowerCAmelCase     :    Tuple						=   floats_list((self.batch_size, self.max_seq_length)	)
			else:
					# make sure that inputs increase in size
					lowerCAmelCase     :    Dict						=   [
					    _flatten(floats_list((x, self.feature_size)	)	)
					    for x in range(self.min_seq_length		,      self.max_seq_length		,      self.seq_length_diff	)
					]

			if numpify:
					lowerCAmelCase     :    List[str]						=   [np.asarray(a_	) for x in speech_inputs]

			return speech_inputs






@require_torch
@require_torchaudio
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   ASTFeatureExtractor



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   ASTFeatureExtractionTester(self	)



	def   _lowerCamelCase    (   self	):
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowerCAmelCase     :    List[str]						=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
			# create three inputs of length 800, 1000, and 1200
			lowerCAmelCase     :    Optional[Any]						=   [floats_list((1, x)	)[0] for x in range(800		,      1_400		,      200	)]
			lowerCAmelCase     :    str						=   [np.asarray(a_	) for speech_input in speech_inputs]

			# Test not batched input
			lowerCAmelCase     :    Any						=   feat_extract(speech_inputs[0]		,      return_tensors="np"	).input_values
			lowerCAmelCase     :    Optional[Any]						=   feat_extract(np_speech_inputs[0]		,      return_tensors="np"	).input_values
			self.assertTrue(np.allclose(a_		,      a_		,      atol=1e-3	)	)

			# Test batched
			lowerCAmelCase     :    Dict						=   feat_extract(a_		,      padding=a_		,      return_tensors="np"	).input_values
			lowerCAmelCase     :    str						=   feat_extract(a_		,      padding=a_		,      return_tensors="np"	).input_values
			for enc_seq_a, enc_seq_a in zip(a_		,      a_	):
					self.assertTrue(np.allclose(a_		,      a_		,      atol=1e-3	)	)

			# Test 2-D numpy arrays are batched.
			lowerCAmelCase     :    List[Any]						=   [floats_list((1, x)	)[0] for x in (800, 800, 800)]
			lowerCAmelCase     :    int						=   np.asarray(a_	)
			lowerCAmelCase     :    List[str]						=   feat_extract(a_		,      return_tensors="np"	).input_values
			lowerCAmelCase     :    Union[str, Any]						=   feat_extract(a_		,      return_tensors="np"	).input_values
			for enc_seq_a, enc_seq_a in zip(a_		,      a_	):
					self.assertTrue(np.allclose(a_		,      a_		,      atol=1e-3	)	)



	@require_torch
	def   _lowerCamelCase    (   self	):
			import torch

			lowerCAmelCase     :    List[Any]						=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
			lowerCAmelCase     :    int						=   np.random.rand(100	).astype(np.floataa	)
			lowerCAmelCase     :    Tuple						=   np_speech_inputs.tolist()

			for inputs in [py_speech_inputs, np_speech_inputs]:
					lowerCAmelCase     :    Any						=   feature_extractor.pad([{"input_values": inputs}]		,      return_tensors="np"	)
					self.assertTrue(np_processed.input_values.dtype == np.floataa	)
					lowerCAmelCase     :    List[Any]						=   feature_extractor.pad([{"input_values": inputs}]		,      return_tensors="pt"	)
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)



	def   _lowerCamelCase    (   self		,      a_	):
			from datasets import load_dataset

			lowerCAmelCase     :    Union[str, Any]						=   load_dataset("hf-internal-testing/librispeech_asr_dummy"		,      "clean"		,      split="validation"	)
			# automatic decoding with librispeech
			lowerCAmelCase     :    Optional[Any]						=   ds.sort("id"	).select(range(a_	)	)[:num_samples]["audio"]

			return [x["array"] for x in speech_samples]





	@require_torch
	def   _lowerCamelCase    (   self	):
			# fmt: off
			lowerCAmelCase     :    Dict						=   torch.tensor(
			    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
			     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
			     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
			     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]	)
			# fmt: on

			lowerCAmelCase     :    List[Any]						=   self._load_datasamples(1	)
			lowerCAmelCase     :    str						=   ASTFeatureExtractor()
			lowerCAmelCase     :    Union[str, Any]						=   feature_extractor(a_		,      return_tensors="pt"	).input_values
			self.assertEquals(input_values.shape		,      (1, 1_024, 128)	)
			self.assertTrue(torch.allclose(input_values[0, 0, :30]		,      a_		,      atol=1e-4	)	)










'''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()



