
import warnings
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


lowercase  :					int						    =							logging.get_logger(__name__)

lowercase  :					List[str]						    =							{
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}

class __snake_case		(      __snake_case     ):
    _a		:						List[str]=				"bart"
    _a		:						Optional[Any]=				["past_key_values"]
    _a		:						Any=				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
    def __init__(  self		,snake_case=50265		,snake_case=1024		,snake_case=12		,snake_case=4096		,snake_case=16		,snake_case=12		,snake_case=4096		,snake_case=16		,snake_case=0.0		,snake_case=0.0		,snake_case="gelu"		,snake_case=1024		,snake_case=0.1		,snake_case=0.0		,snake_case=0.0		,snake_case=0.02		,snake_case=0.0		,snake_case=False		,snake_case=True		,snake_case=3		,snake_case=1		,snake_case=0		,snake_case=2		,snake_case=True		,snake_case=2		,snake_case=2		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Dict       	=      vocab_size
           lowercase							:     Any       	=      max_position_embeddings
           lowercase							:     Union[str, Any]       	=      d_model
           lowercase							:     str       	=      encoder_ffn_dim
           lowercase							:     Dict       	=      encoder_layers
           lowercase							:     str       	=      encoder_attention_heads
           lowercase							:     Union[str, Any]       	=      decoder_ffn_dim
           lowercase							:     Optional[Any]       	=      decoder_layers
           lowercase							:     List[Any]       	=      decoder_attention_heads
           lowercase							:     Optional[int]       	=      dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Optional[int]       	=      activation_dropout
           lowercase							:     Dict       	=      activation_function
           lowercase							:     List[str]       	=      init_std
           lowercase							:     Any       	=      encoder_layerdrop
           lowercase							:     List[Any]       	=      decoder_layerdrop
           lowercase							:     List[str]       	=      classifier_dropout
           lowercase							:     Any       	=      use_cache
           lowercase							:     Optional[Any]       	=      encoder_layers
           lowercase							:     Tuple       	=      scale_embedding  # scale factor will be sqrt(d_model) if True

           super().__init__(
               num_labels=snake_case		,pad_token_id=snake_case		,bos_token_id=snake_case		,eos_token_id=snake_case		,is_encoder_decoder=snake_case		,decoder_start_token_id=snake_case		,forced_eos_token_id=snake_case		,**snake_case		,)

           # ensure backward compatibility for BART CNN models
           if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""		,snake_case      ):
                  lowercase							:     Union[str, Any]       	=      self.bos_token_id
                  warnings.warn(
                      f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                      """The config can simply be saved and uploaded again to be fixed."""      )






class __snake_case		(      __snake_case     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.task in ["default", "seq2seq-lm"]:
                  lowercase							:     Dict       	=      OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]      )

                  if self.use_past:
                         lowercase							:     Optional[int]       	=      {0: '''batch'''}
                         lowercase							:     Optional[int]       	=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                  else:
                         lowercase							:     str       	=      {0: '''batch''', 1: '''decoder_sequence'''}
                         lowercase							:     Optional[int]       	=      {0: '''batch''', 1: '''decoder_sequence'''}

                  if self.use_past:
                         self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
           elif self.task == "causal-lm":
                  # TODO: figure this case out.
                  lowercase							:     List[Any]       	=      OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]      )
                  if self.use_past:
                         lowercase							:     Tuple       	=      self.num_layers
                         for i in range(snake_case      ):
                                lowercase							:     Optional[Any]       	=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                                lowercase							:     int       	=      {0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
                  lowercase							:     Tuple       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.task in ["default", "seq2seq-lm"]:
                  lowercase							:     str       	=      super().outputs
           else:
                  lowercase							:     Tuple       	=      super(snake_case		,self      ).outputs
                  if self.use_past:
                         lowercase							:     Any       	=      self.num_layers
                         for i in range(snake_case      ):
                                lowercase							:     List[Any]       	=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                                lowercase							:     List[str]       	=      {0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     int       	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case		,snake_case		,snake_case		,snake_case		,snake_case      )

           # Generate decoder inputs
           lowercase							:     Union[str, Any]       	=      seq_length if not self.use_past else 1
           lowercase							:     Union[str, Any]       	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case		,snake_case		,snake_case		,snake_case		,snake_case      )
           lowercase							:     Union[str, Any]       	=      {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
           lowercase							:     Union[str, Any]       	=      dict(**snake_case		,**snake_case      )

