






'''simple docstring'''




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 :  str  =						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 			lowerCamelCase_			(lowerCamelCase__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		'blenderbot'
      __UpperCamelCase:						Dict      			=		['past_key_values']
      __UpperCamelCase:						List[Any]      			=		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




      def __init__( self		:       Dict		,      A		:       Optional[int]=8008		,      A		:       str=128		,      A		:       Union[str, Any]=2		,      A		:       Union[str, Any]=10240		,      A		:       Dict=32		,      A		:       Dict=24		,      A		:       Union[str, Any]=10240		,      A		:       int=32		,      A		:       List[Any]=0.0		,      A		:       int=0.0		,      A		:       List[Any]=True		,      A		:       Tuple=True		,      A		:       List[Any]="gelu"		,      A		:       List[Any]=2560		,      A		:       Optional[Any]=0.1		,      A		:       str=0.0		,      A		:       Dict=0.0		,      A		:       List[Any]=0.02		,      A		:       List[str]=1		,      A		:       Any=False		,      A		:       Tuple=0		,      A		:       str=1		,      A		:       int=2		,      A		:       Optional[Any]=3		,      A		:       str=2		,      **A		:       Tuple		,      ):
             _UpperCAmelCase  :						Any							   =					vocab_size
             _UpperCAmelCase  :						Any							   =					max_position_embeddings
             _UpperCAmelCase  :						str							   =					d_model
             _UpperCAmelCase  :						Union[str, Any]							   =					encoder_ffn_dim
             _UpperCAmelCase  :						Union[str, Any]							   =					encoder_layers
             _UpperCAmelCase  :						int							   =					encoder_attention_heads
             _UpperCAmelCase  :						Optional[Any]							   =					decoder_ffn_dim
             _UpperCAmelCase  :						List[Any]							   =					decoder_layers
             _UpperCAmelCase  :						str							   =					decoder_attention_heads
             _UpperCAmelCase  :						Union[str, Any]							   =					dropout
             _UpperCAmelCase  :						str							   =					attention_dropout
             _UpperCAmelCase  :						Tuple							   =					activation_dropout
             _UpperCAmelCase  :						str							   =					activation_function
             _UpperCAmelCase  :						Optional[int]							   =					init_std
             _UpperCAmelCase  :						List[str]							   =					encoder_layerdrop
             _UpperCAmelCase  :						List[Any]							   =					decoder_layerdrop
             _UpperCAmelCase  :						Dict							   =					use_cache
             _UpperCAmelCase  :						List[Any]							   =					encoder_layers
             _UpperCAmelCase  :						List[Any]							   =					scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 pad_token_id=lowercase_		,      bos_token_id=lowercase_		,      eos_token_id=lowercase_		,      is_encoder_decoder=lowercase_		,      decoder_start_token_id=lowercase_		,      encoder_no_repeat_ngram_size=lowercase_		,      forced_eos_token_id=lowercase_		,      **lowercase_		,      )

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

      @property
      def   _A			( self		:       Optional[Any] ):






             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase  :						str							   =					OrderedDict(
                        [
                            ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                            ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                        ] )
                    if self.use_past:
                           _UpperCAmelCase  :						Any							   =					{0: "batch"}
                           _UpperCAmelCase  :						Optional[Any]							   =					{0: "batch", 1: "past_decoder_sequence + sequence"}
                    else:
                           _UpperCAmelCase  :						int							   =					{0: "batch", 1: "decoder_sequence"}
                           _UpperCAmelCase  :						Any							   =					{0: "batch", 1: "decoder_sequence"}
                    if self.use_past:
                           self.fill_with_past_key_values_(lowercase_		,      direction="inputs" )
             elif self.task == "causal-lm":
                    _UpperCAmelCase  :						List[Any]							   =					OrderedDict(
                        [
                            ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                            ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                        ] )
                    if self.use_past:
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					self.num_layers
                           for i in range(lowercase_ ):
                                  _UpperCAmelCase  :						Union[str, Any]							   =					{0: "batch", 2: "past_sequence + sequence"}
                                  _UpperCAmelCase  :						Optional[Any]							   =					{0: "batch", 2: "past_sequence + sequence"}
             else:
                    _UpperCAmelCase  :						List[Any]							   =					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		:       int ):






