

import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 	__magic_name__				(       nn.Linear							):






     def __init__(							self		,	_lowercase		,	_lowercase		,	_lowercase = True		,	_lowercase = "constant"		,	_lowercase = 0.0		,	_lowercase = "topK"		,	)->    Optional[Any]:
         super(_lowercase		,	self  ).__init__(in_features=_lowercase		,	out_features=_lowercase		,	bias=_lowercase  )
         assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
         UpperCamelCase_      =	pruning_method

         if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
             UpperCamelCase_      =	mask_scale
             UpperCamelCase_      =	mask_init
             UpperCamelCase_      =	nn.Parameter(torch.empty(self.weight.size()  )  )
             self.init_mask()






     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         if self.mask_init == "constant":
             init.constant_(self.mask_scores		,	val=self.mask_scale  )
         elif self.mask_init == "uniform":
             init.uniform_(self.mask_scores		,	a=-self.mask_scale		,	b=self.mask_scale  )
         elif self.mask_init == "kaiming":
             init.kaiming_uniform_(self.mask_scores		,	a=math.sqrt(5  )  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    Any:
         # Get the mask
         if self.pruning_method == "topK":
             UpperCamelCase_      =	TopKBinarizer.apply(self.mask_scores		,	_lowercase  )
         elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
             UpperCamelCase_      =	"sigmoied" in self.pruning_method
             UpperCamelCase_      =	ThresholdBinarizer.apply(self.mask_scores		,	_lowercase		,	_lowercase  )
         elif self.pruning_method == "magnitude":
             UpperCamelCase_      =	MagnitudeBinarizer.apply(self.weight		,	_lowercase  )
         elif self.pruning_method == "l0":
             UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	-0.1, 1.1, 2 / 3
             if self.training:
                 UpperCamelCase_      =	torch.zeros_like(self.mask_scores  ).uniform_().clamp(0.0_001		,	0.9_999  )
                 UpperCamelCase_      =	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b  )
             else:
                 UpperCamelCase_      =	torch.sigmoid(self.mask_scores  )
             UpperCamelCase_      =	s * (r - l) + l
             UpperCamelCase_      =	s_bar.clamp(min=0.0		,	max=1.0  )
         # Mask weights with computed mask
         UpperCamelCase_      =	mask * self.weight
         # Compute output (linear layer) with masked weights
         return nn.functional.linear(_lowercase		,	_lowercase		,	self.bias  )



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		:Any				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict				  =    {
    """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 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :str								=			"""blenderbot"""
     UpperCamelCase_  :Union[str, Any]								=			["""past_key_values"""]
     UpperCamelCase_  :Any								=			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






     def __init__(							self		,	_lowercase=8_008		,	_lowercase=128		,	_lowercase=2		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=24		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=True		,	_lowercase=True		,	_lowercase="gelu"		,	_lowercase=2_560		,	_lowercase=0.1		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=0.02		,	_lowercase=1		,	_lowercase=False		,	_lowercase=0		,	_lowercase=1		,	_lowercase=2		,	_lowercase=3		,	_lowercase=2		,	**_lowercase		,	)->    List[str]:
         UpperCamelCase_      =	vocab_size
         UpperCamelCase_      =	max_position_embeddings
         UpperCamelCase_      =	d_model
         UpperCamelCase_      =	encoder_ffn_dim
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	encoder_attention_heads
         UpperCamelCase_      =	decoder_ffn_dim
         UpperCamelCase_      =	decoder_layers
         UpperCamelCase_      =	decoder_attention_heads
         UpperCamelCase_      =	dropout
         UpperCamelCase_      =	attention_dropout
         UpperCamelCase_      =	activation_dropout
         UpperCamelCase_      =	activation_function
         UpperCamelCase_      =	init_std
         UpperCamelCase_      =	encoder_layerdrop
         UpperCamelCase_      =	decoder_layerdrop
         UpperCamelCase_      =	use_cache
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	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 	__magic_name__				(       snake_case							):






     @property
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_      =	{0: "batch"}
                 UpperCamelCase_      =	{0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                 UpperCamelCase_      =	{0: "batch", 1: "decoder_sequence"}
                 UpperCamelCase_      =	{0: "batch", 1: "decoder_sequence"}
             if self.use_past:
                 self.fill_with_past_key_values_(_lowercase		,	direction="inputs"  )
         elif self.task == "causal-lm":
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         else:
             UpperCamelCase_      =	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   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super().outputs
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  ).outputs
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         # Generate decoder inputs
         UpperCamelCase_      =	seq_length if not self.use_past else 1
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         UpperCamelCase_      =	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_      =	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_      =	common_inputs["input_ids"].shape
             UpperCamelCase_      =	common_inputs["decoder_input_ids"].shape[1]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	decoder_seq_length
             UpperCamelCase_      =	(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["decoder_attention_mask"], torch.ones(_lowercase		,	_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[]
             UpperCamelCase_     ,					UpperCamelCase_      =	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   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	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_      =	common_inputs["input_ids"].shape
             UpperCamelCase_      =	seqlen
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	common_inputs["attention_mask"].dtype
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["attention_mask"], torch.ones(_lowercase		,	_lowercase		,	dtype=_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[
                 (torch.zeros(_lowercase  ), torch.zeros(_lowercase  )) for _ in range(_lowercase  )
             ]
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         # 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_      =	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_      =	tokenizer.num_special_tokens_to_add(_lowercase  )
         UpperCamelCase_      =	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_      =	[" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
         UpperCamelCase_      =	dict(tokenizer(_lowercase		,	return_tensors=_lowercase  )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	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_      =	self._generate_dummy_inputs_for_causal_lm(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )
         else:
             UpperCamelCase_      =	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   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    List[Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super()._flatten_past_key_values_(_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  )._flatten_past_key_values_(
                 _lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    Union[str, Any]:
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"  )

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

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

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