
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 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int]=None ,			_UpperCAmelCase  :   Optional[int]=None  )   ->			Tuple:






						'''simple docstring'''

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




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



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int]  )   ->			str:






						'''simple docstring'''

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str]  )   ->			str:






						'''simple docstring'''

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




class   __lowercase						:






	def __init__(self				: Optional[Any]      ,	snake_case				: List[Any]    ) ->			Tuple:
							_lowercase       :	List[Any]           =   args
							_lowercase       :	List[str]           =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

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






	def       _a(self				: List[str]    ) ->			Optional[int]:
							_lowercase      ,    _lowercase       :	Optional[Any]           =   plt.subplots()
							_lowercase       :	Optional[Any]           =   "Time usage" if self.args.is_time else "Memory usage"
							_lowercase       :	Optional[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()    ):
													_lowercase       :	Tuple           =   sorted(set(self.result_dict[model_name]["bsz"]    )    )
													_lowercase       :	List[str]           =   sorted(set(self.result_dict[model_name]["seq_len"]    )    )
													_lowercase       :	Tuple           =   self.result_dict[model_name]["result"]

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

													_lowercase       :	List[str]           =   (
													    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:
																									_lowercase       :	List[Any]           =   np.asarray(
																									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,	dtype=snake_case      ,	)
																			else:
																									_lowercase       :	Dict           =   np.asarray(
																									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,	dtype=np.floataa      ,	)

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

																			_lowercase       :	str           =   np.asarray(snake_case      ,	snake_case    )[: len(snake_case    )]
																			plt.scatter(
																			    snake_case      ,	snake_case      ,	label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""    )
																			plt.plot(snake_case      ,	snake_case      ,	"--"    )

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

							_lowercase       :	Tuple           =   title_str[:-4]
							_lowercase       :	str           =   "Time in s" if self.args.is_time else "Memory in MB"

							# plot
							plt.title(snake_case    )
							plt.xlabel(snake_case    )
							plt.ylabel(snake_case    )
							plt.legend()

							if self.args.figure_png_file is not None:
													plt.savefig(self.args.figure_png_file    )
							else:
													plt.show()



def 		UpperCamelCase     (       )   ->			List[str]:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   HfArgumentParser(_UpperCAmelCase  )
						_lowercase       :	List[str]           =   parser.parse_args_into_dataclasses()[0]
						_lowercase       :	Dict           =   Plot(args=_UpperCAmelCase  )
						plot.plot()


if __name__ == "__main__":
							main()






import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    DistilBertForMaskedLM,
							    DistilBertForMultipleChoice,
							    DistilBertForQuestionAnswering,
							    DistilBertForSequenceClassification,
							    DistilBertForTokenClassification,
							    DistilBertModel,
							)




class   __lowercase						(						__snake_case       ):






	def __init__(self				: Dict      ,	snake_case				: str      ,	snake_case				: str=13      ,	snake_case				: Union[str, Any]=7      ,	snake_case				: int=True      ,	snake_case				: Any=True      ,	snake_case				: str=False      ,	snake_case				: Optional[Any]=True      ,	snake_case				: Optional[Any]=99      ,	snake_case				: Dict=32      ,	snake_case				: Union[str, Any]=5      ,	snake_case				: List[str]=4      ,	snake_case				: Optional[int]=37      ,	snake_case				: Optional[int]="gelu"      ,	snake_case				: Optional[Any]=0.1      ,	snake_case				: Optional[int]=0.1      ,	snake_case				: List[Any]=512      ,	snake_case				: List[Any]=16      ,	snake_case				: Optional[int]=2      ,	snake_case				: Tuple=0.02      ,	snake_case				: Union[str, Any]=3      ,	snake_case				: Any=4      ,	snake_case				: Any=None      ,	) ->			List[Any]:
							_lowercase       :	Dict           =   parent
							_lowercase       :	int           =   batch_size
							_lowercase       :	Optional[Any]           =   seq_length
							_lowercase       :	int           =   is_training
							_lowercase       :	Dict           =   use_input_mask
							_lowercase       :	Union[str, Any]           =   use_token_type_ids
							_lowercase       :	Tuple           =   use_labels
							_lowercase       :	int           =   vocab_size
							_lowercase       :	Union[str, Any]           =   hidden_size
							_lowercase       :	int           =   num_hidden_layers
							_lowercase       :	Optional[Any]           =   num_attention_heads
							_lowercase       :	Tuple           =   intermediate_size
							_lowercase       :	Dict           =   hidden_act
							_lowercase       :	int           =   hidden_dropout_prob
							_lowercase       :	int           =   attention_probs_dropout_prob
							_lowercase       :	Tuple           =   max_position_embeddings
							_lowercase       :	str           =   type_vocab_size
							_lowercase       :	int           =   type_sequence_label_size
							_lowercase       :	Any           =   initializer_range
							_lowercase       :	Optional[Any]           =   num_labels
							_lowercase       :	Optional[Any]           =   num_choices
							_lowercase       :	str           =   scope






	def       _a(self				: int    ) ->			Dict:
							_lowercase       :	str           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size    )

