


'''simple docstring'''




from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCAmelCase_      :		Any          =		Lock()


def    UpperCAmelCase_  (       A		,	A		,	A		,	A		,	A		,	A		,	A      ):
   '''simple docstring'''





   global process_lock

   # we perform n swaps since after n swaps we know we are sorted
   # we *could* stop early if we are sorted already, but it takes as long to
   # find out we are sorted as it does to sort the list with this algorithm
   for i in range(0		,	1_0      ):
      if (i + position) % 2 == 0 and r_send is not None:
         # send your value to your right neighbor
         process_lock.acquire()
         r_send[1].send(A__      )
         process_lock.release()

         # receive your right neighbor's value
         process_lock.acquire()
         _a					:  Optional[int]						=  rr_cv[0].recv()
         process_lock.release()

         # take the lower value since you are on the left
         _a					:  List[Any]						=  min(A__		,	A__      )
      elif (i + position) % 2 != 0 and l_send is not None:
         # send your value to your left neighbor
         process_lock.acquire()
         l_send[1].send(A__      )
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         _a					:  Tuple						=  lr_cv[0].recv()
         process_lock.release()

         # take the higher value since you are on the right
         _a					:  Dict						=  max(A__		,	A__      )
    # after all swaps are performed, send the values back to main
   result_pipe[1].send(A__      )


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  List[Any]						=  []
   _a					:  List[str]						=  []
   # initialize the list of pipes where the values will be retrieved
   for _ in arr:
      result_pipe.append(Pipe()      )
   # creates the processes
   # the first and last process only have one neighbor so they are made outside
   # of the loop
   _a					:  List[Any]						=  Pipe()
   _a					:  Union[str, Any]						=  Pipe()
   process_array_.append(
       Process(
           target=A__		,	args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])		,	)      )
   _a					:  Optional[Any]						=  temp_rs
   _a					:  str						=  temp_rr

   for i in range(1		,	len(A__      ) - 1      ):
      _a					:  Any						=  Pipe()
      _a					:  Union[str, Any]						=  Pipe()
      process_array_.append(
          Process(
              target=A__		,	args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])		,	)      )
      _a					:  Any						=  temp_rs
      _a					:  Dict						=  temp_rr

   process_array_.append(
       Process(
           target=A__		,	args=(
               len(A__      ) - 1,
               arr[len(A__      ) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(A__      ) - 1],
           )		,	)      )

   # start the processes
   for p in process_array_:
      p.start()

   # wait for the processes to end and write their values to the list
   for p in range(0		,	len(A__      )      ):
      _a					:  Dict						=  result_pipe[p][0].recv()
      process_array_[p].join()
   return arr


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  list(range(1_0		,	0		,	-1      )      )
   print('Initial List'      )
   print(*A__      )
   _a					:  Tuple						=  odd_even_transposition(A__      )
   print('Sorted List\n'      )
   print(*A__      )


if __name__ == "__main__":
 main()




'''simple docstring'''
import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptTokenizer,
		)
		from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class   a_ :




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase=13					,   UpperCAmelCase=7					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   UpperCAmelCase=False					,   UpperCAmelCase=True					,   UpperCAmelCase=99					,   UpperCAmelCase=32					,   UpperCAmelCase=5					,   UpperCAmelCase=4					,   UpperCAmelCase=37					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=0.1					,   UpperCAmelCase=0.1					,   UpperCAmelCase=5_12					,   UpperCAmelCase=16					,   UpperCAmelCase=2					,   UpperCAmelCase=0.02					,   UpperCAmelCase=3					,   UpperCAmelCase=4					,   UpperCAmelCase=None					,   ):
				a_      				=						parent
				a_      				=						batch_size
				a_      				=						seq_length
				a_      				=						is_training
				a_      				=						use_input_mask
				a_      				=						use_token_type_ids
				a_      				=						use_labels
				a_      				=						vocab_size
				a_      				=						hidden_size
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						intermediate_size
				a_      				=						hidden_act
				a_      				=						hidden_dropout_prob
				a_      				=						attention_probs_dropout_prob
				a_      				=						max_position_embeddings
				a_      				=						type_vocab_size
				a_      				=						type_sequence_label_size
				a_      				=						initializer_range
				a_      				=						num_labels
				a_      				=						num_choices
				a_      				=						scope




			def 					lowerCAmelCase__					(       self	):
				a_      				=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

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

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

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

				a_      				=						self.get_config()

