




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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	List[str]    		=   'blenderbot-small'
      UpperCAmelCase__      :	int    		=   ['past_key_values']
      UpperCAmelCase__      :	List[str]    		=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(				self: Tuple    ,       UpperCamelCase_: Dict=5_02_65    ,       UpperCamelCase_: List[str]=5_12    ,       UpperCamelCase_: Any=8    ,       UpperCamelCase_: int=20_48    ,       UpperCamelCase_: str=16    ,       UpperCamelCase_: Tuple=8    ,       UpperCamelCase_: Optional[int]=20_48    ,       UpperCamelCase_: Any=16    ,       UpperCamelCase_: List[str]=0.0    ,       UpperCamelCase_: Any=0.0    ,       UpperCamelCase_: Tuple=True    ,       UpperCamelCase_: Dict=True    ,       UpperCamelCase_: str="gelu"    ,       UpperCamelCase_: List[str]=5_12    ,       UpperCamelCase_: List[str]=0.1    ,       UpperCamelCase_: Union[str, Any]=0.0    ,       UpperCamelCase_: Dict=0.0    ,       UpperCamelCase_: List[str]=0.02    ,       UpperCamelCase_: Tuple=1    ,       UpperCamelCase_: Tuple=False    ,       UpperCamelCase_: int=0    ,       UpperCamelCase_: List[Any]=1    ,       UpperCamelCase_: List[str]=2    ,       UpperCamelCase_: Union[str, Any]=2    ,       **UpperCamelCase_: Tuple    ,       ):
             __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=UpperCamelCase_    ,       bos_token_id=UpperCamelCase_    ,       eos_token_id=UpperCamelCase_    ,       is_encoder_decoder=UpperCamelCase_    ,       decoder_start_token_id=UpperCamelCase_    ,       forced_eos_token_id=UpperCamelCase_    ,       **UpperCamelCase_    ,       )





class     lowerCamelCase__(					__lowerCamelCase):
      @property
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             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_(UpperCamelCase_    ,       direction="""inputs"""   )
             elif self.task == "causal-lm":
                    # TODO: figure this case out.
                    __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(UpperCamelCase_   ):
                                  __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
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             if self.task in ["default", "seq2seq-lm"]:
                    __lowerCamelCase   =						super().outputs
             else:
                    __lowerCamelCase   =						super(UpperCamelCase_    ,       self   ).outputs
                    if self.use_past:
                           __lowerCamelCase,   __lowerCamelCase   =						self.num_layers
                           for i in range(UpperCamelCase_   ):
                                  __lowerCamelCase   =						{0: """batch""", 2: """past_sequence + sequence"""}
                                  __lowerCamelCase   =						{0: """batch""", 2: """past_sequence + sequence"""}
             return common_outputs
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: PreTrainedTokenizer    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[TensorType] = None    ,       ):
             __lowerCamelCase   =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )

             # Generate decoder inputs
             __lowerCamelCase   =						seq_length if not self.use_past else 1
             __lowerCamelCase   =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
             __lowerCamelCase   =						{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
             __lowerCamelCase   =						dict(**UpperCamelCase_    ,       **UpperCamelCase_   )

             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 + 3
                    __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(UpperCamelCase_    ,       UpperCamelCase_   )]    ,       dim=1   )

                    __lowerCamelCase   =						[]
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    __lowerCamelCase,   __lowerCamelCase   =						self.num_layers
                    __lowerCamelCase   =						min(UpperCamelCase_    ,       UpperCamelCase_   )
                    __lowerCamelCase   =						max(UpperCamelCase_    ,       UpperCamelCase_   ) - min_num_layers
                    __lowerCamelCase   =						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

                    for _ in range(UpperCamelCase_   ):
                           common_inputs["past_key_values"].append(
                               (
                                   torch.zeros(UpperCamelCase_   ),
                                   torch.zeros(UpperCamelCase_   ),
                                   torch.zeros(UpperCamelCase_   ),
                                   torch.zeros(UpperCamelCase_   ),
                               )   )
                    # TODO: test this.
                    __lowerCamelCase   =						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                    for _ in range(UpperCamelCase_    ,       UpperCamelCase_   ):
                           common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_   ), torch.zeros(UpperCamelCase_   ))   )
             return common_inputs
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: PreTrainedTokenizer    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[TensorType] = None    ,       ):
             __lowerCamelCase   =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )

             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
                    # Not using the same length for past_key_values
                    __lowerCamelCase   =						seqlen + 2
                    __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(UpperCamelCase_    ,       UpperCamelCase_    ,       dtype=UpperCamelCase_   )]    ,       dim=1   )
                    __lowerCamelCase   =						[
                        (torch.zeros(UpperCamelCase_   ), torch.zeros(UpperCamelCase_   )) for _ in range(UpperCamelCase_   )
                    ]
             return common_inputs
      def 			lowerCAmelCase__						(				self: Any    ,       UpperCamelCase_: PreTrainedTokenizer    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[TensorType] = None    ,       ):
             # 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(
                 UpperCamelCase_    ,       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(UpperCamelCase_   )
             __lowerCamelCase   =						compute_effective_axis_dimension(
                 UpperCamelCase_    ,       fixed_dimension=OnnxConfig.default_fixed_sequence    ,       num_token_to_add=UpperCamelCase_   )

             # Generate dummy inputs according to compute batch and sequence
             __lowerCamelCase   =						[""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
             __lowerCamelCase   =						dict(tokenizer(UpperCamelCase_    ,       return_tensors=UpperCamelCase_   )   )
             return common_inputs
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: PreTrainedTokenizer    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: int = -1    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[TensorType] = None    ,       ):
             if self.task in ["default", "seq2seq-lm"]:
                    __lowerCamelCase   =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        UpperCamelCase_    ,       batch_size=UpperCamelCase_    ,       seq_length=UpperCamelCase_    ,       is_pair=UpperCamelCase_    ,       framework=UpperCamelCase_   )

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

             return common_inputs




      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: List[str]   ):
             if self.task in ["default", "seq2seq-lm"]:
                    __lowerCamelCase   =						super()._flatten_past_key_values_(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						super(UpperCamelCase_    ,       self   )._flatten_past_key_values_(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )



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()