							_lowercase       :	Tuple           =   None
							if self.use_input_mask:
													_lowercase       :	int           =   random_attention_mask([self.batch_size, self.seq_length]    )

							_lowercase       :	Tuple           =   None
							_lowercase       :	Union[str, Any]           =   None
							_lowercase       :	Tuple           =   None
							if self.use_labels:
													_lowercase       :	Dict           =   ids_tensor([self.batch_size]      ,	self.type_sequence_label_size    )
													_lowercase       :	Any           =   ids_tensor([self.batch_size, self.seq_length]      ,	self.num_labels    )
													_lowercase       :	str           =   ids_tensor([self.batch_size]      ,	self.num_choices    )

							_lowercase       :	Dict           =   self.get_config()

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






	def       _a(self				: Any    ) ->			Any:
							return DistilBertConfig(
							    vocab_size=self.vocab_size      ,	dim=self.hidden_size      ,	n_layers=self.num_hidden_layers      ,	n_heads=self.num_attention_heads      ,	hidden_dim=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	dropout=self.hidden_dropout_prob      ,	attention_dropout=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	initializer_range=self.initializer_range      ,	)






	def       _a(self				: int      ,	snake_case				: Optional[Any]      ,	snake_case				: Tuple      ,	snake_case				: List[str]      ,	snake_case				: Tuple      ,	snake_case				: Any      ,	snake_case				: Dict    ) ->			Optional[int]:
							_lowercase       :	Optional[int]           =   DistilBertModel(config=snake_case    )
							model.to(snake_case    )
							model.eval()
							_lowercase       :	List[Any]           =   model(snake_case      ,	snake_case    )
							_lowercase       :	int           =   model(snake_case    )
							self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)    )






	def       _a(self				: int      ,	snake_case				: Optional[Any]      ,	snake_case				: Tuple      ,	snake_case				: Optional[Any]      ,	snake_case				: List[str]      ,	snake_case				: Optional[Any]      ,	snake_case				: Optional[Any]    ) ->			Dict:
							_lowercase       :	Optional[int]           =   DistilBertForMaskedLM(config=snake_case    )
							model.to(snake_case    )
							model.eval()
							_lowercase       :	int           =   model(snake_case      ,	attention_mask=snake_case      ,	labels=snake_case    )
							self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.seq_length, self.vocab_size)    )






	def       _a(self				: Tuple      ,	snake_case				: List[str]      ,	snake_case				: Any      ,	snake_case				: List[str]      ,	snake_case				: Dict      ,	snake_case				: str      ,	snake_case				: str    ) ->			Any:
							_lowercase       :	Dict           =   DistilBertForQuestionAnswering(config=snake_case    )
							model.to(snake_case    )
							model.eval()
							_lowercase       :	List[str]           =   model(
							    snake_case      ,	attention_mask=snake_case      ,	start_positions=snake_case      ,	end_positions=snake_case    )
							self.parent.assertEqual(result.start_logits.shape      ,	(self.batch_size, self.seq_length)    )
							self.parent.assertEqual(result.end_logits.shape      ,	(self.batch_size, self.seq_length)    )






	def       _a(self				: Union[str, Any]      ,	snake_case				: str      ,	snake_case				: Dict      ,	snake_case				: Dict      ,	snake_case				: Union[str, Any]      ,	snake_case				: Optional[int]      ,	snake_case				: Dict    ) ->			Dict:
							_lowercase       :	str           =   self.num_labels
							_lowercase       :	Any           =   DistilBertForSequenceClassification(snake_case    )
							model.to(snake_case    )
							model.eval()
							_lowercase       :	Optional[Any]           =   model(snake_case      ,	attention_mask=snake_case      ,	labels=snake_case    )
							self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_labels)    )






	def       _a(self				: int      ,	snake_case				: Union[str, Any]      ,	snake_case				: Optional[Any]      ,	snake_case				: int      ,	snake_case				: Optional[int]      ,	snake_case				: Optional[int]      ,	snake_case				: str    ) ->			str:
							_lowercase       :	str           =   self.num_labels
							_lowercase       :	List[str]           =   DistilBertForTokenClassification(config=snake_case    )
							model.to(snake_case    )
							model.eval()