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




			def 					lowerCAmelCase__					(       self	):
				return BioGptConfig(
				    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=UpperCAmelCase					,   initializer_range=self.initializer_range					,   )




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						BioGptModel(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase	)
				self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   ):
				a_      				=						BioGptForCausalLM(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   token_type_ids=UpperCAmelCase					,   labels=UpperCAmelCase	)
				self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						BioGptModel(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()

				# create attention mask
				a_      				=						torch.ones(input_ids.shape					,   dtype=torch.long					,   device=UpperCAmelCase	)
				a_      				=						self.seq_length // 2
				a_      				=						0

				# first forward pass
				a_				,  a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	).to_tuple()

				# create hypothetical next token and extent to next_input_ids
				a_      				=						ids_tensor((self.batch_size, 1)					,   config.vocab_size	)

				# change a random masked slice from input_ids
				a_      				=						ids_tensor((1,)					,   UpperCAmelCase	).item() + 1
				a_      				=						ids_tensor((self.batch_size, 1)					,   config.vocab_size	).squeeze(-1	)
				a_      				=						random_other_next_tokens

				# append to next input_ids and attn_mask
				a_      				=						torch.cat([input_ids, next_tokens]					,   dim=-1	)
				a_      				=						torch.cat(
				    [attn_mask, torch.ones((attn_mask.shape[0], 1)					,   dtype=torch.long					,   device=UpperCAmelCase	)]					,   dim=1					,   )

				# get two different outputs
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)["""last_hidden_state"""]
				a_      				=						model(UpperCAmelCase					,   past_key_values=UpperCAmelCase					,   attention_mask=UpperCAmelCase	)["""last_hidden_state"""]

				# select random slice
				a_      				=						ids_tensor((1,)					,   output_from_past.shape[-1]	).item()
				a_      				=						output_from_no_past[:, -1, random_slice_idx].detach()
				a_      				=						output_from_past[:, 0, random_slice_idx].detach()

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-3	)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						BioGptModel(config=UpperCAmelCase	).to(UpperCAmelCase	).eval()

				a_      				=						torch.ones(input_ids.shape					,   dtype=torch.long					,   device=UpperCAmelCase	)

				# first forward pass
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   use_cache=UpperCAmelCase	)

				a_				,  a_      				=						outputs.to_tuple()

				# create hypothetical multiple next token and extent to next_input_ids
				a_      				=						ids_tensor((self.batch_size, 3)					,   config.vocab_size	)
				a_      				=						ids_tensor((self.batch_size, 3)					,   2	)

				# append to next input_ids and
				a_      				=						torch.cat([input_ids, next_tokens]					,   dim=-1	)
				a_      				=						torch.cat([attention_mask, next_attn_mask]					,   dim=-1	)

				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)["""last_hidden_state"""]
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   past_key_values=UpperCAmelCase	)[
				    """last_hidden_state"""
				]

				# select random slice
				a_      				=						ids_tensor((1,)					,   output_from_past.shape[-1]	).item()
				a_      				=						output_from_no_past[:, -3:, random_slice_idx].detach()
				a_      				=						output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-3	)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase					,   UpperCAmelCase=False	):
				a_      				=						BioGptForCausalLM(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				if gradient_checkpointing:
					model.gradient_checkpointing_enable()

				a_      				=						model(UpperCAmelCase					,   labels=UpperCAmelCase	)
				self.parent.assertEqual(result.loss.shape					,   ()	)
				self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)	)
				result.loss.backward()




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						BioGptModel(UpperCAmelCase	)
				a_      				=						model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers	)
				for key in model.state_dict().keys():
					if "c_proj" in key and "weight" in key:
						self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]	) - model_std	)					,   0.0_01	)
						self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]	) - 0.0	)					,   0.01	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						self.num_labels
				a_      				=						BioGptForTokenClassification(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   token_type_ids=UpperCAmelCase	)
				self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.num_labels)	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.prepare_config_and_inputs()
				(
				    (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  
				)      				=						config_and_inputs
				a_      				=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
				return config, inputs_dict







@require_torch
class   a_ (      UpperCamelCase__		,  UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Optional[int]       =    (
			    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__	:			Dict       =    (BioGptForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__	:			int       =    (
			    {
			        'feature-extraction': BioGptModel,
			        'text-classification': BioGptForSequenceClassification,
			        'text-generation': BioGptForCausalLM,
			        'token-classification': BioGptForTokenClassification,
			        'zero-shot': BioGptForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__	:			Union[str, Any]       =    False




			def 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase					,   hidden_size=37	)




			def 					lowerCAmelCase__					(       self	):
				self.config_tester.run_common_tests()




