'''simple docstring'''




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	:					Union[str, Any]					=  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:					Any					=  {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]      =						"mbart"
      __UpperCamelCase:  Optional[Any]      =						["past_key_values"]
      __UpperCamelCase:  Tuple      =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(	self						:    Optional[int]						, A						:    Union[str, Any]=50265						, A						:    Union[str, Any]=1024						, A						:    Any=12						, A						:    Tuple=4096						, A						:    Tuple=16						, A						:    Dict=12						, A						:    int=4096						, A						:    List[str]=16						, A						:    List[Any]=0.0						, A						:    List[Any]=0.0						, A						:    str=True						, A						:    int=True						, A						:    List[Any]="gelu"						, A						:    Dict=1024						, A						:    Dict=0.1						, A						:    Optional[int]=0.0						, A						:    int=0.0						, A						:    List[str]=0.02						, A						:    Tuple=0.0						, A						:    List[str]=False						, A						:    Dict=1						, A						:    Dict=0						, A						:    Any=2						, A						:    List[Any]=2						, **A						:    str						, ):
             _UpperCAmelCase					:  Tuple											=   vocab_size
             _UpperCAmelCase					:  Tuple											=   max_position_embeddings
             _UpperCAmelCase					:  Optional[int]											=   d_model
             _UpperCAmelCase					:  Tuple											=   encoder_ffn_dim
             _UpperCAmelCase					:  Dict											=   encoder_layers
             _UpperCAmelCase					:  List[Any]											=   encoder_attention_heads
             _UpperCAmelCase					:  str											=   decoder_ffn_dim
             _UpperCAmelCase					:  List[str]											=   decoder_layers
             _UpperCAmelCase					:  Dict											=   decoder_attention_heads
             _UpperCAmelCase					:  List[str]											=   dropout
             _UpperCAmelCase					:  str											=   attention_dropout
             _UpperCAmelCase					:  Tuple											=   activation_dropout
             _UpperCAmelCase					:  Optional[int]											=   activation_function
             _UpperCAmelCase					:  Tuple											=   init_std
             _UpperCAmelCase					:  int											=   encoder_layerdrop
             _UpperCAmelCase					:  str											=   decoder_layerdrop
             _UpperCAmelCase					:  int											=   classifier_dropout
             _UpperCAmelCase					:  str											=   use_cache
             _UpperCAmelCase					:  Union[str, Any]											=   encoder_layers
             _UpperCAmelCase					:  Any											=   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						, forced_eos_token_id=A						, **A						, )

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      @property
      def 					_A     (	self						:    Tuple	):
             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase					:  List[str]											=   OrderedDict(
                        [
                            ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                            ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                        ]	)

                    if self.use_past:
                           _UpperCAmelCase					:  Tuple											=   {0: "batch"}
                           _UpperCAmelCase					:  int											=   {0: "batch", 1: "past_decoder_sequence + sequence"}
                    else:
                           _UpperCAmelCase					:  int											=   {0: "batch", 1: "decoder_sequence"}
                           _UpperCAmelCase					:  Optional[int]											=   {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.
                    _UpperCAmelCase					:  List[Any]											=   OrderedDict(
                        [
                            ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                            ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                        ]	)
                    if self.use_past:
                           _UpperCAmelCase				,  _UpperCAmelCase					:  Optional[int]											=   self.num_layers
                           for i in range(A	):
                                  _UpperCAmelCase					:  str											=   {0: "batch", 2: "past_sequence + sequence"}
                                  _UpperCAmelCase					:  Union[str, Any]											=   {0: "batch", 2: "past_sequence + sequence"}
             else:
                    _UpperCAmelCase					:  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
      def 					_A     (	self						:    int	):
             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase					:  Dict											=   super().outputs
             else:
                    _UpperCAmelCase					:  str											=   super(A						, self	).outputs
                    if self.use_past:
                           _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   self.num_layers
                           for i in range(A	):
                                  _UpperCAmelCase					:  List[Any]											=   {0: "batch", 2: "past_sequence + sequence"}
                                  _UpperCAmelCase					:  Tuple											=   {0: "batch", 2: "past_sequence + sequence"}
             return common_outputs




      def 					_A     (	self						:    Dict						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    Optional[TensorType] = None						, ):
             _UpperCAmelCase					:  Optional[Any]											=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 A						, A						, A						, A						, A	)

