
import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
					    SpeechaTextaDecoder,
					    SpeechaTextaForCausalLM,
					)

@require_torch
class      lowerCAmelCase__  :






		def __init__(	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=99							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=13							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=16							,    SCREAMING_SNAKE_CASE__	:							int=7							,    SCREAMING_SNAKE_CASE__	:							str=True							,    SCREAMING_SNAKE_CASE__	:							str=True							,    SCREAMING_SNAKE_CASE__	:							str=True							,    SCREAMING_SNAKE_CASE__	:							Any=False							,    SCREAMING_SNAKE_CASE__	:							Any=True							,    SCREAMING_SNAKE_CASE__	:							str=2							,    SCREAMING_SNAKE_CASE__	:							Tuple=32							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=4							,    SCREAMING_SNAKE_CASE__	:							List[Any]=4							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=30							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=0							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=1							,    SCREAMING_SNAKE_CASE__	:							int=2							,    SCREAMING_SNAKE_CASE__	:							List[str]=None							,    )  ->							Optional[int]:
						__lowerCamelCase					      =			parent
						__lowerCamelCase					      =			batch_size
						__lowerCamelCase					      =			decoder_seq_length
						# For common tests
						__lowerCamelCase					      =			self.decoder_seq_length
						__lowerCamelCase					      =			is_training
						__lowerCamelCase					      =			use_attention_mask
						__lowerCamelCase					      =			use_labels

						__lowerCamelCase					      =			vocab_size
						__lowerCamelCase					      =			d_model
						__lowerCamelCase					      =			d_model
						__lowerCamelCase					      =			decoder_layers
						__lowerCamelCase					      =			decoder_layers
						__lowerCamelCase					      =			decoder_ffn_dim
						__lowerCamelCase					      =			decoder_attention_heads
						__lowerCamelCase					      =			decoder_attention_heads
						__lowerCamelCase					      =			eos_token_id
						__lowerCamelCase					      =			bos_token_id
						__lowerCamelCase					      =			pad_token_id
						__lowerCamelCase					      =			decoder_start_token_id
						__lowerCamelCase					      =			use_cache
						__lowerCamelCase					      =			max_position_embeddings

						__lowerCamelCase					      =			None
						__lowerCamelCase					      =			decoder_seq_length
						__lowerCamelCase					      =			2
						__lowerCamelCase					      =			1






		def 				__A (	self	:							Dict						)  ->							Optional[Any]:
						__lowerCamelCase					      =			ids_tensor([self.batch_size, self.decoder_seq_length]							,    self.vocab_size						)

						__lowerCamelCase					      =			None
						if self.use_attention_mask:
										__lowerCamelCase					      =			ids_tensor([self.batch_size, self.decoder_seq_length]							,    vocab_size=2						)

						__lowerCamelCase					      =			None
						if self.use_labels:
										__lowerCamelCase					      =			ids_tensor([self.batch_size, self.decoder_seq_length]							,    self.vocab_size						)

						__lowerCamelCase					      =			SpeechaTextaConfig(
						    vocab_size=self.vocab_size							,    d_model=self.d_model							,    decoder_layers=self.decoder_layers							,    decoder_ffn_dim=self.decoder_ffn_dim							,    decoder_attention_heads=self.decoder_attention_heads							,    eos_token_id=self.eos_token_id							,    bos_token_id=self.bos_token_id							,    use_cache=self.use_cache							,    pad_token_id=self.pad_token_id							,    decoder_start_token_id=self.decoder_start_token_id							,    max_position_embeddings=self.max_position_embeddings							,    )

						return (
						    config,
						    input_ids,
						    attention_mask,
						    lm_labels,
						)






