



'''simple docstring'''
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   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	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=5_12					,   A=16					,   A=2					,   A=0.02					,   A=3					,   A=4					,   A=None					,   A=0					,   ) ->							int:
				A:      Optional[Any]														=   parent
				A:      str														=   batch_size
				A:      int														=   seq_length
				A:      Union[str, Any]														=   is_training
				A:      int														=   use_input_mask
				A:      Optional[int]														=   use_token_type_ids
				A:      List[Any]														=   use_labels
				A:      List[str]														=   vocab_size
				A:      Union[str, Any]														=   hidden_size
				A:      Optional[int]														=   num_hidden_layers
				A:      List[str]														=   num_attention_heads
				A:      List[str]														=   intermediate_size
				A:      List[str]														=   hidden_act
				A:      Any														=   hidden_dropout_prob
				A:      str														=   attention_probs_dropout_prob
				A:      Dict														=   max_position_embeddings
				A:      Optional[int]														=   type_vocab_size
				A:      List[Any]														=   type_sequence_label_size
				A:      Optional[int]														=   initializer_range
				A:      List[Any]														=   num_labels
				A:      Any														=   num_choices
				A:      Union[str, Any]														=   scope
				A:      Optional[Any]														=   projection_dim


	def      a__ (		self			) ->							List[Any]:
				A:      int														=   ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size			)

				A:      List[Any]														=   None
				if self.use_input_mask:
							A:      int														=   random_attention_mask([self.batch_size, self.seq_length]			)

				A:      int														=   None
				if self.use_token_type_ids:
							A:      Union[str, Any]														=   ids_tensor([self.batch_size, self.seq_length]					,   self.type_vocab_size			)

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

				A:      Union[str, Any]														=   self.get_config()

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


	def      a__ (		self			) ->							Optional[Any]:
				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      a__ (		self					,   A					,   A					,   A					,   A					,   A					,   A					,   A			) ->							Any:
				A:      Optional[int]														=   DPRContextEncoder(config=A			)
				model.to(A			)
				model.eval()
				A:      Dict														=   model(A					,   attention_mask=A					,   token_type_ids=A			)
				A:      List[Any]														=   model(A					,   token_type_ids=A			)
				A:      Dict														=   model(A			)
				self.parent.assertEqual(result.pooler_output.shape					,   (self.batch_size, self.projection_dim or self.hidden_size)			)


	def      a__ (		self					,   A					,   A					,   A					,   A					,   A					,   A					,   A			) ->							int:
				A:      str														=   DPRQuestionEncoder(config=A			)
				model.to(A			)
				model.eval()
				A:      str														=   model(A					,   attention_mask=A					,   token_type_ids=A			)
				A:      Dict														=   model(A					,   token_type_ids=A			)
				A:      List[str]														=   model(A			)
				self.parent.assertEqual(result.pooler_output.shape					,   (self.batch_size, self.projection_dim or self.hidden_size)			)


	def      a__ (		self					,   A					,   A					,   A					,   A					,   A					,   A					,   A			) ->							Optional[Any]:
				A:      List[Any]														=   DPRReader(config=A			)
				model.to(A			)
				model.eval()
				A:      Union[str, Any]														=   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      a__ (		self			) ->							Optional[int]:
				A:      List[Any]														=   self.prepare_config_and_inputs()
				(
				    (
				    A
				)							,		(
				    A
				)							,		(
				    A
				)							,		(
				    A
				)							,		(
				    A
				)							,		(
				    A
				)							,		(
				    A
				)							,		
				):      Any														=   config_and_inputs
				A:      Optional[int]														=   {"""input_ids""": input_ids}
				return config, inputs_dict







@require_torch
class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_     ,			unittest.TestCase					):
	"""simple docstring"""


	A__    :    Union[str, Any]						=				(
	    (
	        DPRContextEncoder,
	        DPRQuestionEncoder,
	        DPRReader,
	    )
	    if is_torch_available()
	    else ()
	)
	A__    :    Optional[Any]						=				{'''feature-extraction''': DPRQuestionEncoder} if is_torch_available() else {}

	A__    :    Optional[int]						=				False
	A__    :    Tuple						=				False  # why?
	A__    :    str						=				False
	A__    :    Any						=				False


	def      a__ (		self			) ->							Union[str, Any]:
				A:      Optional[int]														=   DPRModelTester(self			)
				A:      Optional[Any]														=   ConfigTester(self					,   config_class=A					,   hidden_size=37			)