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

                    _UpperCAmelCase					:  Tuple											=   torch.cat(
                        [common_inputs["decoder_attention_mask"], torch.ones(A						, A	)]						, dim=1	)

                    _UpperCAmelCase					:  Dict											=   []
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   self.num_layers
                    _UpperCAmelCase					:  Tuple											=   min(A						, A	)
                    _UpperCAmelCase					:  Any											=   max(A						, A	) - min_num_layers
                    _UpperCAmelCase					:  Optional[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.
                    _UpperCAmelCase					:  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 					_A     (	self						:    List[Any]						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    Optional[TensorType] = None						, ):
             _UpperCAmelCase					:  Dict											=   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
                    _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   common_inputs["input_ids"].shape
                    # Not using the same length for past_key_values
                    _UpperCAmelCase					:  Dict											=   seqlen + 2
                    _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   self.num_layers
                    _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   self.num_attention_heads
                    _UpperCAmelCase					:  Union[str, Any]											=   (
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )

                    _UpperCAmelCase					:  Optional[int]											=   common_inputs["attention_mask"].dtype
                    _UpperCAmelCase					:  Tuple											=   torch.cat(
                        [common_inputs["attention_mask"], torch.ones(A						, A						, dtype=A	)]						, dim=1	)
                    _UpperCAmelCase					:  List[Any]											=   [
                        (torch.zeros(A	), torch.zeros(A	)) for _ in range(A	)
                    ]
             return common_inputs




      def 					_A     (	self						:    Optional[Any]						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    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
             _UpperCAmelCase					:  Dict											=   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
             _UpperCAmelCase					:  int											=   tokenizer.num_special_tokens_to_add(A	)
             _UpperCAmelCase					:  List[Any]											=   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
             _UpperCAmelCase					:  List[Any]											=   [" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
             _UpperCAmelCase					:  Union[str, Any]											=   dict(tokenizer(A						, return_tensors=A	)	)
             return common_inputs




      def 					_A     (	self						:    Dict						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    Optional[TensorType] = None						, ):
             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase					:  Tuple											=   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":
                    _UpperCAmelCase					:  List[str]											=   self._generate_dummy_inputs_for_causal_lm(
                        A						, batch_size=A						, seq_length=A						, is_pair=A						, framework=A	)
             else:
                    _UpperCAmelCase					:  List[str]											=   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 					_A     (	self						:    int						, A						:    Optional[Any]						, A						:    List[Any]						, A						:    Optional[Any]						, A						:    str	):
             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase					:  Dict											=   super()._flatten_past_key_values_(A						, A						, A						, A	)
             else:
                    _UpperCAmelCase					:  Optional[int]											=   super(A						, self	)._flatten_past_key_values_(
                        A						, A						, A						, A	)


'''simple docstring'''




from __future__ import annotations

from collections.abc import Generator

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   Generator[int, None, None]:


       """simple docstring"""







       if not isinstance(_UpperCAmelCase      ,     _UpperCAmelCase   ) or n < 1:
              raise Exception("Sequence only defined for positive integers"   )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _UpperCAmelCase					:  Optional[Any]											=   3 * n + 1
              yield n

def      UpperCamelCase_							( )   ->   Any:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   43
       _UpperCAmelCase					:  Optional[int]											=   tuple(collatz_sequence(_UpperCAmelCase   )   )
       print(_UpperCAmelCase   )
       print(F"""Collatz sequence from {n} took {len(_UpperCAmelCase   )} steps."""   )


if __name__ == "__main__":
  main()

