
import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
			import torch

			from transformers import (
			    MraForMaskedLM,
			    MraForMultipleChoice,
			    MraForQuestionAnswering,
			    MraForSequenceClassification,
			    MraForTokenClassification,
			    MraModel,
			)
			from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=2				,				_UpperCamelCase=8				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=99				,				_UpperCamelCase=16				,				_UpperCamelCase=5				,				_UpperCamelCase=2				,				_UpperCamelCase=36				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.0				,				_UpperCamelCase=512				,				_UpperCamelCase=16				,				_UpperCamelCase=2				,				_UpperCamelCase=0.02				,				_UpperCamelCase=3				,				_UpperCamelCase=4				,				_UpperCamelCase=None				,				)				->      List[Any]:
							lowerCAmelCase_						=	parent
							lowerCAmelCase_						=	batch_size
							lowerCAmelCase_						=	seq_length
							lowerCAmelCase_						=	is_training
							lowerCAmelCase_						=	use_input_mask
							lowerCAmelCase_						=	use_token_type_ids
							lowerCAmelCase_						=	use_labels
							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	type_vocab_size
							lowerCAmelCase_						=	type_sequence_label_size
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	num_labels
							lowerCAmelCase_						=	num_choices
							lowerCAmelCase_						=	scope




				def      __a						(					self     )				->      Tuple:
							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_						=	ids_tensor([self.batch_size]				,				self.num_choices     )

							lowerCAmelCase_						=	self.get_config()

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




				def      __a						(					self     )				->      List[Any]:
							return MraConfig(
							    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      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	self.get_config()
							lowerCAmelCase_						=	300
							return config




				def      __a						(					self     )				->      Optional[int]:
							(
							    (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  
							)						=	self.prepare_config_and_inputs()

							lowerCAmelCase_						=	True
							lowerCAmelCase_						=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
							lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length]				,				vocab_size=2     )

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	MraModel(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase				,				token_type_ids=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase     )
							self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				)				->      Dict:
							lowerCAmelCase_						=	True
							lowerCAmelCase_						=	MraModel(_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				encoder_hidden_states=_UpperCamelCase				,				encoder_attention_mask=_UpperCamelCase				,				)
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				encoder_hidden_states=_UpperCamelCase				,				)
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase     )
							self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[Any]:
							lowerCAmelCase_						=	MraForMaskedLM(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	MraForQuestionAnswering(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				start_positions=_UpperCamelCase				,				end_positions=_UpperCamelCase				,				)
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	self.num_labels
							lowerCAmelCase_						=	MraForSequenceClassification(_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase     )
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.num_labels
							lowerCAmelCase_						=	MraForTokenClassification(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase     )
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Tuple:
							lowerCAmelCase_						=	self.num_choices
							lowerCAmelCase_						=	MraForMultipleChoice(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	input_ids.unsqueeze(1     ).expand(-1				,				self.num_choices				,				-1     ).contiguous()
							lowerCAmelCase_						=	token_type_ids.unsqueeze(1     ).expand(-1				,				self.num_choices				,				-1     ).contiguous()
							lowerCAmelCase_						=	input_mask.unsqueeze(1     ).expand(-1				,				self.num_choices				,				-1     ).contiguous()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase				,				)
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_choices)     )




				def      __a						(					self     )				->      int:
							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_torch
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=(
				    (
				        MraModel,
				        MraForMaskedLM,
				        MraForMultipleChoice,
				        MraForQuestionAnswering,
				        MraForSequenceClassification,
				        MraForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False

				_lowercase					=()




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	MraModelTester(self     )
							lowerCAmelCase_						=	ConfigTester(self				,				config_class=_UpperCamelCase				,				hidden_size=37     )




				def      __a						(					self     )				->      List[Any]:
							self.config_tester.run_common_tests()




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase     )




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase_						=	type
										self.model_tester.create_and_check_model(*_UpperCamelCase     )




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase     )




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase     )




				@slow
				def      __a						(					self     )				->      List[Any]:
							for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_						=	MraModel.from_pretrained(_UpperCamelCase     )
										self.assertIsNotNone(_UpperCamelCase     )




				@unittest.skip(reason="MRA does not output attentions"     )
				def      __a						(					self     )				->      Optional[Any]:
							return





@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):




				@slow
				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	MraModel.from_pretrained("uw-madison/mra-base-512-4"     )
							lowerCAmelCase_						=	torch.arange(256     ).unsqueeze(0     )

							with torch.no_grad():
										lowerCAmelCase_						=	model(_UpperCamelCase     )[0]

							lowerCAmelCase_						=	torch.Size((1, 256, 768)     )
							self.assertEqual(output.shape				,				_UpperCamelCase     )

							lowerCAmelCase_						=	torch.tensor(
							    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]     )

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




				@slow
				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"     )
							lowerCAmelCase_						=	torch.arange(256     ).unsqueeze(0     )

							with torch.no_grad():
										lowerCAmelCase_						=	model(_UpperCamelCase     )[0]

							lowerCAmelCase_						=	50_265

							lowerCAmelCase_						=	torch.Size((1, 256, vocab_size)     )
							self.assertEqual(output.shape				,				_UpperCamelCase     )

							lowerCAmelCase_						=	torch.tensor(
							    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]     )

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




				@slow
				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"     )
							lowerCAmelCase_						=	torch.arange(4_096     ).unsqueeze(0     )

							with torch.no_grad():
										lowerCAmelCase_						=	model(_UpperCamelCase     )[0]

							lowerCAmelCase_						=	50_265

							lowerCAmelCase_						=	torch.Size((1, 4_096, vocab_size)     )
							self.assertEqual(output.shape				,				_UpperCamelCase     )

							lowerCAmelCase_						=	torch.tensor(
							    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]     )

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





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = ""				):




			"""simple docstring"""


			lowerCAmelCase_						=	url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
			lowerCAmelCase_						=	BeautifulSoup(requests.get(__lowerCAmelCase				).text    ,							"html.parser"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							attrs="titleColumn"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							class_="ratingColumn imdbRating"				)
			return {
			    title.a.text: float(rating.strong.text				)
			    for title, rating in zip(__lowerCAmelCase    ,							__lowerCAmelCase				)
			}






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = "IMDb_Top_250_Movies.csv"				):




			"""simple docstring"""


			lowerCAmelCase_						=	get_imdb_top_aaa_movies()
			with open(__lowerCAmelCase    ,							"w"    ,							newline=""				) as out_file:
						lowerCAmelCase_						=	csv.writer(__lowerCAmelCase				)
						writer.writerow(["Movie title", "IMDb rating"]				)
						for title, rating in movies.items():
									writer.writerow([title, rating]				)


if __name__ == "__main__":
			write_movies()



