
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


SCREAMING_SNAKE_CASE__     :    Dict      							=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :    List[str]      							=					{
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}

class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		List[Any]   						=	"""blenderbot"""
		a__ :		Optional[int]   						=	["""past_key_values"""]
		a__ :		int   						=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






		def __init__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=80_08							,    SCREAMING_SNAKE_CASE__	:							Tuple=1_28							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=2							,    SCREAMING_SNAKE_CASE__	:							Dict=1_02_40							,    SCREAMING_SNAKE_CASE__	:							List[Any]=32							,    SCREAMING_SNAKE_CASE__	:							List[Any]=24							,    SCREAMING_SNAKE_CASE__	:							List[str]=1_02_40							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=32							,    SCREAMING_SNAKE_CASE__	:							List[Any]=0.0							,    SCREAMING_SNAKE_CASE__	:							Dict=0.0							,    SCREAMING_SNAKE_CASE__	:							Tuple=True							,    SCREAMING_SNAKE_CASE__	:							List[str]=True							,    SCREAMING_SNAKE_CASE__	:							List[str]="gelu"							,    SCREAMING_SNAKE_CASE__	:							str=25_60							,    SCREAMING_SNAKE_CASE__	:							Dict=0.1							,    SCREAMING_SNAKE_CASE__	:							Any=0.0							,    SCREAMING_SNAKE_CASE__	:							Dict=0.0							,    SCREAMING_SNAKE_CASE__	:							Any=0.02							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=1							,    SCREAMING_SNAKE_CASE__	:							List[str]=False							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=0							,    SCREAMING_SNAKE_CASE__	:							int=1							,    SCREAMING_SNAKE_CASE__	:							List[str]=2							,    SCREAMING_SNAKE_CASE__	:							List[Any]=3							,    SCREAMING_SNAKE_CASE__	:							Dict=2							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    )  ->							str:
						__lowerCamelCase					      =			vocab_size
						__lowerCamelCase					      =			max_position_embeddings
						__lowerCamelCase					      =			d_model
						__lowerCamelCase					      =			encoder_ffn_dim
						__lowerCamelCase					      =			encoder_layers
						__lowerCamelCase					      =			encoder_attention_heads
						__lowerCamelCase					      =			decoder_ffn_dim
						__lowerCamelCase					      =			decoder_layers
						__lowerCamelCase					      =			decoder_attention_heads
						__lowerCamelCase					      =			dropout
						__lowerCamelCase					      =			attention_dropout
						__lowerCamelCase					      =			activation_dropout
						__lowerCamelCase					      =			activation_function
						__lowerCamelCase					      =			init_std
						__lowerCamelCase					      =			encoder_layerdrop
						__lowerCamelCase					      =			decoder_layerdrop
						__lowerCamelCase					      =			use_cache
						__lowerCamelCase					      =			encoder_layers
						__lowerCamelCase					      =			scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE__							,    bos_token_id=SCREAMING_SNAKE_CASE__							,    eos_token_id=SCREAMING_SNAKE_CASE__							,    is_encoder_decoder=SCREAMING_SNAKE_CASE__							,    decoder_start_token_id=SCREAMING_SNAKE_CASE__							,    encoder_no_repeat_ngram_size=SCREAMING_SNAKE_CASE__							,    forced_eos_token_id=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__							,    )





class      lowerCAmelCase__  (			__lowercase    ):






		@property
		def 				__A (	self	:							Union[str, Any]						)  ->							Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
										__lowerCamelCase					      =			OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]						)
										if self.use_past:
														__lowerCamelCase					      =			{0: '''batch'''}
														__lowerCamelCase					      =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										else:
														__lowerCamelCase					      =			{0: '''batch''', 1: '''decoder_sequence'''}
														__lowerCamelCase					      =			{0: '''batch''', 1: '''decoder_sequence'''}
										if self.use_past:
														self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__							,    direction='''inputs'''						)
						elif self.task == "causal-lm":
										__lowerCamelCase					      =			OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]						)
										if self.use_past:
														__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_layers
														for i in range(SCREAMING_SNAKE_CASE__						):
																		__lowerCamelCase					      =			{0: '''batch''', 2: '''past_sequence + sequence'''}
																		__lowerCamelCase					      =			{0: '''batch''', 2: '''past_sequence + sequence'''}
						else:
										__lowerCamelCase					      =			OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
										        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
										    ]						)

						return common_inputs






		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def 				__A (	self	:							Any						)  ->							Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
										__lowerCamelCase					      =			super().outputs
						else:
										__lowerCamelCase					      =			super(SCREAMING_SNAKE_CASE__							,    self						).outputs
										if self.use_past:
														__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_layers
														for i in range(SCREAMING_SNAKE_CASE__						):
																		__lowerCamelCase					      =			{0: '''batch''', 2: '''past_sequence + sequence'''}
																		__lowerCamelCase					      =			{0: '''batch''', 2: '''past_sequence + sequence'''}
						return common_outputs






