



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


_lowerCAmelCase	:   str    =   logging.get_logger(__name__)

_lowerCAmelCase	:   List[str]    =   {
    "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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'blenderbot-small'
		SCREAMING_SNAKE_CASE__								=	['past_key_values']
		SCREAMING_SNAKE_CASE__								=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(							self    ,a_=5_0265    ,a_=512    ,a_=8    ,a_=2048    ,a_=16    ,a_=8    ,a_=2048    ,a_=16    ,a_=0.0    ,a_=0.0    ,a_=True    ,a_=True    ,a_="gelu"    ,a_=512    ,a_=0.1    ,a_=0.0    ,a_=0.0    ,a_=0.02    ,a_=1    ,a_=False    ,a_=0    ,a_=1    ,a_=2    ,a_=2    ,**a_    ,):







						"""simple docstring"""
						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=a_    ,bos_token_id=a_    ,eos_token_id=a_    ,is_encoder_decoder=a_    ,decoder_start_token_id=a_    ,forced_eos_token_id=a_    ,**a_    ,)



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						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_(a_    ,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(a_						):
																		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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase__		  =  super().outputs
						else:
										lowerCAmelCase__		  =  super(a_    ,self						).outputs
										if self.use_past:
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.num_layers
														for i in range(a_						):
																		lowerCAmelCase__		  =  {0: 'batch', 2: 'past_sequence + sequence'}
																		lowerCAmelCase__		  =  {0: 'batch', 2: 'past_sequence + sequence'}
						return common_outputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    a_    ,a_    ,a_    ,a_    ,a_						)

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

						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(a_    ,a_						)]    ,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(a_    ,a_						)
										lowerCAmelCase__		  =  max(a_    ,a_						) - min_num_layers
										lowerCAmelCase__		  =  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

										for _ in range(a_						):
														common_inputs["past_key_values"].append(
														    (
														        torch.zeros(a_						),
														        torch.zeros(a_						),
														        torch.zeros(a_						),
														        torch.zeros(a_						),
														    )						)
										# TODO: test this.
										lowerCAmelCase__		  =  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
										for _ in range(a_    ,a_						):
														common_inputs["past_key_values"].append((torch.zeros(a_						), torch.zeros(a_						))						)
						return common_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    a_    ,a_    ,a_    ,a_    ,a_						)

						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(a_    ,a_    ,dtype=a_						)]    ,dim=1						)
										lowerCAmelCase__		  =  [
										    (torch.zeros(a_						), torch.zeros(a_						)) for _ in range(a_						)
										]
						return common_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,):







						"""simple docstring"""
						# 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(
						    a_    ,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(a_						)
						lowerCAmelCase__		  =  compute_effective_axis_dimension(
						    a_    ,fixed_dimension=OnnxConfig.default_fixed_sequence    ,num_token_to_add=a_						)

						# Generate dummy inputs according to compute batch and sequence
						lowerCAmelCase__		  =  [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
						lowerCAmelCase__		  =  dict(tokenizer(a_    ,return_tensors=a_						)						)
						return common_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,):







						"""simple docstring"""
						if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase__		  =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    a_    ,batch_size=a_    ,seq_length=a_    ,is_pair=a_    ,framework=a_						)

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

						return common_inputs



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						if self.task in ["default", "seq2seq-lm"]:
										lowerCAmelCase__		  =  super()._flatten_past_key_values_(a_    ,a_    ,a_    ,a_						)
						else:
										lowerCAmelCase__		  =  super(a_    ,self						)._flatten_past_key_values_(
										    a_    ,a_    ,a_    ,a_						)





from bisect import bisect
from itertools import accumulate







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  sorted(zip(snake_case__    ,			snake_case__					)    ,			key=lambda snake_case__					: x[0] / x[1]    ,			reverse=snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__		  =  list(accumulate(snake_case__					)					)
				lowerCAmelCase__		  =  bisect(snake_case__    ,			snake_case__					)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k]					)
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
