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

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


SCREAMING_SNAKE_CASE__		:					Any						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					List[str]						=					{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'marian'
  lowercase_			=     ['past_key_values']
  lowercase_			=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




  def __init__(      self     :	Dict     , a_     :	Optional[Any]=5_8101     , a_     :	Optional[Any]=None     , a_     :	Optional[int]=1024     , a_     :	Optional[Any]=12     , a_     :	int=4096     , a_     :	List[str]=16     , a_     :	Tuple=12     , a_     :	Union[str, Any]=4096     , a_     :	Dict=16     , a_     :	List[Any]=0.0     , a_     :	Optional[int]=0.0     , a_     :	Optional[Any]=True     , a_     :	Union[str, Any]=True     , a_     :	Union[str, Any]="gelu"     , a_     :	Optional[int]=1024     , a_     :	Dict=0.1     , a_     :	List[Any]=0.0     , a_     :	Optional[Any]=0.0     , a_     :	Any=0.02     , a_     :	Union[str, Any]=5_8100     , a_     :	str=False     , a_     :	Optional[Any]=5_8100     , a_     :	int=0     , a_     :	Optional[Any]=0     , a_     :	Union[str, Any]=True     , **a_     :	int     , )->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				Any			=		decoder_vocab_size or vocab_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		max_position_embeddings
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		d_model
       SCREAMING_SNAKE_CASE__				:				int			=		encoder_ffn_dim
       SCREAMING_SNAKE_CASE__				:				int			=		encoder_layers
       SCREAMING_SNAKE_CASE__				:				str			=		encoder_attention_heads
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		decoder_ffn_dim
       SCREAMING_SNAKE_CASE__				:				Any			=		decoder_layers
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		decoder_attention_heads
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		dropout
       SCREAMING_SNAKE_CASE__				:				Any			=		attention_dropout
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		activation_dropout
       SCREAMING_SNAKE_CASE__				:				str			=		activation_function
       SCREAMING_SNAKE_CASE__				:				Dict			=		init_std
       SCREAMING_SNAKE_CASE__				:				List[str]			=		encoder_layerdrop
       SCREAMING_SNAKE_CASE__				:				Tuple			=		decoder_layerdrop
       SCREAMING_SNAKE_CASE__				:				int			=		use_cache
       SCREAMING_SNAKE_CASE__				:				Any			=		encoder_layers
       SCREAMING_SNAKE_CASE__				:				Tuple			=		scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE__				:				Dict			=		share_encoder_decoder_embeddings
       super().__init__(
           pad_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					(      UpperCamelCase_						):




  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def 	__lowercase(      self     :	Optional[Any]				)->       Mapping[str, Mapping[int, str]]:




       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]				)

            if self.use_past:
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		{0: 'batch'}
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{0: 'batch', 1: 'decoder_sequence'}
                 SCREAMING_SNAKE_CASE__				:				str			=		{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.
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]				)
            if self.use_past:
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				str			=		self.num_layers
                 for i in range(a_				):
                      SCREAMING_SNAKE_CASE__				:				List[Any]			=		{0: 'batch', 2: 'past_sequence + sequence'}
                      SCREAMING_SNAKE_CASE__				:				str			=		{0: 'batch', 2: 'past_sequence + sequence'}
       else:
            SCREAMING_SNAKE_CASE__				:				Dict			=		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 	__lowercase(      self     :	Union[str, Any]				)->       Mapping[str, Mapping[int, str]]:




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




  def 	__lowercase(      self     :	Dict     , a_     :	PreTrainedTokenizer     , a_     :	int = -1     , a_     :	int = -1     , a_     :	bool = False     , a_     :	Optional[TensorType] = None     , )->       Mapping[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self._generate_dummy_inputs_for_encoder_and_decoder(
           a_     , a_     , a_     , a_     , a_				)

       # Generate decoder inputs
       SCREAMING_SNAKE_CASE__				:				Tuple			=		seq_length if not self.use_past else 1
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self._generate_dummy_inputs_for_encoder_and_decoder(
           a_     , a_     , a_     , a_     , a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       SCREAMING_SNAKE_CASE__				:				str			=		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
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Tuple			=		common_inputs['input_ids'].shape
            SCREAMING_SNAKE_CASE__				:				str			=		common_inputs['decoder_input_ids'].shape[1]
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.num_attention_heads
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		decoder_seq_length + 3
            SCREAMING_SNAKE_CASE__				:				Tuple			=		(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(a_     , a_				)]     , dim=1				)

