





'''simple docstring'''






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.bert.modeling_flax_bert import (
					    FlaxBertForMaskedLM,
					    FlaxBertForMultipleChoice,
					    FlaxBertForNextSentencePrediction,
					    FlaxBertForPreTraining,
					    FlaxBertForQuestionAnswering,
					    FlaxBertForSequenceClassification,
					    FlaxBertForTokenClassification,
					    FlaxBertModel,
					)
class 		lowerCamelCase  (      unittest.TestCase       ):



	def __init__(   self		,      a_		,      a_=13		,      a_=7		,      a_=True		,      a_=True		,      a_=True		,      a_=True		,      a_=99		,      a_=32		,      a_=5		,      a_=4		,      a_=37		,      a_="gelu"		,      a_=0.1		,      a_=0.1		,      a_=512		,      a_=16		,      a_=2		,      a_=0.02		,      a_=4		,      ):
			lowerCAmelCase     :    List[str]						=   parent
			lowerCAmelCase     :    Any						=   batch_size
			lowerCAmelCase     :    Optional[int]						=   seq_length
			lowerCAmelCase     :    Dict						=   is_training
			lowerCAmelCase     :    Union[str, Any]						=   use_attention_mask
			lowerCAmelCase     :    str						=   use_token_type_ids
			lowerCAmelCase     :    List[str]						=   use_labels
			lowerCAmelCase     :    List[Any]						=   vocab_size
			lowerCAmelCase     :    Optional[int]						=   hidden_size
			lowerCAmelCase     :    Dict						=   num_hidden_layers
			lowerCAmelCase     :    int						=   num_attention_heads
			lowerCAmelCase     :    Tuple						=   intermediate_size
			lowerCAmelCase     :    List[Any]						=   hidden_act
			lowerCAmelCase     :    Union[str, Any]						=   hidden_dropout_prob
			lowerCAmelCase     :    Optional[int]						=   attention_probs_dropout_prob
			lowerCAmelCase     :    int						=   max_position_embeddings
			lowerCAmelCase     :    Any						=   type_vocab_size
			lowerCAmelCase     :    List[str]						=   type_sequence_label_size
			lowerCAmelCase     :    Optional[Any]						=   initializer_range
			lowerCAmelCase     :    Tuple						=   num_choices



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size	)

			lowerCAmelCase     :    int						=   None
			if self.use_attention_mask:
					lowerCAmelCase     :    int						=   random_attention_mask([self.batch_size, self.seq_length]	)

			lowerCAmelCase     :    Tuple						=   None
			if self.use_token_type_ids:
					lowerCAmelCase     :    str						=   ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size	)

			lowerCAmelCase     :    Union[str, Any]						=   BertConfig(
			    vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      type_vocab_size=self.type_vocab_size		,      is_decoder=a_		,      initializer_range=self.initializer_range		,      )

			return config, input_ids, token_type_ids, attention_mask



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   self.prepare_config_and_inputs()
			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   config_and_inputs
			lowerCAmelCase     :    Tuple						=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
			return config, inputs_dict





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.prepare_config_and_inputs()
			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    str						=   config_and_inputs

			lowerCAmelCase     :    Optional[int]						=   True
			lowerCAmelCase     :    Any						=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
			lowerCAmelCase     :    Tuple						=   ids_tensor([self.batch_size, self.seq_length]		,      vocab_size=2	)

			return (
			    config,
			    input_ids,
			    attention_mask,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)






@require_flax
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   True

	snake_case_      =   (
	    (
	        FlaxBertModel,
	        FlaxBertForPreTraining,
	        FlaxBertForMaskedLM,
	        FlaxBertForMultipleChoice,
	        FlaxBertForQuestionAnswering,
	        FlaxBertForNextSentencePrediction,
	        FlaxBertForSequenceClassification,
	        FlaxBertForTokenClassification,
	        FlaxBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   FlaxBertModelTester(self	)





	@slow
	def   _lowerCamelCase    (   self	):
			# Only check this for base model, not necessary for all model classes.
			# This will also help speed-up tests.
			lowerCAmelCase     :    Any						=   FlaxBertModel.from_pretrained("bert-base-cased"	)
			lowerCAmelCase     :    List[str]						=   model(np.ones((1, 1)	)	)
			self.assertIsNotNone(a_	)










'''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()



