




from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFDebertaForMaskedLM,
	    TFDebertaForQuestionAnswering,
	    TFDebertaForSequenceClassification,
	    TFDebertaForTokenClassification,
	    TFDebertaModel,
	)

class 		lowercase_       :




					def __init__(   self:		Tuple,     _lowercase:		Tuple,     _lowercase:		Union[str, Any]=13,     _lowercase:		str=7,     _lowercase:		Dict=True,     _lowercase:		List[str]=True,     _lowercase:		Tuple=True,     _lowercase:		Optional[Any]=True,     _lowercase:		Optional[int]=99,     _lowercase:		Dict=32,     _lowercase:		Any=2,     _lowercase:		Optional[int]=4,     _lowercase:		List[Any]=37,     _lowercase:		Optional[Any]="gelu",     _lowercase:		Dict=0.1,     _lowercase:		Tuple=0.1,     _lowercase:		Any=512,     _lowercase:		List[Any]=16,     _lowercase:		List[str]=2,     _lowercase:		Any=0.02,     _lowercase:		Any=3,     _lowercase:		List[str]=4,     _lowercase:		List[str]=None,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  13
									__lowerCAmelCase					=  7
									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  99
									__lowerCAmelCase					=  32
									__lowerCAmelCase					=  2
									__lowerCAmelCase					=  4
									__lowerCAmelCase					=  37
									__lowerCAmelCase					=  """gelu"""
									__lowerCAmelCase					=  0.1
									__lowerCAmelCase					=  0.1
									__lowerCAmelCase					=  512
									__lowerCAmelCase					=  16
									__lowerCAmelCase					=  False
									__lowerCAmelCase					=  -1
									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  2
									__lowerCAmelCase					=  0.02
									__lowerCAmelCase					=  3
									__lowerCAmelCase					=  4
									__lowerCAmelCase					=  None




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  ids_tensor([self.batch_size, self.seq_length],     self.vocab_size)

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

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

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

									__lowerCAmelCase					=  DebertaConfig(
									    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,     relative_attention=self.relative_attention,     max_relative_positions=self.max_relative_positions,     position_biased_input=self.position_biased_input,     initializer_range=self.initializer_range,     return_dict=_lowercase,     )

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




					def     _lowercase						(   self:		List[str],     _lowercase:		Any,     _lowercase:		List[Any],     _lowercase:		Dict,     _lowercase:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		List[str],     _lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  TFDebertaModel(config=_lowercase)
									__lowerCAmelCase					=  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

									__lowerCAmelCase					=  [input_ids, input_mask]
									__lowerCAmelCase					=  model(_lowercase)

									__lowerCAmelCase					=  model(_lowercase)

									self.parent.assertEqual(result.last_hidden_state.shape,     (self.batch_size, self.seq_length, self.hidden_size))




					def     _lowercase						(   self:		List[str],     _lowercase:		Any,     _lowercase:		Dict,     _lowercase:		Union[str, Any],     _lowercase:		List[str],     _lowercase:		int,     _lowercase:		Optional[int],     _lowercase:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  TFDebertaForMaskedLM(config=_lowercase)
									__lowerCAmelCase					=  {
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									}
									__lowerCAmelCase					=  model(_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.vocab_size))




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		Optional[int],     _lowercase:		Optional[Any],     _lowercase:		Union[str, Any],     _lowercase:		Tuple,     _lowercase:		List[Any],     _lowercase:		Dict,     _lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  self.num_labels
									__lowerCAmelCase					=  TFDebertaForSequenceClassification(config=_lowercase)
									__lowerCAmelCase					=  {
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									}

									__lowerCAmelCase					=  model(_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels))




					def     _lowercase						(   self:		Tuple,     _lowercase:		int,     _lowercase:		Optional[Any],     _lowercase:		int,     _lowercase:		List[str],     _lowercase:		Dict,     _lowercase:		Dict,     _lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.num_labels
									__lowerCAmelCase					=  TFDebertaForTokenClassification(config=_lowercase)
									__lowerCAmelCase					=  {
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									}
									__lowerCAmelCase					=  model(_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.num_labels))




					def     _lowercase						(   self:		Tuple,     _lowercase:		Dict,     _lowercase:		Optional[Any],     _lowercase:		Tuple,     _lowercase:		Union[str, Any],     _lowercase:		Optional[int],     _lowercase:		List[str],     _lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  TFDebertaForQuestionAnswering(config=_lowercase)
									__lowerCAmelCase					=  {
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									}

