






'''simple docstring'''



import torch
from torch import nn
class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[str]=1  ,   UpperCAmelCase      :		Union[str, Any]=False		)	->	Dict:


       '''simple docstring'''

       super().__init__()

       lowercase       : str						=n_token
       lowercase       : int						=d_embed
       lowercase       : Tuple						=d_proj

       lowercase       : Union[str, Any]						=cutoffs + [n_token]
       lowercase       : Dict						=[0] + self.cutoffs
       lowercase       : Union[str, Any]						=div_val

       lowercase       : Union[str, Any]						=self.cutoffs[0]
       lowercase       : Any						=len(self.cutoffs		) - 1
       lowercase       : Dict						=self.shortlist_size + self.n_clusters

       if self.n_clusters > 0:
        lowercase       : Tuple						=nn.Parameter(torch.zeros(self.n_clusters  ,   self.d_embed		)		)
        lowercase       : Union[str, Any]						=nn.Parameter(torch.zeros(self.n_clusters		)		)

       lowercase       : Tuple						=nn.ModuleList()
       lowercase       : Optional[Any]						=nn.ParameterList()

       if div_val == 1:
        for i in range(len(self.cutoffs		)		):
         if d_proj != d_embed:
          self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCAmelCase  ,   UpperCAmelCase		)		)		)
         else:
          self.out_projs.append(UpperCAmelCase		)

        self.out_layers.append(nn.Linear(UpperCAmelCase  ,   UpperCAmelCase		)		)
       else:
        for i in range(len(self.cutoffs		)		):
         lowercase	,		lowercase       : Union[str, Any]						=self.cutoff_ends[i], self.cutoff_ends[i + 1]
         lowercase       : Optional[int]						=d_embed // (div_val**i)

         self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCAmelCase  ,   UpperCAmelCase		)		)		)

         self.out_layers.append(nn.Linear(UpperCAmelCase  ,   r_idx - l_idx		)		)

       lowercase       : int						=keep_order






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		List[str]		)	->	Optional[int]:


       '''simple docstring'''

       if proj is None:
        lowercase       : Optional[int]						=nn.functional.linear(UpperCAmelCase  ,   UpperCAmelCase  ,   bias=UpperCAmelCase		)
       else:
        # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
        lowercase       : List[Any]						=nn.functional.linear(UpperCAmelCase  ,   proj.t().contiguous()		)
        lowercase       : Optional[Any]						=nn.functional.linear(UpperCAmelCase  ,   UpperCAmelCase  ,   bias=UpperCAmelCase		)
        # else:
        #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
        #     if bias is not None:
        #         logit = logit + bias

       return logit






      def 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		Union[str, Any]=False		)	->	List[str]:


       '''simple docstring'''

       if labels is not None:
        # Shift so that tokens < n predict n
        lowercase       : int						=hidden[..., :-1, :].contiguous()
        lowercase       : Dict						=labels[..., 1:].contiguous()
        lowercase       : str						=hidden.view(-1  ,   hidden.size(-1		)		)
        lowercase       : List[Any]						=labels.view(-1		)
        if hidden.size(0		) != labels.size(0		):
         raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''		)
       else:
        lowercase       : Union[str, Any]						=hidden.view(-1  ,   hidden.size(-1		)		)

       if self.n_clusters == 0:
        lowercase       : Any						=self._compute_logit(UpperCAmelCase  ,   self.out_layers[0].weight  ,   self.out_layers[0].bias  ,   self.out_projs[0]		)
        if labels is not None:
         lowercase       : Optional[Any]						=labels != -100
         lowercase       : Optional[Any]						=torch.zeros_like(UpperCAmelCase  ,   dtype=hidden.dtype  ,   device=hidden.device		)
         lowercase       : str						=(
             -nn.functional.log_softmax(UpperCAmelCase  ,   dim=-1		)[mask].gather(1  ,   labels[mask].unsqueeze(1		)		).squeeze(1		)
         )
        else:
         lowercase       : int						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=-1		)
       else:
        # construct weights and biases
        lowercase	,		lowercase       : Optional[int]						=[], []
        for i in range(len(self.cutoffs		)		):
         if self.div_val == 1:
          lowercase	,		lowercase       : str						=self.cutoff_ends[i], self.cutoff_ends[i + 1]
          lowercase       : List[Any]						=self.out_layers[0].weight[l_idx:r_idx]
          lowercase       : Optional[int]						=self.out_layers[0].bias[l_idx:r_idx]
         else:
          lowercase       : int						=self.out_layers[i].weight
          lowercase       : str						=self.out_layers[i].bias

         if i == 0:
          lowercase       : int						=torch.cat([weight_i, self.cluster_weight]  ,   dim=0		)
          lowercase       : Tuple						=torch.cat([bias_i, self.cluster_bias]  ,   dim=0		)