		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    )  ->							Dict:
						__lowerCamelCase					      =			True
						__lowerCamelCase					      =			SpeechaTextaDecoder(config=SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						).eval()
						__lowerCamelCase					      =			input_ids[:2]

						input_ids[input_ids == 0] += 1
						# first forward pass
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__							,    use_cache=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__							,    use_cache=SCREAMING_SNAKE_CASE__						)

						self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__						) == len(SCREAMING_SNAKE_CASE__						)						)
						self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__						) == len(SCREAMING_SNAKE_CASE__						) + 1						)

						__lowerCamelCase					      =			outputs['''past_key_values''']

						# create hypothetical next token and extent to next_input_ids
						__lowerCamelCase					      =			ids_tensor((2, 1)							,    config.vocab_size - 1						) + 1

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

						print(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)['''last_hidden_state''']
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__							,    past_key_values=SCREAMING_SNAKE_CASE__						)['''last_hidden_state''']

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

						# test that outputs are equal for slice
						assert torch.allclose(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    atol=1e-3						)







		def 				__A (	self	:							Any						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			self.prepare_config_and_inputs()
						(
						    (
						    __lowerCamelCase
						)      ,					(
						    __lowerCamelCase
						)      ,					(
						    __lowerCamelCase
						)      ,					(
						    __lowerCamelCase
						)      ,					
						)					      =			config_and_inputs

						__lowerCamelCase					      =			{
						    '''input_ids''': input_ids,
						    '''attention_mask''': attention_mask,
						}
						return config, inputs_dict





@require_torch
class      lowerCAmelCase__  (			__lowercase						,			__lowercase						,			__lowercase						,			unittest.TestCase    ):
		a__ :		List[Any]   						=	(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
		a__ :		Optional[int]   						=	(SpeechaTextaForCausalLM,) if is_torch_available() else ()
		a__ :		Union[str, Any]   						=	{"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
		a__ :		Any   						=	True
		a__ :		List[str]   						=	False






		def 				__A (	self	:							Any							,    )  ->							Tuple:
						__lowerCamelCase					      =			SpeechaTextaStandaloneDecoderModelTester(self							,    is_training=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			ConfigTester(self							,    config_class=SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Optional[Any]						)  ->							Any:
						pass






		def 				__A (	self	:							List[str]						)  ->							List[Any]:
						pass






		def 				__A (	self	:							Optional[Any]						)  ->							Union[str, Any]:
						pass






		def 				__A (	self	:							Optional[int]						)  ->							Optional[int]:
						self.config_tester.run_common_tests()






		def 				__A (	self	:							Union[str, Any]						)  ->							Dict:
						__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE__						)







		def 				__A (	self	:							Any						)  ->							str:
						return





import bsa
import requests




def 				__magic_name__				(     __lowerCAmelCase			:		bsa.element.ResultSet							)    ->     dict[str, str]:
				return {
				    "name": soup.ha.a.text,
				    "genre": soup.find('''span'''	,						class_='''genre'''							).text.strip(),
				    "rating": soup.strong.text,
				    "page_link": f'''https://www.imdb.com{soup.a.get('href'							)}''',
				}




def 				__magic_name__				(     __lowerCAmelCase			:		int = 5							)    ->     tuple:
				__lowerCamelCase					      =			int(float(__lowerCAmelCase							)							)
				if num_movies < 1:
								return ()
				__lowerCamelCase					      =			(
				    '''https://www.imdb.com/search/title?title_type='''
				    f'''feature&sort=num_votes,desc&count={num_movies}'''
				)
				__lowerCamelCase					      =			bsa.BeautifulSoup(requests.get(__lowerCAmelCase							).content	,						'''html.parser'''							)
				return tuple(
				    get_movie_data_from_soup(__lowerCAmelCase							)
				    for movie in source.find_all('''div'''	,						class_='''lister-item mode-advanced'''							)							)


if __name__ == "__main__":
					import json

					SCREAMING_SNAKE_CASE__     :    str      							=					int(input("How many movies would you like to see? "))
					print(
					    ", ".join(
					        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
					    )
					)



