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:						int    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						Optional[int]    	=       {
    '''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 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      '''blenderbot'''
		A_						 =      ['''past_key_values''']
		A_						 =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



		def __init__(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=8_008  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=128  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=2  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=10_240  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=32  ,					__SCREAMING_SNAKE_CASE    :				str=24  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=10_240  ,					__SCREAMING_SNAKE_CASE    :				Tuple=32  ,					__SCREAMING_SNAKE_CASE    :				Any=0.0  ,					__SCREAMING_SNAKE_CASE    :				List[str]=0.0  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True  ,					__SCREAMING_SNAKE_CASE    :				List[str]=True  ,					__SCREAMING_SNAKE_CASE    :				int="gelu"  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=2_560  ,					__SCREAMING_SNAKE_CASE    :				List[str]=0.1  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=0.0  ,					__SCREAMING_SNAKE_CASE    :				Dict=0.0  ,					__SCREAMING_SNAKE_CASE    :				List[str]=0.0_2  ,					__SCREAMING_SNAKE_CASE    :				Dict=1  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=0  ,					__SCREAMING_SNAKE_CASE    :				int=1  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=2  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=3  ,					__SCREAMING_SNAKE_CASE    :				int=2  ,					**__SCREAMING_SNAKE_CASE    :				Tuple  ,					):






					"""simple docstring"""
					_snake_case              =      vocab_size
					_snake_case              =      max_position_embeddings
					_snake_case              =      d_model
					_snake_case              =      encoder_ffn_dim
					_snake_case              =      encoder_layers
					_snake_case              =      encoder_attention_heads
					_snake_case              =      decoder_ffn_dim
					_snake_case              =      decoder_layers
					_snake_case              =      decoder_attention_heads
					_snake_case              =      dropout
					_snake_case              =      attention_dropout
					_snake_case              =      activation_dropout
					_snake_case              =      activation_function
					_snake_case              =      init_std
					_snake_case              =      encoder_layerdrop
					_snake_case              =      decoder_layerdrop
					_snake_case              =      use_cache
					_snake_case              =      encoder_layers
					_snake_case              =      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 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		@property
		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					if self.task in ["default", "seq2seq-lm"]:
								_snake_case              =      OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
								        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
								    ]   )
								if self.use_past:
											_snake_case              =      {0: "batch"}
											_snake_case              =      {0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
											_snake_case              =      {0: "batch", 1: "decoder_sequence"}
											_snake_case              =      {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":
								_snake_case              =      OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
								        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
								    ]   )
								if self.use_past:
											_snake_case       , _snake_case              =      self.num_layers
											for i in range(__SCREAMING_SNAKE_CASE   ):
														_snake_case              =      {0: "batch", 2: "past_sequence + sequence"}
														_snake_case              =      {0: "batch", 2: "past_sequence + sequence"}
					else:
								_snake_case              =      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    :				List[str]   ):






					"""simple docstring"""
					if self.task in ["default", "seq2seq-lm"]:
								_snake_case              =      super().outputs
					else:
								_snake_case              =      super(__SCREAMING_SNAKE_CASE  ,					self   ).outputs
								if self.use_past:
											_snake_case       , _snake_case              =      self.num_layers
											for i in range(__SCREAMING_SNAKE_CASE   ):
														_snake_case              =      {0: "batch", 2: "past_sequence + sequence"}
														_snake_case              =      {0: "batch", 2: "past_sequence + sequence"}
					return common_outputs



		def 				_a				(			self    :				Dict  ,					__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  ,					):






					"""simple docstring"""
					_snake_case              =      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
					_snake_case              =      seq_length if not self.use_past else 1
					_snake_case              =      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   )
					_snake_case              =      {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
					_snake_case              =      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
								_snake_case       , _snake_case              =      common_inputs["input_ids"].shape
								_snake_case              =      common_inputs["decoder_input_ids"].shape[1]
								_snake_case       , _snake_case              =      self.num_attention_heads
								_snake_case              =      (
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								_snake_case              =      decoder_seq_length
								_snake_case              =      (
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)
								_snake_case              =      torch.cat(
								    [common_inputs["decoder_attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )]  ,					dim=1   )
								_snake_case              =      []
								_snake_case       , _snake_case              =      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    :				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  ,					):






					"""simple docstring"""
					_snake_case              =      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
								_snake_case       , _snake_case              =      common_inputs["input_ids"].shape
								_snake_case              =      seqlen
								_snake_case       , _snake_case              =      self.num_layers
								_snake_case       , _snake_case              =      self.num_attention_heads
								_snake_case              =      (
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								_snake_case              =      common_inputs["attention_mask"].dtype
								_snake_case              =      torch.cat(
								    [common_inputs["attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					dtype=__SCREAMING_SNAKE_CASE   )]  ,					dim=1   )
								_snake_case              =      [
								    (torch.zeros(__SCREAMING_SNAKE_CASE   ), torch.zeros(__SCREAMING_SNAKE_CASE   )) for _ in range(__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  ,					):






					"""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
					_snake_case              =      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
					_snake_case              =      tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      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
					_snake_case              =      [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
					_snake_case              =      dict(tokenizer(__SCREAMING_SNAKE_CASE  ,					return_tensors=__SCREAMING_SNAKE_CASE   )   )
					return common_inputs



		def 				_a				(			self    :				List[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  ,					):






					"""simple docstring"""
					if self.task in ["default", "seq2seq-lm"]:
								_snake_case              =      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":
								_snake_case              =      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:
								_snake_case              =      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    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






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







		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Mapping[str, Mapping[int, str]]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					if direction not in ["inputs", "outputs"]:
								raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'   )

					_snake_case              =      "past_key_values" if direction == "inputs" else "present"
					_snake_case       , _snake_case              =      self.num_layers

					_snake_case              =      "past_encoder_sequence"
					_snake_case              =      "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

					for i in range(__SCREAMING_SNAKE_CASE   ):
								_snake_case              =      {0: "batch", 2: decoder_sequence}
								_snake_case              =      {0: "batch", 2: decoder_sequence}
								_snake_case              =      {0: "batch", 2: encoder_sequence}
								_snake_case              =      {0: "batch", 2: encoder_sequence}


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