									__lowerCAmelCase					=  model(_lowercase)
									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     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.prepare_config_and_inputs()
									(
									    (
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				
									)					=  config_and_inputs
									__lowerCAmelCase					=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
									return config, inputs_dict





@require_tf
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (
					    (
					        TFDebertaModel,
					        TFDebertaForMaskedLM,
					        TFDebertaForQuestionAnswering,
					        TFDebertaForSequenceClassification,
					        TFDebertaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					__UpperCamelCase     	= (
					    {
					        "feature-extraction": TFDebertaModel,
					        "fill-mask": TFDebertaForMaskedLM,
					        "question-answering": TFDebertaForQuestionAnswering,
					        "text-classification": TFDebertaForSequenceClassification,
					        "token-classification": TFDebertaForTokenClassification,
					        "zero-shot": TFDebertaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					__UpperCamelCase     	= False
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  TFDebertaModelTester(self)
									__lowerCAmelCase					=  ConfigTester(self,     config_class=_lowercase,     hidden_size=37)




					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									self.config_tester.run_common_tests()




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_lowercase)




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_lowercase)




					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_lowercase)







					@slow
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""")
									self.assertIsNotNone(_lowercase)





@require_tf
class 		lowercase_       (	unittest.TestCase				):




					@unittest.skip(reason="""Model not available yet""")
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									pass







					@slow
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""")
									__lowerCAmelCase					=  tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
									__lowerCAmelCase					=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
									__lowerCAmelCase					=  model(_lowercase,     attention_mask=_lowercase)[0]

									__lowerCAmelCase					=  tf.constant(
									    [
									        [
									            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
									            [1.4_484_025, -0.93_483_794, -0.80_593_085],
									            [0.3_122_741, 0.00_316_059, -1.4_131_377],
									        ]
									    ])
									tf.debugging.assert_near(output[:, 1:4, 1:4],     _lowercase,     atol=1e-4)













import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCAmelCase_     =			pathlib.Path.cwd().joinpath('project_euler')
lowerCAmelCase_     =			pathlib.Path.cwd().joinpath(
    'scripts', 'project_euler_answers.json'
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
					lowerCAmelCase_     =			json.load(file_handle)







def   snake_case		(				UpperCAmelCase						:							pathlib.Path ):
				A									=   importlib.util.spec_from_file_location(file_path.name,    str(UpperCAmelCase ) )
				A									=   importlib.util.module_from_spec(UpperCAmelCase )  # type: ignore
				spec.loader.exec_module(UpperCAmelCase )  # type: ignore
				return module







def   snake_case		(				):
				A									=   []
				for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
								if problem_dir_path.is_file() or problem_dir_path.name.startswith('_' ):
												continue
								for file_path in problem_dir_path.iterdir():
												if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test') ):
																continue
												solution_file_paths.append(UpperCAmelCase )
				return solution_file_paths







def   snake_case		(				):
				with open(os.environ['GITHUB_EVENT_PATH'] ) as file:
								A									=   json.load(UpperCAmelCase )
				return event["pull_request"]["url"] + "/files"







def   snake_case		(				):
				A									=   []
				A									=   {
				    'Accept': 'application/vnd.github.v3+json',
				    'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
				}
				A									=   requests.get(get_files_url(),    headers=UpperCAmelCase ).json()
				for file in files:
								A									=   pathlib.Path.cwd().joinpath(file['filename'] )
								if (
								    filepath.suffix != ".py"
								    or filepath.name.startswith(('_', 'test') )
								    or not filepath.name.startswith('sol' )
								):
												continue
								solution_file_paths.append(UpperCAmelCase )
				return solution_file_paths







def   snake_case		(				):
				if os.environ.get('CI' ) and os.environ.get('GITHUB_EVENT_NAME' ) == "pull_request":
								# Return only if there are any, otherwise default to all solutions
								if filepaths := added_solution_file_path():
												return filepaths
				return all_solution_file_paths()







@pytest.mark.parametrize(
    'solution_path',    collect_solution_file_paths(),    ids=lambda UpperCAmelCase : f'{path.parent.name}/{path.name}',    )
def   snake_case		(				UpperCAmelCase						:							pathlib.Path ):
				A									=   solution_path.parent.name[8:].zfill(3 )
				A									=   PROBLEM_ANSWERS[problem_number]
				A									=   convert_path_to_module(UpperCAmelCase )
				A									=   str(solution_module.solution() )  # type: ignore
				A									=   hashlib.shaaaa(answer.encode() ).hexdigest()
				assert (
				    answer == expected
				), f'Expected solution to {problem_number} to have hash {expected}, got {answer}'