           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch
                  lowercase							:     Union[str, Any]       	=      common_inputs['''input_ids'''].shape
                  lowercase							:     Optional[Any]       	=      common_inputs['''decoder_input_ids'''].shape[1]
                  lowercase							:     str       	=      self.num_attention_heads
                  lowercase							:     int       	=      (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowercase							:     Tuple       	=      decoder_seq_length + 3
                  lowercase							:     List[Any]       	=      (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case		,snake_case      )]		,dim=1      )

                  lowercase							:     Tuple       	=      []
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  lowercase							:     Tuple       	=      self.num_layers
                  lowercase							:     Union[str, Any]       	=      min(snake_case		,snake_case      )
                  lowercase							:     Optional[Any]       	=      max(snake_case		,snake_case      ) - min_num_layers
                  lowercase							:     Any       	=      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

                  for _ in range(snake_case      ):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(snake_case      ),
                                 torch.zeros(snake_case      ),
                                 torch.zeros(snake_case      ),
                                 torch.zeros(snake_case      ),
                             )      )
                  # TODO: test this.
                  lowercase							:     List[str]       	=      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                  for _ in range(snake_case		,snake_case      ):
                         common_inputs["past_key_values"].append((torch.zeros(snake_case      ), torch.zeros(snake_case      ))      )
           return common_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case		,snake_case		,snake_case		,snake_case		,snake_case      )

           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch
                  lowercase							:     Optional[int]       	=      common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  lowercase							:     List[Any]       	=      seqlen + 2
                  lowercase							:     List[Any]       	=      self.num_layers
                  lowercase							:     Optional[Any]       	=      self.num_attention_heads
                  lowercase							:     Union[str, Any]       	=      (
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  lowercase							:     Union[str, Any]       	=      common_inputs['''attention_mask'''].dtype
                  lowercase							:     List[Any]       	=      torch.cat(
                      [common_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )
                  lowercase							:     Tuple       	=      [
                      (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(snake_case      )
                  ]
           return common_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      compute_effective_axis_dimension(
               snake_case		,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							:     int       	=      tokenizer.num_special_tokens_to_add(snake_case      )
           lowercase							:     int       	=      compute_effective_axis_dimension(
               snake_case		,fixed_dimension=OnnxConfig.default_fixed_sequence		,num_token_to_add=snake_case      )

           # Generate dummy inputs according to compute batch and sequence
           lowercase							:     Tuple       	=      [''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
           lowercase							:     List[Any]       	=      dict(tokenizer(snake_case		,return_tensors=snake_case      )      )
           return common_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           if self.task in ["default", "seq2seq-lm"]:
                  lowercase							:     Tuple       	=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           elif self.task == "causal-lm":
                  lowercase							:     int       	=      self._generate_dummy_inputs_for_causal_lm(
                      snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )
           else:
                  lowercase							:     Dict       	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                      snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           return common_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if self.task in ["default", "seq2seq-lm"]:
                  lowercase							:     List[str]       	=      super()._flatten_past_key_values_(snake_case		,snake_case		,snake_case		,snake_case      )
           else:
                  lowercase							:     Optional[Any]       	=      super(snake_case		,self      )._flatten_past_key_values_(
                      snake_case		,snake_case		,snake_case		,snake_case      )


'''simple docstring'''




import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


lowercase			:				Optional[int]    		=			{
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

lowercase			:				Optional[Any]    		=			{
    '169M': 7_68,
    '430M': 10_24,
    '1B5': 20_48,
    '3B': 25_60,
    '7B': 40_96,
    '14B': 51_20,
}







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						list(state_dict.keys()						)
    for name in state_dict_keys:
        A       :   str					=						state_dict.pop(snake_case__						)
        # emb -> embedding
        if name.startswith('''emb.'''						):
            A       :   Optional[Any]					=						name.replace('''emb.'''		,				'''embeddings.'''						)
        # ln_0 -> pre_ln (only present at block 0)
        if name.startswith('''blocks.0.ln0'''						):
            A       :   Union[str, Any]					=						name.replace('''blocks.0.ln0'''		,				'''blocks.0.pre_ln'''						)
        # att -> attention
        A       :   int					=						re.sub(R'''blocks\.(\d+)\.att'''		,				R'''blocks.\1.attention'''		,				snake_case__						)
        # ffn -> feed_forward
        A       :   List[Any]					=						re.sub(R'''blocks\.(\d+)\.ffn'''		,				R'''blocks.\1.feed_forward'''		,				snake_case__						)
        # time_mix_k -> time_mix_key and reshape
        if name.endswith('''.time_mix_k'''						):
            A       :   List[str]					=						name.replace('''.time_mix_k'''		,				'''.time_mix_key'''						)
        # time_mix_v -> time_mix_value and reshape
        if name.endswith('''.time_mix_v'''						):
            A       :   Union[str, Any]					=						name.replace('''.time_mix_v'''		,				'''.time_mix_value'''						)
        # time_mix_r -> time_mix_key and reshape
        if name.endswith('''.time_mix_r'''						):
            A       :   Union[str, Any]					=						name.replace('''.time_mix_r'''		,				'''.time_mix_receptance'''						)

        if name != "head.weight":
            A       :   List[Any]					=						'''rwkv.''' + name