             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase  :						Optional[int]							   =					super().outputs
             else:
                    _UpperCAmelCase  :						Union[str, Any]							   =					super(lowercase_		,      self ).outputs
                    if self.use_past:
                           _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					self.num_layers
                           for i in range(lowercase_ ):
                                  _UpperCAmelCase  :						Dict							   =					{0: "batch", 2: "past_sequence + sequence"}
                                  _UpperCAmelCase  :						Tuple							   =					{0: "batch", 2: "past_sequence + sequence"}
             return common_outputs




      def   _A			( self		:       Union[str, Any]		,      A		:       Dict		,      A		:       List[str] = -1		,      A		:       Any = -1		,      A		:       List[str] = False		,      A		:       Optional[Any] = None		,      ):
             _UpperCAmelCase  :						str							   =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_		,      lowercase_		,      lowercase_		,      lowercase_		,      lowercase_ )
             # Generate decoder inputs
             _UpperCAmelCase  :						List[str]							   =					seq_length if not self.use_past else 1
             _UpperCAmelCase  :						Any							   =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_		,      lowercase_		,      lowercase_		,      lowercase_		,      lowercase_ )
             _UpperCAmelCase  :						Optional[int]							   =					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
             _UpperCAmelCase  :						Optional[Any]							   =					dict(**lowercase_		,      **lowercase_ )

             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  :						Tuple							   =					common_inputs["input_ids"].shape
                    _UpperCAmelCase  :						List[str]							   =					common_inputs["decoder_input_ids"].shape[1]
                    _UpperCAmelCase   ,						_UpperCAmelCase  :						Tuple							   =					self.num_attention_heads
                    _UpperCAmelCase  :						Any							   =					(
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    _UpperCAmelCase  :						Optional[Any]							   =					decoder_seq_length
                    _UpperCAmelCase  :						Dict							   =					(
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )
                    _UpperCAmelCase  :						List[Any]							   =					torch.cat(
                        [common_inputs["decoder_attention_mask"], torch.ones(lowercase_		,      lowercase_ )]		,      dim=1 )
                    _UpperCAmelCase  :						Optional[Any]							   =					[]
                    _UpperCAmelCase   ,						_UpperCAmelCase  :						Optional[int]							   =					self.num_layers

                    for _ in range(lowercase_ ):
                           common_inputs["past_key_values"].append(
                               (
                                   torch.zeros(lowercase_ ),
                                   torch.zeros(lowercase_ ),
                                   torch.zeros(lowercase_ ),
                                   torch.zeros(lowercase_ ),
                               ) )
             return common_inputs




      def   _A			( self		:       List[Any]		,      A		:       Optional[int]		,      A		:       Optional[int] = -1		,      A		:       Union[str, Any] = -1		,      A		:       Tuple = False		,      A		:       Dict = None		,      ):
             _UpperCAmelCase  :						List[str]							   =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_		,      lowercase_		,      lowercase_		,      lowercase_		,      lowercase_ )

             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  :						Optional[int]							   =					common_inputs["input_ids"].shape
                    _UpperCAmelCase  :						Any							   =					seqlen
                    _UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					self.num_layers
                    _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					self.num_attention_heads
                    _UpperCAmelCase  :						str							   =					(
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    _UpperCAmelCase  :						Tuple							   =					common_inputs["attention_mask"].dtype
                    _UpperCAmelCase  :						str							   =					torch.cat(
                        [common_inputs["attention_mask"], torch.ones(lowercase_		,      lowercase_		,      dtype=lowercase_ )]		,      dim=1 )
                    _UpperCAmelCase  :						Tuple							   =					[
                        (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
                    ]
             return common_inputs




