
import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, 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 DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
					from transformers.models.dpr.modeling_dpr import (
					    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
					    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
					    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
					)


class 							lowercase   :



		def __init__(					self							, A_							, A_=13							, A_=7							, A_=False							, 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_=3							, A_=4							, A_=None							, A_=0							, )    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	parent
				UpperCamelCase            =	batch_size
				UpperCamelCase            =	seq_length
				UpperCamelCase            =	is_training
				UpperCamelCase            =	use_input_mask
				UpperCamelCase            =	use_token_type_ids
				UpperCamelCase            =	use_labels
				UpperCamelCase            =	vocab_size
				UpperCamelCase            =	hidden_size
				UpperCamelCase            =	num_hidden_layers
				UpperCamelCase            =	num_attention_heads
				UpperCamelCase            =	intermediate_size
				UpperCamelCase            =	hidden_act
				UpperCamelCase            =	hidden_dropout_prob
				UpperCamelCase            =	attention_probs_dropout_prob
				UpperCamelCase            =	max_position_embeddings
				UpperCamelCase            =	type_vocab_size
				UpperCamelCase            =	type_sequence_label_size
				UpperCamelCase            =	initializer_range
				UpperCamelCase            =	num_labels
				UpperCamelCase            =	num_choices
				UpperCamelCase            =	scope
				UpperCamelCase            =	projection_dim



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size			)

				UpperCamelCase            =	None
				if self.use_input_mask:
						UpperCamelCase            =	random_attention_mask([self.batch_size, self.seq_length]			)

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

				UpperCamelCase            =	None
				UpperCamelCase            =	None
				UpperCamelCase            =	None
				if self.use_labels:
						UpperCamelCase            =	ids_tensor([self.batch_size]							, self.type_sequence_label_size			)
						UpperCamelCase            =	ids_tensor([self.batch_size, self.seq_length]							, self.num_labels			)
						UpperCamelCase            =	ids_tensor([self.batch_size]							, self.num_choices			)

				UpperCamelCase            =	self.get_config()

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



		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				return DPRConfig(
				    projection_dim=self.projection_dim							, 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							, initializer_range=self.initializer_range							, )



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_							, A_							, A_							, A_			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	DPRContextEncoder(config=A_			)
				model.to(A_			)
				model.eval()
				UpperCamelCase            =	model(A_							, attention_mask=A_							, token_type_ids=A_			)
				UpperCamelCase            =	model(A_							, token_type_ids=A_			)
				UpperCamelCase            =	model(A_			)
				self.parent.assertEqual(result.pooler_output.shape							, (self.batch_size, self.projection_dim or self.hidden_size)			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_							, A_							, A_							, A_			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	DPRQuestionEncoder(config=A_			)
				model.to(A_			)
				model.eval()
				UpperCamelCase            =	model(A_							, attention_mask=A_							, token_type_ids=A_			)
				UpperCamelCase            =	model(A_							, token_type_ids=A_			)
				UpperCamelCase            =	model(A_			)
				self.parent.assertEqual(result.pooler_output.shape							, (self.batch_size, self.projection_dim or self.hidden_size)			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_							, A_							, A_							, A_			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	DPRReader(config=A_			)
				model.to(A_			)
				model.eval()
				UpperCamelCase            =	model(
				    A_							, attention_mask=A_							, )

				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)			)
				self.parent.assertEqual(result.relevance_logits.shape							, (self.batch_size,)			)



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)      ,    (
				    UpperCamelCase
				)      ,    (
				    UpperCamelCase
				)      ,    (
				    UpperCamelCase
				)      ,    (
				    UpperCamelCase
				)      ,    (
				    UpperCamelCase
				)      ,    (
				    UpperCamelCase
				)      ,    
				)            =	config_and_inputs
				UpperCamelCase            =	{'input_ids': input_ids}
				return config, inputs_dict






@require_torch
class 							lowercase   (						_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  unittest.TestCase  ):
		__lowercase			:			Optional[int]      	=							(
		    (
		        DPRContextEncoder,
		        DPRQuestionEncoder,
		        DPRReader,
		    )
		    if is_torch_available()
		    else ()
		)
		__lowercase			:			List[Any]      	=							{"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}

		__lowercase			:			Optional[Any]      	=							False
		__lowercase			:			Union[str, Any]      	=							False  # why?
		__lowercase			:			Dict      	=							False
		__lowercase			:			Dict      	=							False



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	DPRModelTester(self			)
				UpperCamelCase            =	ConfigTester(self							, config_class=A_							, hidden_size=37			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_context_encoder(*A_			)



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_question_encoder(*A_			)



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_reader(*A_			)



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()[0]

				UpperCamelCase            =	DPRQuestionEncoder(config=A_			)
				model.to(A_			)
				model.eval()

				with tempfile.TemporaryDirectory() as tmp_dirname:
						model.save_pretrained(A_			)
						UpperCamelCase            =	DPRQuestionEncoder.from_pretrained(A_							, projection_dim=512			)

				self.assertIsNotNone(A_			)



		@slow
		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase            =	DPRContextEncoder.from_pretrained(A_			)
						self.assertIsNotNone(A_			)

				for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase            =	DPRContextEncoder.from_pretrained(A_			)
						self.assertIsNotNone(A_			)

				for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase            =	DPRQuestionEncoder.from_pretrained(A_			)
						self.assertIsNotNone(A_			)

				for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase            =	DPRReader.from_pretrained(A_			)
						self.assertIsNotNone(A_			)






@require_torch
class 							lowercase   (						unittest.TestCase  ):



		@slow
		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'							, return_dict=A_			)
				model.to(A_			)

				UpperCamelCase            =	torch.tensor(
				    [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]							, dtype=torch.long							, device=A_			)  # [CLS] hello, is my dog cute? [SEP]
				UpperCamelCase            =	model(A_			)[0]  # embedding shape = (1, 768)
				# compare the actual values for a slice.
				UpperCamelCase            =	torch.tensor(
				    [
				        [
				            0.0323_6253,
				            0.1275_3335,
				            0.1681_8509,
				            0.0027_9786,
				            0.389_6933,
				            0.2426_4945,
				            0.217_8971,
				            -0.0233_5227,
				            -0.0848_1959,
				            -0.1432_4117,
				        ]
				    ]							, dtype=torch.float							, device=A_							, )
				self.assertTrue(torch.allclose(output[:, :10]							, A_							, atol=1e-4			)			)



		@slow
		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base'			)
				UpperCamelCase            =	DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base'			)
				model.to(A_			)

				UpperCamelCase            =	tokenizer(
				    questions='What is love ?'							, titles='Haddaway'							, texts='What Is Love is a song recorded by the artist Haddaway'							, padding=A_							, return_tensors='pt'							, )
				encoded_inputs.to(A_			)

				UpperCamelCase            =	model(**A_			)

				# compare the actual values for a slice.
				UpperCamelCase            =	torch.tensor(
				    [[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]]							, dtype=torch.float							, device=A_							, )

				UpperCamelCase            =	torch.tensor(
				    [[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]]							, dtype=torch.float							, device=A_							, )
				self.assertTrue(torch.allclose(outputs.start_logits[:, :10]							, A_							, atol=1e-4			)			)
				self.assertTrue(torch.allclose(outputs.end_logits[:, :10]							, A_							, atol=1e-4			)			)


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()