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					a_      				=						type
					self.model_tester.create_and_check_model(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_forward_and_backwards(*UpperCAmelCase					,   gradient_checkpointing=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCAmelCase	)




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""	)
				model.to(UpperCAmelCase	)
				a_      				=						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""	)

				a_      				=						"""left"""

				# Define PAD Token = EOS Token = 50256
				a_      				=						tokenizer.eos_token
				a_      				=						model.config.eos_token_id

				# use different length sentences to test batching
				a_      				=						[
				    """Hello, my dog is a little""",
				    """Today, I""",
				]

				a_      				=						tokenizer(UpperCAmelCase					,   return_tensors="""pt"""					,   padding=UpperCAmelCase	)
				a_      				=						inputs["""input_ids"""].to(UpperCAmelCase	)

				a_      				=						model.generate(
				    input_ids=UpperCAmelCase					,   attention_mask=inputs["""attention_mask"""].to(UpperCAmelCase	)					,   )

				a_      				=						tokenizer(sentences[0]					,   return_tensors="""pt"""	).input_ids.to(UpperCAmelCase	)
				a_      				=						model.generate(input_ids=UpperCAmelCase	)

				a_      				=						inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
				a_      				=						tokenizer(sentences[1]					,   return_tensors="""pt"""	).input_ids.to(UpperCAmelCase	)
				a_      				=						model.generate(input_ids=UpperCAmelCase					,   max_length=model.config.max_length - num_paddings	)

				a_      				=						tokenizer.batch_decode(UpperCAmelCase					,   skip_special_tokens=UpperCAmelCase	)
				a_      				=						tokenizer.decode(output_non_padded[0]					,   skip_special_tokens=UpperCAmelCase	)
				a_      				=						tokenizer.decode(output_padded[0]					,   skip_special_tokens=UpperCAmelCase	)

				a_      				=						[
				    """Hello, my dog is a little bit bigger than a little bit.""",
				    """Today, I have a good idea of how to use the information""",
				]
				self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)
				self.assertListEqual(UpperCAmelCase					,   [non_padded_sentence, padded_sentence]	)




			@slow
			def 					lowerCAmelCase__					(       self	):
				for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					a_      				=						BioGptModel.from_pretrained(UpperCAmelCase	)
					self.assertIsNotNone(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				a_      				=						3
				a_      				=						input_dict["""input_ids"""]
				a_      				=						input_ids.ne(1	).to(UpperCAmelCase	)
				a_      				=						ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size	)
				a_      				=						BioGptForSequenceClassification(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   labels=UpperCAmelCase	)
				self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)	)






			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				a_      				=						3
				a_      				=						"""multi_label_classification"""
				a_      				=						input_dict["""input_ids"""]
				a_      				=						input_ids.ne(1	).to(UpperCAmelCase	)
				a_      				=						ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]					,   self.model_tester.type_sequence_label_size	).to(torch.float	)
				a_      				=						BioGptForSequenceClassification(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   labels=UpperCAmelCase	)
				self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)	)







@require_torch
class   a_ (      unittest.TestCase					):




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""	)
				a_      				=						torch.tensor([[2, 48_05, 9, 6_56, 21]]	)
				a_      				=						model(UpperCAmelCase	)[0]

				a_      				=						4_23_84

				a_      				=						torch.Size((1, 5, vocab_size)	)
				self.assertEqual(output.shape					,   UpperCAmelCase	)

				a_      				=						torch.tensor(
				    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]	)

				self.assertTrue(torch.allclose(output[:, :3, :3]					,   UpperCAmelCase					,   atol=1e-4	)	)






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""	)
				a_      				=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""	)
				model.to(UpperCAmelCase	)

				torch.manual_seed(0	)
				a_      				=						tokenizer("""COVID-19 is"""					,   return_tensors="""pt"""	).to(UpperCAmelCase	)
				a_      				=						model.generate(
				    **UpperCAmelCase					,   min_length=1_00					,   max_length=10_24					,   num_beams=5					,   early_stopping=UpperCAmelCase					,   )
				a_      				=						tokenizer.decode(output_ids[0]					,   skip_special_tokens=UpperCAmelCase	)

				a_      				=						(
				    """COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
				    """ causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
				    """ territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
				    """ and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
				    """ more than 800,000 deaths."""
				)
				self.assertEqual(UpperCAmelCase					,   UpperCAmelCase	)
