





'''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   UpperCAmelCase      :
 def __init__(							self :str		,			lowercase_ :Any		,			lowercase_ :Optional[Any]=13		,			lowercase_ :List[str]=7		,			lowercase_ :int=True		,			lowercase_ :int=True		,			lowercase_ :Optional[int]=False		,			lowercase_ :str=True		,			lowercase_ :List[str]=99		,			lowercase_ :List[Any]=32		,			lowercase_ :List[str]=5		,			lowercase_ :Tuple=4		,			lowercase_ :Tuple=37		,			lowercase_ :str="gelu"		,			lowercase_ :str=0.1		,			lowercase_ :Union[str, Any]=0.1		,			lowercase_ :Any=5_12		,			lowercase_ :List[Any]=16		,			lowercase_ :Any=2		,			lowercase_ :Tuple=0.0_2		,			lowercase_ :Tuple=3		,			lowercase_ :Dict=4		,			lowercase_ :Optional[int]=None		,			)->				List[str]:
     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 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[int]:
     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 	UpperCAmelCase_				(							self :List[str]				)->				Any:
     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=lowerCamelCase_		,			initializer_range=self.initializer_range		,			)
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Any		,			lowercase_ :Tuple		,			lowercase_ :int		,			lowercase_ :List[str]		,			lowercase_ :List[str]		,			lowercase_ :Optional[int]		,			lowercase_ :str				)->				Dict:
     A__							=     BioGptModel(config=lowerCamelCase_				)
     model.to(lowerCamelCase_				)
     model.eval()
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_				)
     A__							=     model(lowerCamelCase_				)
     self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)				)
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Union[str, Any]		,			lowercase_ :str		,			lowercase_ :str		,			lowercase_ :Union[str, Any]		,			lowercase_ :Optional[Any]		,			lowercase_ :List[Any]		,			lowercase_ :Tuple		,			lowercase_ :Union[str, Any]		,			lowercase_ :List[Any]		,			)->				Optional[Any]:
     A__							=     BioGptForCausalLM(config=lowerCamelCase_				)
     model.to(lowerCamelCase_				)
     model.eval()
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			token_type_ids=lowerCamelCase_		,			labels=lowerCamelCase_				)
     self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Dict		,			lowercase_ :Dict		,			lowercase_ :Optional[int]		,			lowercase_ :Any		,			lowercase_ :Any		,			*lowercase_ :Tuple				)->				str:
     A__							=     BioGptModel(config=lowerCamelCase_				)
     model.to(lowerCamelCase_				)
     model.eval()

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

     # first forward pass
     A__,      A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_				).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,)		,			lowerCamelCase_				).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=lowerCamelCase_				)]		,			dim=1		,			)

     # get two different outputs
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_				)["last_hidden_state"]
     A__							=     model(lowerCamelCase_		,			past_key_values=lowerCamelCase_		,			attention_mask=lowerCamelCase_				)["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(lowerCamelCase_		,			lowerCamelCase_		,			atol=1E-3				)				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Dict		,			lowercase_ :List[Any]		,			lowercase_ :int		,			lowercase_ :Tuple		,			lowercase_ :Optional[Any]		,			*lowercase_ :int				)->				Tuple:
     A__							=     BioGptModel(config=lowerCamelCase_				).to(lowerCamelCase_				).eval()

     A__							=     torch.ones(input_ids.shape		,			dtype=torch.long		,			device=lowerCamelCase_				)

     # first forward pass
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			use_cache=lowerCamelCase_				)

     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(lowerCamelCase_		,			attention_mask=lowerCamelCase_				)["last_hidden_state"]
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			past_key_values=lowerCamelCase_				)[
         "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(lowerCamelCase_		,			lowerCamelCase_		,			atol=1E-3				)				)
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :Dict		,			lowercase_ :Dict		,			lowercase_ :str		,			lowercase_ :Tuple		,			lowercase_ :Dict		,			*lowercase_ :Tuple		,			lowercase_ :str=False				)->				List[Any]:
     A__							=     BioGptForCausalLM(lowerCamelCase_				)
     model.to(lowerCamelCase_				)
     if gradient_checkpointing:
         model.gradient_checkpointing_enable()

     A__							=     model(lowerCamelCase_		,			labels=lowerCamelCase_				)
     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 	UpperCAmelCase_				(							self :int		,			lowercase_ :Any		,			*lowercase_ :List[Any]				)->				Optional[Any]:
     A__							=     BioGptModel(lowerCamelCase_				)
     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_0_1				)
             self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]				) - 0.0				)		,			0.0_1				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Union[str, Any]		,			lowercase_ :List[Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :str		,			lowercase_ :Union[str, Any]		,			*lowercase_ :str				)->				str:
     A__							=     self.num_labels
     A__							=     BioGptForTokenClassification(lowerCamelCase_				)
     model.to(lowerCamelCase_				)
     model.eval()
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			token_type_ids=lowerCamelCase_				)
     self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)				)



