





'''simple docstring'''


import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[int]			,__lowerCamelCase      :      Union[str, Any]			,__lowerCamelCase      :      List[Any]			,__lowerCamelCase      :      str=None				):
   lowercase_    :List[Any]					    =				None
   # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
   if str(weight.dtype				) == "torch.bfloat16":
      # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
      lowercase_    :int					    =				weight.view(torch.intaa				)
      lowercase_    :List[Any]					    =				"bfloat16"
   lowercase_    :Optional[Any]					    =				weight.cpu().numpy()
   lowercase_    :Optional[int]					    =				os.path.join(__lowerCamelCase			,F'{weight_name}.dat'				)
   if index is not None:
      if dtype is None:
         lowercase_    :str					    =				str(array.dtype				)
      lowercase_    :int					    =				{"dtype": dtype, "shape": list(array.shape				)}
   if array.ndim == 0:
      lowercase_    :int					    =				array[None]
   lowercase_    :List[Any]					    =				np.memmap(__lowerCamelCase			,dtype=array.dtype			,mode="w+"			,shape=array.shape				)
   lowercase_    :List[Any]					    =				array[:]
   file_array.flush()
   return index
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int			,__lowerCamelCase      :      Dict				):
   lowercase_    :int					    =				tuple(weight_info["shape"]				)
   if shape == ():
      # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
      lowercase_    :List[str]					    =				(1,)

   lowercase_    :List[str]					    =				weight_info["dtype"]
   if dtype == "bfloat16":
      # NumPy does not support bfloat16 so this was saved as a int16
      lowercase_    :Optional[Any]					    =				"int16"

   lowercase_    :str					    =				np.memmap(__lowerCamelCase			,dtype=__lowerCamelCase			,shape=__lowerCamelCase			,mode="r"				)

   if len(weight_info["shape"]				) == 0:
      lowercase_    :Union[str, Any]					    =				weight[0]
   lowercase_    :str					    =				torch.tensor(__lowerCamelCase				)
   if weight_info["dtype"] == "bfloat16":
      lowercase_    :Any					    =				weight.view(torch.bfloataa				)

   return weight
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict			,__lowerCamelCase      :      List[str]				):
   if index is None or len(__lowerCamelCase				) == 0:
      # Nothing to save
      return

   lowercase_    :List[Any]					    =				os.path.join(__lowerCamelCase			,"index.json"				)
   if os.path.isfile(__lowerCamelCase				):
      with open(__lowerCamelCase			,"r"			,encoding="utf-8"				) as f:
         lowercase_    :Any					    =				json.load(__lowerCamelCase				)
   else:
      lowercase_    :Union[str, Any]					    =				{}
   current_index.update(__lowerCamelCase				)

   with open(__lowerCamelCase			,"w"			,encoding="utf-8"				) as f:
      json.dump(__lowerCamelCase			,__lowerCamelCase			,indent=2				)
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Union[str, os.PathLike]			,__lowerCamelCase      :      Dict[str, torch.Tensor]				):
   os.makedirs(__lowerCamelCase			,exist_ok=__lowerCamelCase				)
   lowercase_    :List[Any]					    =				{}
   for name, parameter in state_dict.items():
      lowercase_    :Tuple					    =				offload_weight(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,index=__lowerCamelCase				)

   # Update index
   save_offload_index(__lowerCamelCase			,__lowerCamelCase				)
class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      int			,   lowercase	:      Mapping			,   lowercase	:      str		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				dataset
        lowercase_    :List[str]					    =				prefix





     def __getitem__(				self	:      Any			,   lowercase	:      List[str]		):





        """simple docstring"""




        return self.dataset[F'{self.prefix}{key}']





     def __iter__(				self	:      Optional[int]		):





        """simple docstring"""




        return iter([key for key in self.dataset if key.startswith(self.prefix		)]		)







     def __len__(				self	:      Any		):





        """simple docstring"""




        return len(self.dataset		)







class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      Tuple			,   lowercase	:      Dict[str, torch.Tensor] = None			,   lowercase	:      Optional[Union[str, os.PathLike]] = None			,   lowercase	:      Mapping = None			,   lowercase	:      Optional[Any]=None			,   ):





        """simple docstring"""




        if state_dict is None and save_folder is None:
           raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."		)

        lowercase_    :Optional[int]					    =				{} if state_dict is None else state_dict
        lowercase_    :str					    =				save_folder
        if index is None and save_folder is not None:
           with open(os.path.join(lowercase			,   "index.json"		)		) as f:
              lowercase_    :Any					    =				json.load(lowercase		)
        lowercase_    :str					    =				{} if index is None else index
        lowercase_    :List[str]					    =				list(self.state_dict.keys()		)
        self.all_keys.extend([key for key in self.index if key not in self.all_keys]		)
        lowercase_    :List[Any]					    =				device





     def __getitem__(				self	:      Any			,   lowercase	:      str		):





        """simple docstring"""




        if key in self.state_dict:
           return self.state_dict[key]
        lowercase_    :int					    =				self.index[key]
        if weight_info.get("safetensors_file"		) is not None:
           if not is_safetensors_available():
              raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`."		)

           from safetensors import safe_open

           lowercase_    :Dict					    =				"cpu" if self.device is None else self.device
           with safe_open(weight_info["safetensors_file"]			,   framework="pt"			,   device=lowercase		) as f:
              lowercase_    :Tuple					    =				f.get_tensor(weight_info.get("weight_name"			,   lowercase		)		)

           if "dtype" in weight_info:
              return tensor.to(getattr(lowercase			,   weight_info["dtype"]		)		)
           else:
              return tensor

        lowercase_    :str					    =				os.path.join(self.save_folder			,   F'{key}.dat'		)
        return load_offloaded_weight(lowercase			,   lowercase		)





     def __iter__(				self	:      Optional[int]		):