      def   _A			( self		:       str		,      A		:       List[str]		,      A		:       int = -1		,      A		:       int = -1		,      A		:       Tuple = False		,      A		:       str = None		,      ):
             _UpperCAmelCase  :						Tuple							   =					compute_effective_axis_dimension(
                 lowercase_		,      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  :						Any							   =					tokenizer.num_special_tokens_to_add(lowercase_ )
             _UpperCAmelCase  :						List[Any]							   =					compute_effective_axis_dimension(
                 lowercase_		,      fixed_dimension=OnnxConfig.default_fixed_sequence		,      num_token_to_add=lowercase_ )

             # Generate dummy inputs according to compute batch and sequence
             _UpperCAmelCase  :						Dict							   =					[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
             _UpperCAmelCase  :						Tuple							   =					dict(tokenizer(lowercase_		,      return_tensors=lowercase_ ) )
             return common_inputs




      def   _A			( self		:       Optional[int]		,      A		:       Union[str, Any]		,      A		:       List[str] = -1		,      A		:       str = -1		,      A		:       List[str] = False		,      A		:       Any = None		,      ):






             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase  :						Optional[Any]							   =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        lowercase_		,      batch_size=lowercase_		,      seq_length=lowercase_		,      is_pair=lowercase_		,      framework=lowercase_ )

             elif self.task == "causal-lm":
                    _UpperCAmelCase  :						Tuple							   =					self._generate_dummy_inputs_for_causal_lm(
                        lowercase_		,      batch_size=lowercase_		,      seq_length=lowercase_		,      is_pair=lowercase_		,      framework=lowercase_ )
             else:
                    _UpperCAmelCase  :						Dict							   =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                        lowercase_		,      batch_size=lowercase_		,      seq_length=lowercase_		,      is_pair=lowercase_		,      framework=lowercase_ )

             return common_inputs




      def   _A			( self		:       str		,      A		:       Dict		,      A		:       Union[str, Any]		,      A		:       List[Any]		,      A		:       Optional[int] ):






             if self.task in ["default", "seq2seq-lm"]:
                    _UpperCAmelCase  :						Optional[Any]							   =					super()._flatten_past_key_values_(lowercase_		,      lowercase_		,      lowercase_		,      lowercase_ )
             else:
                    _UpperCAmelCase  :						Optional[Any]							   =					super(lowercase_		,      self )._flatten_past_key_values_(
                        lowercase_		,      lowercase_		,      lowercase_		,      lowercase_ )





      def   _A			( self		:       Optional[Any]		,      A		:       List[str]		,      A		:       int ):






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

             _UpperCAmelCase  :						int							   =					"past_key_values" if direction == "inputs" else "present"
             _UpperCAmelCase   ,						_UpperCAmelCase  :						Dict							   =					self.num_layers

             _UpperCAmelCase  :						List[str]							   =					"past_encoder_sequence"
             _UpperCAmelCase  :						List[str]							   =					"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

             for i in range(lowercase_ ):
                    _UpperCAmelCase  :						List[Any]							   =					{0: "batch", 2: decoder_sequence}
                    _UpperCAmelCase  :						Any							   =					{0: "batch", 2: decoder_sequence}
                    _UpperCAmelCase  :						Optional[int]							   =					{0: "batch", 2: encoder_sequence}
                    _UpperCAmelCase  :						Tuple							   =					{0: "batch", 2: encoder_sequence}


'''simple docstring'''




import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"    ,			[
        (Version("1.0.0"					), True),
        ("1.0.0", True),
        (Version("2.0.0"					), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]    ,			)
def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   List[Any]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					Version("1.0.0"					)
       assert (version == other) is expected_equality
       assert (version != other) is not expected_equality
       assert (hash(_UpperCAmelCase					) == hash(_UpperCAmelCase					)) is expected_equality