 def 	UpperCAmelCase_				(							self :Dict				)->				str:
     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   UpperCAmelCase      (			a__     ,			a__     ,			a__     ,			unittest.TestCase							):
 __lowercase  						=							(
     (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
     if is_torch_available()
     else ()
 )
 __lowercase  						=							(BioGptForCausalLM,) if is_torch_available() else ()
 __lowercase  						=							(
     {
         "feature-extraction": BioGptModel,
         "text-classification": BioGptForSequenceClassification,
         "text-generation": BioGptForCausalLM,
         "token-classification": BioGptForTokenClassification,
         "zero-shot": BioGptForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase  						=							False
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Tuple:
     A__							=     BioGptModelTester(self				)
     A__							=     ConfigTester(self		,			config_class=lowerCamelCase_		,			hidden_size=37				)
 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     self.config_tester.run_common_tests()
 def 	UpperCAmelCase_				(							self :int				)->				Union[str, Any]:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCamelCase_				)
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[int]:
     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(*lowerCamelCase_				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase_				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase_		,			gradient_checkpointing=lowerCamelCase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				str:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				str:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase_				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Any:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase_				)
 @slow
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Union[str, Any]:
     A__							=     BioGptForCausalLM.from_pretrained("microsoft/biogpt"				)
     model.to(lowerCamelCase_				)
     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(lowerCamelCase_		,			return_tensors="pt"		,			padding=lowerCamelCase_				)
     A__							=     inputs["input_ids"].to(lowerCamelCase_				)

     A__							=     model.generate(
         input_ids=lowerCamelCase_		,			attention_mask=inputs["attention_mask"].to(lowerCamelCase_				)		,			)

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

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

     A__							=     tokenizer.batch_decode(lowerCamelCase_		,			skip_special_tokens=lowerCamelCase_				)
     A__							=     tokenizer.decode(output_non_padded[0]		,			skip_special_tokens=lowerCamelCase_				)
     A__							=     tokenizer.decode(output_padded[0]		,			skip_special_tokens=lowerCamelCase_				)

     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(lowerCamelCase_		,			lowerCamelCase_				)
     self.assertListEqual(lowerCamelCase_		,			[non_padded_sentence, padded_sentence]				)
 @slow
 def 	UpperCAmelCase_				(							self :str				)->				Tuple:
     for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__							=     BioGptModel.from_pretrained(lowerCamelCase_				)
         self.assertIsNotNone(lowerCamelCase_				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__,      A__							=     self.model_tester.prepare_config_and_inputs_for_common()
     A__							=     3
     A__							=     input_dict["input_ids"]
     A__							=     input_ids.ne(1				).to(lowerCamelCase_				)
     A__							=     ids_tensor([self.model_tester.batch_size]		,			self.model_tester.type_sequence_label_size				)
     A__							=     BioGptForSequenceClassification(lowerCamelCase_				)
     model.to(lowerCamelCase_				)
     model.eval()
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			labels=lowerCamelCase_				)
     self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels)				)



 def 	UpperCAmelCase_				(							self :int				)->				Union[str, Any]:
     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(lowerCamelCase_				)
     A__							=     ids_tensor(
         [self.model_tester.batch_size, config.num_labels]		,			self.model_tester.type_sequence_label_size				).to(torch.float				)
     A__							=     BioGptForSequenceClassification(lowerCamelCase_				)
     model.to(lowerCamelCase_				)
     model.eval()
     A__							=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			labels=lowerCamelCase_				)
     self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels)				)






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 @slow
 def 	UpperCAmelCase_				(							self :Any				)->				Optional[Any]:
     A__							=     BioGptForCausalLM.from_pretrained("microsoft/biogpt"				)
     A__							=     torch.tensor([[2, 48_05, 9, 6_56, 21]]				)
     A__							=     model(lowerCamelCase_				)[0]

     A__							=     4_23_84

     A__							=     torch.Size((1, 5, vocab_size)				)
     self.assertEqual(output.shape		,			lowerCamelCase_				)

     A__							=     torch.tensor(
         [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]]				)

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



 @slow
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Any:
     A__							=     BioGptTokenizer.from_pretrained("microsoft/biogpt"				)
     A__							=     BioGptForCausalLM.from_pretrained("microsoft/biogpt"				)
     model.to(lowerCamelCase_				)

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

     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(lowerCamelCase_		,			lowerCamelCase_				)







'''simple docstring'''






from __future__ import annotations

__UpperCAmelCase       							=				list[list[int]]

# assigning initial values to the grid
__UpperCAmelCase       							=				[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
__UpperCAmelCase       							=				[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]


def    _snake_case  (	A					,       A					,       A					,       A			)		->       bool:
							for i in range(9			):
														if grid[row][i] == n or grid[i][column] == n:
																					return False

							for i in range(3			):
														for j in range(3			):
																					if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																												return False

							return True


def    _snake_case  (	A			)		->       tuple[int, int] | None:
							for i in range(9			):
														for j in range(9			):
																					if grid[i][j] == 0:
																												return i, j
							return None


def    _snake_case  (	A			)		->       Matrix | None:
							if location := find_empty_location(A			):
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      location
							else:
														# If the location is ``None``, then the grid is solved.
														return grid

							for digit in range(1					,       10			):
														if is_safe(A					,       A					,       A					,       A			):
																					lowerCAmelCase__			  =      digit

																					if sudoku(A			) is not None:
																												return grid

																					lowerCAmelCase__			  =      0

							return None


def    _snake_case  (	A			)		->       None:
							for row in grid:
														for cell in row:
																					print(A					,       end=''' '''			)
														print()


if __name__ == "__main__":
		# make a copy of grid so that you can compare with the unmodified grid
		for example_grid in (initial_grid, no_solution):
				print('''\nExample grid:\n''' + '''=''' * 20)
				print_solution(example_grid)
				print('''\nExample grid solution:''')
				__UpperCAmelCase       							=				sudoku(example_grid)
				if solution is not None:
						print_solution(solution)
				else:
						print('''Cannot find a solution.''')