        A       :   Dict					=						weight
    return state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=False		,				snake_case__=None						):




    '''simple docstring'''



    if tokenizer_file is None:
        print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''						)
        A       :   int					=						5_0277
        A       :   Optional[int]					=						AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''						)
    else:
        A       :   str					=						PreTrainedTokenizerFast(tokenizer_file=snake_case__						)
        A       :   Any					=						len(snake_case__						)
    tokenizer.save_pretrained(snake_case__						)

    # 2. Build the config
    A       :   List[str]					=						list(NUM_HIDDEN_LAYERS_MAPPING.keys()						)
    if size is None:
        # Try to infer size from the checkpoint name
        for candidate in possible_sizes:
            if candidate in checkpoint_file:
                A       :   List[str]					=						candidate
                break
        if size is None:
            raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''						)
    if size not in possible_sizes:
        raise ValueError(F'`size` should be one of {possible_sizes}, got {size}.'						)

    A       :   Any					=						RwkvConfig(
        vocab_size=snake_case__		,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]		,				hidden_size=HIDEN_SIZE_MAPPING[size]		,				)
    config.save_pretrained(snake_case__						)

    # 3. Download model file then convert state_dict
    A       :   Union[str, Any]					=						hf_hub_download(snake_case__		,				snake_case__						)
    A       :   Tuple					=						torch.load(snake_case__		,				map_location='''cpu'''						)
    A       :   List[Any]					=						convert_state_dict(snake_case__						)

    # 4. Split in shards and save
    A,  A       :   List[str]					=						shard_checkpoint(snake_case__						)
    for shard_file, shard in shards.items():
        torch.save(snake_case__		,				os.path.join(snake_case__		,				snake_case__						)						)

    if index is not None:
        A       :   Dict					=						os.path.join(snake_case__		,				snake_case__						)
        # Save the index as well
        with open(snake_case__		,				'''w'''		,				encoding='''utf-8'''						) as f:
            A       :   List[Any]					=						json.dumps(snake_case__		,				indent=2		,				sort_keys=snake_case__						) + '''\n'''
            f.write(snake_case__						)

        # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
        print(
            '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''						)
        A       :   List[Any]					=						list(shards.keys()						)

        del state_dict
        del shards
        gc.collect()

        for shard_file in shard_files:
            A       :   Union[str, Any]					=						torch.load(os.path.join(snake_case__		,				snake_case__						)						)
            torch.save({k: v.cpu().clone() for k, v in state_dict.items()}		,				os.path.join(snake_case__		,				snake_case__						)						)

    del state_dict
    gc.collect()

    if push_to_hub:
        if model_name is None:
            raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''						)
        A       :   int					=						AutoModelForCausalLM.from_pretrained(snake_case__						)
        model.push_to_hub(snake_case__		,				max_shard_size='''2GB'''						)
        tokenizer.push_to_hub(snake_case__						)


if __name__ == "__main__":
      lowercase			:				List[str]    		=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
      )
      parser.add_argument(
          '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
      )
      parser.add_argument(
          '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
      )
      parser.add_argument(
          '--tokenizer_file',
          default=None,
          type=str,
          help='Path to the tokenizer file to use (if not provided, only the model is converted).',
      )
      parser.add_argument(
          '--size',
          default=None,
          type=str,
          help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Push to the Hub the converted model.',
      )
      parser.add_argument(
          '--model_name',
          default=None,
          type=str,
          help='Name of the pushed model on the Hub, including the username / organization.',
      )

      lowercase			:				Union[str, Any]    		=			parser.parse_args()
      convert_rmkv_checkpoint_to_hf_format(
          args.repo_id,
          args.checkpoint_file,
          args.output_dir,
          size=args.size,
          tokenizer_file=args.tokenizer_file,
          push_to_hub=args.push_to_hub,
          model_name=args.model_name,
      )