							_lowercase       :	str           =   model(snake_case      ,	attention_mask=snake_case      ,	labels=snake_case    )
							self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.seq_length, self.num_labels)    )






	def       _a(self				: List[str]      ,	snake_case				: int      ,	snake_case				: str      ,	snake_case				: Union[str, Any]      ,	snake_case				: Dict      ,	snake_case				: int      ,	snake_case				: Union[str, Any]    ) ->			Optional[Any]:
							_lowercase       :	str           =   self.num_choices
							_lowercase       :	Dict           =   DistilBertForMultipleChoice(config=snake_case    )
							model.to(snake_case    )
							model.eval()
							_lowercase       :	Optional[Any]           =   input_ids.unsqueeze(1    ).expand(-1      ,	self.num_choices      ,	-1    ).contiguous()
							_lowercase       :	int           =   input_mask.unsqueeze(1    ).expand(-1      ,	self.num_choices      ,	-1    ).contiguous()
							_lowercase       :	Tuple           =   model(
							    snake_case      ,	attention_mask=snake_case      ,	labels=snake_case      ,	)
							self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_choices)    )






	def       _a(self				: List[str]    ) ->			List[str]:
							_lowercase       :	Union[str, Any]           =   self.prepare_config_and_inputs()
							((_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase))       :	Union[str, Any]           =   config_and_inputs
							_lowercase       :	Union[str, Any]           =   {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_torch
class   __lowercase						(						__snake_case   , __snake_case   , unittest.TestCase       ):
	_A       =   (
	    (
	        DistilBertModel,
	        DistilBertForMaskedLM,
	        DistilBertForMultipleChoice,
	        DistilBertForQuestionAnswering,
	        DistilBertForSequenceClassification,
	        DistilBertForTokenClassification,
	    )
	    if is_torch_available()
	    else None
	)
	_A       =   (
	    {
	        "feature-extraction": DistilBertModel,
	        "fill-mask": DistilBertForMaskedLM,
	        "question-answering": DistilBertForQuestionAnswering,
	        "text-classification": DistilBertForSequenceClassification,
	        "token-classification": DistilBertForTokenClassification,
	        "zero-shot": DistilBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_A       =   True
	_A       =   True
	_A       =   True
	_A       =   True






	def       _a(self				: Dict    ) ->			List[Any]:
							_lowercase       :	Optional[Any]           =   DistilBertModelTester(self    )
							_lowercase       :	str           =   ConfigTester(self      ,	config_class=snake_case      ,	dim=37    )






	def       _a(self				: int    ) ->			List[str]:
							self.config_tester.run_common_tests()






	def       _a(self				: Optional[Any]    ) ->			Optional[int]:
							_lowercase       :	Tuple           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_model(*snake_case    )






	def       _a(self				: Any    ) ->			int:
							_lowercase       :	str           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_masked_lm(*snake_case    )






	def       _a(self				: Dict    ) ->			List[Any]:
							_lowercase       :	List[str]           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_question_answering(*snake_case    )






	def       _a(self				: str    ) ->			Tuple:
							_lowercase       :	Tuple           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_sequence_classification(*snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							_lowercase       :	Optional[Any]           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_token_classification(*snake_case    )






	def       _a(self				: Optional[int]    ) ->			Optional[int]:
							_lowercase       :	Any           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_multiple_choice(*snake_case    )






	@slow
	def       _a(self				: Optional[Any]    ) ->			Dict:
							for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	Tuple           =   DistilBertModel.from_pretrained(snake_case    )
													self.assertIsNotNone(snake_case    )






	@slow
	@require_torch_gpu
	def       _a(self				: Optional[int]    ) ->			Optional[int]:
							_lowercase      ,    _lowercase       :	Optional[int]           =   self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
													# BertForMultipleChoice behaves incorrectly in JIT environments.
													if model_class == DistilBertForMultipleChoice:
																			return

													_lowercase       :	str           =   True
													_lowercase       :	Tuple           =   model_class(config=snake_case    )

													_lowercase       :	str           =   self._prepare_for_class(snake_case      ,	snake_case    )
													_lowercase       :	Optional[int]           =   torch.jit.trace(
													    snake_case      ,	(inputs_dict["input_ids"].to("cpu"    ), inputs_dict["attention_mask"].to("cpu"    ))    )

													with tempfile.TemporaryDirectory() as tmp:
																			torch.jit.save(snake_case      ,	os.path.join(snake_case      ,	"traced_model.pt"    )    )
																			_lowercase       :	Dict           =   torch.jit.load(os.path.join(snake_case      ,	"traced_model.pt"    )      ,	map_location=snake_case    )
																			loaded(inputs_dict["input_ids"].to(snake_case    )      ,	inputs_dict["attention_mask"].to(snake_case    )    )




@require_torch
class   __lowercase						(						unittest.TestCase       ):






	@slow
	def       _a(self				: int    ) ->			str:
							_lowercase       :	Any           =   DistilBertModel.from_pretrained("distilbert-base-uncased"    )
							_lowercase       :	Dict           =   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
							_lowercase       :	List[Any]           =   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
							with torch.no_grad():
													_lowercase       :	Optional[int]           =   model(snake_case      ,	attention_mask=snake_case    )[0]
							_lowercase       :	List[str]           =   torch.Size((1, 11, 768)    )
							self.assertEqual(output.shape      ,	snake_case    )
							_lowercase       :	Any           =   torch.tensor(
							    [[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]    )

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,	snake_case      ,	atol=1e-4    )    )