         weights.append(UpperCAmelCase		)
         biases.append(UpperCAmelCase		)

        lowercase	,		lowercase	,		lowercase       : List[Any]						=weights[0], biases[0], self.out_projs[0]

        lowercase       : Any						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
        lowercase       : Tuple						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)

        if labels is None:
         lowercase       : Optional[Any]						=hidden.new_empty((head_logit.size(0		), self.n_token)		)
        else:
         lowercase       : List[str]						=torch.zeros_like(UpperCAmelCase  ,   dtype=hidden.dtype  ,   device=hidden.device		)

        lowercase       : Union[str, Any]						=0
        lowercase       : Optional[Any]						=[0] + self.cutoffs
        for i in range(len(UpperCAmelCase		) - 1		):
         lowercase	,		lowercase       : Union[str, Any]						=cutoff_values[i], cutoff_values[i + 1]

         if labels is not None:
          lowercase       : str						=(labels >= l_idx) & (labels < r_idx)
          lowercase       : Dict						=mask_i.nonzero().squeeze()

          if indices_i.numel() == 0:
           continue

          lowercase       : List[str]						=labels.index_select(0  ,   UpperCAmelCase		) - l_idx
          lowercase       : Optional[int]						=head_logprob.index_select(0  ,   UpperCAmelCase		)
          lowercase       : Union[str, Any]						=hidden.index_select(0  ,   UpperCAmelCase		)
         else:
          lowercase       : str						=hidden

         if i == 0:
          if labels is not None:
           lowercase       : Dict						=head_logprob_i.gather(1  ,   target_i[:, None]		).squeeze(1		)
          else:
           lowercase       : List[str]						=head_logprob[:, : self.cutoffs[0]]
         else:
          lowercase	,		lowercase	,		lowercase       : int						=weights[i], biases[i], self.out_projs[i]

          lowercase       : List[Any]						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
          lowercase       : List[Any]						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)
          lowercase       : Tuple						=self.cutoffs[0] + i - 1  # No probability for the head cluster
          if labels is not None:
           lowercase       : Any						=head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
               1  ,   target_i[:, None]		).squeeze(1		)
          else:
           lowercase       : List[str]						=head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
           lowercase       : Union[str, Any]						=logprob_i

         if labels is not None:
          if (hasattr(self  ,   '''keep_order'''		) and self.keep_order) or keep_order:
           out.index_copy_(0  ,   UpperCAmelCase  ,   -logprob_i		)
          else:
           out[offset : offset + logprob_i.size(0		)].copy_(-logprob_i		)
          offset += logprob_i.size(0		)

       return out






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		List[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       if self.n_clusters == 0:
        lowercase       : Optional[Any]						=self._compute_logit(UpperCAmelCase  ,   self.out_layers[0].weight  ,   self.out_layers[0].bias  ,   self.out_projs[0]		)
        return nn.functional.log_softmax(UpperCAmelCase  ,   dim=-1		)
       else:
        # construct weights and biases
        lowercase	,		lowercase       : Tuple						=[], []
        for i in range(len(self.cutoffs		)		):
         if self.div_val == 1:
          lowercase	,		lowercase       : Union[str, Any]						=self.cutoff_ends[i], self.cutoff_ends[i + 1]
          lowercase       : Tuple						=self.out_layers[0].weight[l_idx:r_idx]
          lowercase       : str						=self.out_layers[0].bias[l_idx:r_idx]
         else:
          lowercase       : Dict						=self.out_layers[i].weight
          lowercase       : Optional[Any]						=self.out_layers[i].bias

         if i == 0:
          lowercase       : Tuple						=torch.cat([weight_i, self.cluster_weight]  ,   dim=0		)
          lowercase       : Union[str, Any]						=torch.cat([bias_i, self.cluster_bias]  ,   dim=0		)

         weights.append(UpperCAmelCase		)
         biases.append(UpperCAmelCase		)

        lowercase	,		lowercase	,		lowercase       : List[Any]						=weights[0], biases[0], self.out_projs[0]
        lowercase       : List[Any]						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)

        lowercase       : List[Any]						=hidden.new_empty((head_logit.size(0		), self.n_token)		)
        lowercase       : int						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)

        lowercase       : Tuple						=[0] + self.cutoffs
        for i in range(len(UpperCAmelCase		) - 1		):
         lowercase	,		lowercase       : Any						=cutoff_values[i], cutoff_values[i + 1]

         if i == 0:
          lowercase       : Any						=head_logprob[:, : self.cutoffs[0]]
         else:
          lowercase	,		lowercase	,		lowercase       : Any						=weights[i], biases[i], self.out_projs[i]

          lowercase       : List[str]						=self._compute_logit(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
          lowercase       : List[str]						=nn.functional.log_softmax(UpperCAmelCase  ,   dim=1		)

          lowercase       : Tuple						=head_logprob[:, -i] + tail_logprob_i
          lowercase       : Optional[int]						=logprob_i

        return out











'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=6.0  ,   UpperCAmelCase      :		Any=None  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		Tuple="fp4"  ,   UpperCAmelCase      :		str=False  ,   **UpperCAmelCase      :		Union[str, Any]  ,   )	->	str:


       '''simple docstring'''

       lowercase       : Optional[int]						=load_in_abit
       lowercase       : Union[str, Any]						=load_in_abit
       lowercase       : Tuple						=llm_inta_threshold
       lowercase       : Optional[Any]						=llm_inta_skip_modules
       lowercase       : int						=llm_inta_enable_fpaa_cpu_offload
       lowercase       : Dict						=llm_inta_has_fpaa_weight
       lowercase       : str						=bnb_abit_quant_type
       lowercase       : int						=bnb_abit_use_double_quant

       if bnb_abit_compute_dtype is None:
        lowercase       : str						=torch.floataa
       elif isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
        lowercase       : Tuple						=getattr(UpperCAmelCase  ,   UpperCAmelCase		)
       elif isinstance(UpperCAmelCase  ,   torch.dtype		):
        lowercase       : Optional[int]						=bnb_abit_compute_dtype
       else:
        raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype'''		)

       self.post_init()






      def 					A__   (			self      :		List[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       if not isinstance(self.llm_inta_threshold  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_threshold must be a float'''		)

       if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_skip_modules must be a list of strings'''		)
       if not isinstance(self.llm_inta_enable_fpaa_cpu_offload  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean'''		)

       if not isinstance(self.llm_inta_has_fpaa_weight  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_has_fp16_weight must be a boolean'''		)

       if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype  ,   torch.dtype		):
        raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype'''		)

       if not isinstance(self.bnb_abit_quant_type  ,   UpperCAmelCase		):
        raise ValueError('''bnb_4bit_quant_type must be a string'''		)

       if not isinstance(self.bnb_abit_use_double_quant  ,   UpperCAmelCase		):
        raise ValueError('''bnb_4bit_use_double_quant must be a boolean'''		)

       if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes'''		)		) >= version.parse(
           '''0.39.0'''		):
        raise ValueError(
            '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'''		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       return self.load_in_abit or self.load_in_abit






      def 					A__   (			self      :		Tuple		)	->	Any:


       '''simple docstring'''

       if self.load_in_abit:
        return "llm_int8"
       elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
        return "fp4"
       elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
        return "nf4"
       else:
        return None






      @classmethod
      def 					A__   (			cls      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Any  ,   **UpperCAmelCase      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Dict						=cls(**UpperCAmelCase		)

       lowercase       : Dict						=[]
       for key, value in kwargs.items():
        if hasattr(UpperCAmelCase  ,   UpperCAmelCase		):
         setattr(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
         to_remove.append(UpperCAmelCase		)
       for key in to_remove:
        kwargs.pop(UpperCAmelCase  ,   UpperCAmelCase		)

       if return_unused_kwargs:
        return config, kwargs
       else:
        return config






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, os.PathLike]		)	->	Dict:


       '''simple docstring'''

       with open(UpperCAmelCase  ,   '''w'''  ,   encoding='''utf-8'''		) as writer:
        lowercase       : Any						=self.to_dict()
        lowercase       : List[Any]						=json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + '''\n'''

        writer.write(UpperCAmelCase		)






      def 					A__   (			self      :		Optional[Any]		)	->	Dict[str, Any]:


       '''simple docstring'''

       lowercase       : Tuple						=copy.deepcopy(self.__dict__		)
       lowercase       : Optional[Any]						=str(output['''bnb_4bit_compute_dtype''']		).split('''.'''		)[1]

       return output






      def __repr__(			self      :		Optional[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       return f'{self.__class__.__name__} {self.to_json_string()}'






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		bool = True		)	->	str:


       '''simple docstring'''

       if use_diff is True:
        lowercase       : int						=self.to_diff_dict()
       else:
        lowercase       : List[str]						=self.to_dict()
       return json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + "\n"






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


       '''simple docstring'''

       lowercase       : Any						=self.to_dict()

       # get the default config dict
       lowercase       : Union[str, Any]						=BitsAndBytesConfig().to_dict()

       lowercase       : int						={}

       # only serialize values that differ from the default config
       for key, value in config_dict.items():
        if value != default_config_dict[key]:
         lowercase       : Dict						=value

       return serializable_config_dict