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


	def      a__ (		self			) ->							Any:
				A:      Tuple														=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_context_encoder(*A			)


	def      a__ (		self			) ->							Union[str, Any]:
				A:      Optional[int]														=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_question_encoder(*A			)


	def      a__ (		self			) ->							int:
				A:      List[str]														=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_reader(*A			)


	def      a__ (		self			) ->							Optional[int]:
				A:      int														=   self.model_tester.prepare_config_and_inputs()[0]

				A:      Dict														=   DPRQuestionEncoder(config=A			)
				model.to(A			)
				model.eval()

				with tempfile.TemporaryDirectory() as tmp_dirname:
							model.save_pretrained(A			)
							A:      Dict														=   DPRQuestionEncoder.from_pretrained(A					,   projection_dim=5_12			)

				self.assertIsNotNone(A			)





	@slow
	def      a__ (		self			) ->							Any:
				for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A:      Optional[int]														=   DPRContextEncoder.from_pretrained(A			)
							self.assertIsNotNone(A			)

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

				for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A:      List[str]														=   DPRQuestionEncoder.from_pretrained(A			)
							self.assertIsNotNone(A			)

				for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A:      List[Any]														=   DPRReader.from_pretrained(A			)
							self.assertIsNotNone(A			)







@require_torch
class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	@slow
	def      a__ (		self			) ->							Dict:
				A:      Dict														=   DPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""					,   return_dict=A			)
				model.to(A			)

				A:      Optional[Any]														=   torch.tensor(
				    [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]					,   dtype=torch.long					,   device=A			)  # [CLS] hello, is my dog cute? [SEP]
				A:      str														=   model(A			)[0]  # embedding shape = (1, 768)
				# compare the actual values for a slice.
				A:      Union[str, Any]														=   torch.tensor(
				    [
				        [
				            0.03236253,
				            0.12753335,
				            0.16818509,
				            0.00279786,
				            0.3896933,
				            0.24264945,
				            0.2178971,
				            -0.02335227,
				            -0.08481959,
				            -0.14324117,
				        ]
				    ]					,   dtype=torch.float					,   device=A					,   )
				self.assertTrue(torch.allclose(output[:, :10]					,   A					,   atol=1e-4			)			)





	@slow
	def      a__ (		self			) ->							Optional[int]:
				A:      int														=   DPRReaderTokenizer.from_pretrained("""facebook/dpr-reader-single-nq-base"""			)
				A:      Union[str, Any]														=   DPRReader.from_pretrained("""facebook/dpr-reader-single-nq-base"""			)
				model.to(A			)

				A:      Dict														=   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			)

				A:      Optional[Any]														=   model(**A			)

				# compare the actual values for a slice.
				A:      Dict														=   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					,   )

				A:      Optional[int]														=   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			)			)










'''simple docstring'''
from __future__ import annotations



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			A:      int														=   [0] * len(lowerCamelCase__				)
			for i in range(1							,							len(lowerCamelCase__				)				):
						A:      Dict														=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]							,							lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(lowerCamelCase__				)
			]



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)


if __name__ == "__main__":
					# process id's
					__SCREAMING_SNAKE_CASE :      Optional[Any]             =[1, 2, 3]

					# ensure that we actually have processes
					if len(processes) == 0:
										print('Zero amount of processes')
										raise SystemExit(0)

					# duration time of all processes
					__SCREAMING_SNAKE_CASE :      Dict             =[19, 8, 9]

					# ensure we can match each id to a duration time
					if len(duration_times) != len(processes):
										print('Unable to match all id\'s with their duration time')
										raise SystemExit(0)

					# get the waiting times and the turnaround times
					__SCREAMING_SNAKE_CASE :      Tuple             =calculate_waiting_times(duration_times)
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_turnaround_times(duration_times, waiting_times)

					# get the average times
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_average_waiting_time(waiting_times)
					__SCREAMING_SNAKE_CASE :      Union[str, Any]             =calculate_average_turnaround_time(turnaround_times)

					# print all the results
					print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
					for i, process in enumerate(processes):
										print(
										    f"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
										    f"""{turnaround_times[i]}"""
										)
					print(f"""Average waiting time = {average_waiting_time}""")
					print(f"""Average turn around time = {average_turnaround_time}""")