            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.num_layers
            SCREAMING_SNAKE_CASE__				:				List[str]			=		min(a_     , a_				)
            SCREAMING_SNAKE_CASE__				:				str			=		max(a_     , a_				) - min_num_layers
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		'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.
            SCREAMING_SNAKE_CASE__				:				int			=		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 	__lowercase(      self     :	Any     , a_     :	PreTrainedTokenizer     , a_     :	int = -1     , a_     :	int = -1     , a_     :	bool = False     , a_     :	Optional[TensorType] = None     , )->       Mapping[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._generate_dummy_inputs_for_encoder_and_decoder(
           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
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		seqlen + 2
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.num_layers
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.num_attention_heads
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            SCREAMING_SNAKE_CASE__				:				str			=		common_inputs['attention_mask'].dtype
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.cat(
                [common_inputs['attention_mask'], torch.ones(a_     , a_     , dtype=a_				)]     , dim=1				)
            SCREAMING_SNAKE_CASE__				:				List[str]			=		[
                (torch.zeros(a_				), torch.zeros(a_				)) for _ in range(a_				)
            ]
       return common_inputs




  def 	__lowercase(      self     :	Optional[int]     , a_     :	PreTrainedTokenizer     , a_     :	int = -1     , a_     :	int = -1     , a_     :	bool = False     , a_     :	Optional[TensorType] = None     , )->       Mapping[str, Any]:




       """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
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer.num_special_tokens_to_add(a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		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
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[' '.join([tokenizer.unk_token]				) * seq_length] * batch_size
       SCREAMING_SNAKE_CASE__				:				Dict			=		dict(tokenizer(a_     , return_tensors=a_				)				)
       return common_inputs




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	PreTrainedTokenizer     , a_     :	int = -1     , a_     :	int = -1     , a_     :	bool = False     , a_     :	Optional[TensorType] = None     , )->       Mapping[str, Any]:




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

       else:
            SCREAMING_SNAKE_CASE__				:				str			=		self._generate_dummy_inputs_for_causal_lm(
                a_     , batch_size=a_     , seq_length=a_     , is_pair=a_     , framework=a_				)

       return common_inputs




  def 	__lowercase(      self     :	Any     , a_     :	Union[str, Any]     , a_     :	Optional[int]     , a_     :	Optional[Any]     , a_     :	int				)->       Optional[int]:




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




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       float:




       """simple docstring"""
       return 1e-4




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		[
           [],
           [],
           [],
       ]




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	int				)->       None:




       """simple docstring"""
       try:
            if len(self.queues[priority]				) >= 100:
                 raise OverflowError('Maximum queue size is 100'				)
            self.queues[priority].append(a_				)
       except IndexError:
            raise ValueError('Valid priorities are 0, 1, and 2'				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       for queue in self.queues:
            if queue:
                 return queue.pop(0				)
       raise UnderFlowError('All queues are empty'				)




  def __str__(      self     :	Any				)->       str:




       """simple docstring"""
       return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]




  def 	__lowercase(      self     :	List[str]     , a_     :	int				)->       None:




       """simple docstring"""
       if len(self.queue				) == 100:
            raise OverFlowError('Maximum queue size is 100'				)
       self.queue.append(a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       if not self.queue:
            raise UnderFlowError('The queue is empty'				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		min(self.queue				)
            self.queue.remove(a_				)
            return data




  def __str__(      self     :	List[str]				)->       str:




       """simple docstring"""
       return str(self.queue				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		FixedPriorityQueue()
     fpq.enqueue(0     ,  10 )
     fpq.enqueue(1     ,  70 )
     fpq.enqueue(0     ,  1_00 )
     fpq.enqueue(2     ,  1 )
     fpq.enqueue(2     ,  5 )
     fpq.enqueue(1     ,  7 )
     fpq.enqueue(2     ,  4 )
     fpq.enqueue(1     ,  64 )
     fpq.enqueue(0     ,  1_28 )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		ElementPriorityQueue()
     epq.enqueue(10 )
     epq.enqueue(70 )
     epq.enqueue(1_00 )
     epq.enqueue(1 )
     epq.enqueue(5 )
     epq.enqueue(7 )
     epq.enqueue(4 )
     epq.enqueue(64 )
     epq.enqueue(1_28 )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()