		def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						# Generate decoder inputs
						__lowerCamelCase					      =			seq_length if not self.use_past else 1
						__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__lowerCamelCase					      =			dict(**SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)

						if self.use_past:
										if not is_torch_available():
														raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
										else:
														import torch
										__lowerCamelCase      ,					__lowerCamelCase					      =			common_inputs['''input_ids'''].shape
										__lowerCamelCase					      =			common_inputs['''decoder_input_ids'''].shape[1]
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_attention_heads
										__lowerCamelCase					      =			(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										__lowerCamelCase					      =			decoder_seq_length
										__lowerCamelCase					      =			(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)
										__lowerCamelCase					      =			torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)]							,    dim=1						)
										__lowerCamelCase					      =			[]
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_layers

										for _ in range(SCREAMING_SNAKE_CASE__						):
														common_inputs["past_key_values"].append(
														    (
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														    )						)
						return common_inputs






		def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						if self.use_past:
										if not is_torch_available():
														raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
										else:
														import torch
										__lowerCamelCase      ,					__lowerCamelCase					      =			common_inputs['''input_ids'''].shape
										__lowerCamelCase					      =			seqlen
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_layers
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_attention_heads
										__lowerCamelCase					      =			(
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										__lowerCamelCase					      =			common_inputs['''attention_mask'''].dtype
										__lowerCamelCase					      =			torch.cat(
										    [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    dtype=SCREAMING_SNAKE_CASE__						)]							,    dim=1						)
										__lowerCamelCase					      =			[
										    (torch.zeros(SCREAMING_SNAKE_CASE__						), torch.zeros(SCREAMING_SNAKE_CASE__						)) for _ in range(SCREAMING_SNAKE_CASE__						)
										]
						return common_inputs






		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						# Copied from OnnxConfig.generate_dummy_inputs
						# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__lowerCamelCase					      =			compute_effective_axis_dimension(
						    SCREAMING_SNAKE_CASE__							,    fixed_dimension=OnnxConfig.default_fixed_batch							,    num_token_to_add=0						)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__lowerCamelCase					      =			tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			compute_effective_axis_dimension(
						    SCREAMING_SNAKE_CASE__							,    fixed_dimension=OnnxConfig.default_fixed_sequence							,    num_token_to_add=SCREAMING_SNAKE_CASE__						)

						# Generate dummy inputs according to compute batch and sequence
						__lowerCamelCase					      =			[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
						__lowerCamelCase					      =			dict(tokenizer(SCREAMING_SNAKE_CASE__							,    return_tensors=SCREAMING_SNAKE_CASE__						)						)
						return common_inputs






		def 				__A (	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						if self.task in ["default", "seq2seq-lm"]:
										__lowerCamelCase					      =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    SCREAMING_SNAKE_CASE__							,    batch_size=SCREAMING_SNAKE_CASE__							,    seq_length=SCREAMING_SNAKE_CASE__							,    is_pair=SCREAMING_SNAKE_CASE__							,    framework=SCREAMING_SNAKE_CASE__						)

						elif self.task == "causal-lm":
										__lowerCamelCase					      =			self._generate_dummy_inputs_for_causal_lm(
										    SCREAMING_SNAKE_CASE__							,    batch_size=SCREAMING_SNAKE_CASE__							,    seq_length=SCREAMING_SNAKE_CASE__							,    is_pair=SCREAMING_SNAKE_CASE__							,    framework=SCREAMING_SNAKE_CASE__						)
						else:
										__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    SCREAMING_SNAKE_CASE__							,    batch_size=SCREAMING_SNAKE_CASE__							,    seq_length=SCREAMING_SNAKE_CASE__							,    is_pair=SCREAMING_SNAKE_CASE__							,    framework=SCREAMING_SNAKE_CASE__						)

						return common_inputs






		def 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Any							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							int						)  ->							Union[str, Any]:
						if self.task in ["default", "seq2seq-lm"]:
										__lowerCamelCase					      =			super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						else:
										__lowerCamelCase					      =			super(SCREAMING_SNAKE_CASE__							,    self						)._flatten_past_key_values_(
										    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)







		def 				__A (	self	:							Any							,    SCREAMING_SNAKE_CASE__	:							Mapping[str, Mapping[int, str]]							,    SCREAMING_SNAKE_CASE__	:							str						)  ->							List[Any]:
						if direction not in ["inputs", "outputs"]:
										raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''						)

						__lowerCamelCase					      =			'''past_key_values''' if direction == '''inputs''' else '''present'''
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_layers

						__lowerCamelCase					      =			'''past_encoder_sequence'''
						__lowerCamelCase					      =			'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

						for i in range(SCREAMING_SNAKE_CASE__						):
										__lowerCamelCase					      =			{0: '''batch''', 2: decoder_sequence}
										__lowerCamelCase					      =			{0: '''batch''', 2: decoder_sequence}
										__lowerCamelCase					      =			{0: '''batch''', 2: encoder_sequence}
										__lowerCamelCase					      =			{0: '''batch''', 2: encoder_sequence}





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)
					    )
					)