        """simple docstring"""




        return iter(self.all_keys		)







     def __len__(				self	:      Tuple		):





        """simple docstring"""




        return len(self.all_keys		)







def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict[str, torch.Tensor]			,__lowerCamelCase      :      List[str]				):
   lowercase_    :List[str]					    =				{}
   for module_name in submodule_names:
      # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
      # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
      result.update(
          {
              key: param
              for key, param in state_dict.items()
              if key == module_name or key.startswith(module_name + "."				)
          }				)
   return result









'''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


lowerCAmelCase      :  str      							=logging.get_logger(__name__)

lowerCAmelCase      :  Union[str, Any]      							={
    '''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    a_     (       _lowerCAmelCase							):
     __A  				=    "blenderbot"
     __A  				=    ["past_key_values"]
     __A  				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(				self	:      Dict			,   lowercase	:      List[Any]=8_008			,   lowercase	:      List[str]=128			,   lowercase	:      Optional[int]=2			,   lowercase	:      Optional[int]=10_240			,   lowercase	:      Tuple=32			,   lowercase	:      int=24			,   lowercase	:      Dict=10_240			,   lowercase	:      Any=32			,   lowercase	:      Optional[int]=0.0			,   lowercase	:      Dict=0.0			,   lowercase	:      int=True			,   lowercase	:      Tuple=True			,   lowercase	:      List[str]="gelu"			,   lowercase	:      int=2_560			,   lowercase	:      Optional[Any]=0.1			,   lowercase	:      Any=0.0			,   lowercase	:      List[str]=0.0			,   lowercase	:      Optional[Any]=0.02			,   lowercase	:      Tuple=1			,   lowercase	:      Union[str, Any]=False			,   lowercase	:      str=0			,   lowercase	:      List[str]=1			,   lowercase	:      Optional[Any]=2			,   lowercase	:      Union[str, Any]=3			,   lowercase	:      List[str]=2			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				vocab_size
        lowercase_    :Any					    =				max_position_embeddings
        lowercase_    :List[Any]					    =				d_model
        lowercase_    :Union[str, Any]					    =				encoder_ffn_dim
        lowercase_    :str					    =				encoder_layers
        lowercase_    :Optional[int]					    =				encoder_attention_heads
        lowercase_    :Dict					    =				decoder_ffn_dim
        lowercase_    :str					    =				decoder_layers
        lowercase_    :int					    =				decoder_attention_heads
        lowercase_    :Union[str, Any]					    =				dropout
        lowercase_    :int					    =				attention_dropout
        lowercase_    :int					    =				activation_dropout
        lowercase_    :str					    =				activation_function
        lowercase_    :Tuple					    =				init_std
        lowercase_    :Any					    =				encoder_layerdrop
        lowercase_    :Dict					    =				decoder_layerdrop
        lowercase_    :int					    =				use_cache
        lowercase_    :Dict					    =				encoder_layers
        lowercase_    :Dict					    =				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    a_     (       _lowerCAmelCase							):





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





        """simple docstring"""




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





        """simple docstring"""




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





     def 							lowercase__   (				self	:      Tuple			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :int					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        # Generate decoder inputs
        lowercase_    :List[Any]					    =				seq_length if not self.use_past else 1
        lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        lowercase_    :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
           lowercase_    ,							lowercase_    :int					    =				common_inputs["input_ids"].shape
           lowercase_    :Tuple					    =				common_inputs["decoder_input_ids"].shape[1]
           lowercase_    ,							lowercase_    :Tuple					    =				self.num_attention_heads
           lowercase_    :List[Any]					    =				(
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase_    :List[Any]					    =				decoder_seq_length
           lowercase_    :str					    =				(
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )
           lowercase_    :Optional[int]					    =				torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(lowercase			,   lowercase		)]			,   dim=1		)
           lowercase_    :int					    =				[]
           lowercase_    ,							lowercase_    :Tuple					    =				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 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				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
        lowercase_    :Optional[Any]					    =				tokenizer.num_special_tokens_to_add(lowercase		)
        lowercase_    :int					    =				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
        lowercase_    :int					    =				[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
        lowercase_    :Dict					    =				dict(tokenizer(lowercase			,   return_tensors=lowercase		)		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :str					    =				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":
           lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_causal_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)
        else:
           lowercase_    :List[str]					    =				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 							lowercase__   (				self	:      Optional[int]			,   lowercase	:      str			,   lowercase	:      Union[str, Any]			,   lowercase	:      int			,   lowercase	:      List[Any]		):





        """simple docstring"""




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







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Mapping[str, Mapping[int, str]]			,   lowercase	:      str		):





        """simple docstring"""




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

        lowercase_    :Any					    =				"past_key_values" if direction == "inputs" else "present"
        lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers

        lowercase_    :str					    =				"past_encoder_sequence"
        lowercase_    :Tuple					    =				"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(lowercase		):
           lowercase_    :Dict					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: encoder_sequence}
           lowercase_    :int					    =				{0: "batch", 2: encoder_sequence}


