


"""simple docstring"""




import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf

if is_torch_available():
				import torch


lowercase_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a = None							, _a = None							, _a=None							, _a=None      ):
				if not conversation_id:
						__a           =			uuid.uuida()
				if past_user_inputs is None:
						__a           =			[]
				if generated_responses is None:
						__a           =			[]

				__a           =			conversation_id
				__a           =			past_user_inputs
				__a           =			generated_responses
				__a           =			text

		def __eq__(  self							, _a      ):
				if not isinstance(_a							, _a      ):
						return False
				if self.uuid == other.uuid:
						return True
				return (
				    self.new_user_input == other.new_user_input
				    and self.past_user_inputs == other.past_user_inputs
				    and self.generated_responses == other.generated_responses
				)

		def 			__UpperCAmelCase       (  self							, _a							, _a = False      ):
				if self.new_user_input:
						if overwrite:
								logger.warning(
								    f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
								    f'''with: "{text}".'''      )
								__a           =			text
						else:
								logger.warning(
								    f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
								    f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''      )
				else:
						__a           =			text

		def 			__UpperCAmelCase       (  self      ):
				if self.new_user_input:
						self.past_user_inputs.append(self.new_user_input      )
				__a           =			None

		def 			__UpperCAmelCase       (  self							, _a      ):
				self.generated_responses.append(_a      )

		def 			__UpperCAmelCase       (  self      ):
				for user_input, generated_response in zip(self.past_user_inputs							, self.generated_responses      ):
						yield True, user_input
						yield False, generated_response
				if self.new_user_input:
						yield True, self.new_user_input





		def __repr__(  self      ):
				__a           =			f'''Conversation id: {self.uuid} \n'''
				for is_user, text in self.iter_texts():
						__a           =			'''user''' if is_user else '''bot'''
						output += f'''{name} >> {text} \n'''
				return output







@add_end_docstrings(
    __SCREAMING_SNAKE_CASE     ,			r'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '     ,			)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, *_a							, **_a      ):
				super().__init__(*_a							, **_a      )
				if self.tokenizer.pad_token_id is None:
						__a           =			self.tokenizer.eos_token

		def 			__UpperCAmelCase       (  self							, _a=None							, _a=None							, _a=None							, **_a      ):
				__a           =			{}
				__a           =			{}
				__a           =			{}

				if min_length_for_response is not None:
						__a           =			min_length_for_response
				if minimum_tokens is not None:
						__a           =			minimum_tokens

				if "max_length" in generate_kwargs:
						__a           =			generate_kwargs['''max_length''']
						# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
				if clean_up_tokenization_spaces is not None:
						__a           =			clean_up_tokenization_spaces

				if generate_kwargs:
						forward_params.update(_a      )
				return preprocess_params, forward_params, postprocess_params

		def __call__(  self							, _a							, _a=0							, **_a      ):
				__a           =			super().__call__(_a							, num_workers=_a							, **_a      )
				if isinstance(_a							, _a      ) and len(_a      ) == 1:
						return outputs[0]
				return outputs

		def 			__UpperCAmelCase       (  self							, _a							, _a=32      ):
				if not isinstance(_a							, _a      ):
						raise ValueError('''ConversationalPipeline, expects Conversation as inputs'''      )
				if conversation.new_user_input is None:
						raise ValueError(
						    f'''Conversation with UUID {type(conversation.uuid      )} does not contain new user input to process. '''
						    '''Add user inputs with the conversation\'s `add_user_input` method'''      )
				if hasattr(self.tokenizer							, '''_build_conversation_input_ids'''      ):
						__a           =			self.tokenizer._build_conversation_input_ids(_a      )
				else:
						# If the tokenizer cannot handle conversations, we default to only the old version
						__a           =			self._legacy_parse_and_tokenize(_a      )

				if self.framework == "pt":
						__a           =			torch.LongTensor([input_ids]      )
				elif self.framework == "tf":
						__a           =			tf.constant([input_ids]      )
				return {"input_ids": input_ids, "conversation": conversation}

		def 			__UpperCAmelCase       (  self							, _a							, _a=10							, **_a      ):
				__a           =			generate_kwargs.get('''max_length'''							, self.model.config.max_length      )

				__a           =			model_inputs['''input_ids'''].shape[1]
				if max_length - minimum_tokens < n:
						logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''      )
						__a           =			max_length - minimum_tokens
						__a           =			model_inputs['''input_ids'''][:, -trim:]
						if "attention_mask" in model_inputs:
								__a           =			model_inputs['''attention_mask'''][:, -trim:]
				__a           =			model_inputs.pop('''conversation'''      )
				__a           =			max_length
				__a           =			self.model.generate(**_a							, **_a      )
				if self.model.config.is_encoder_decoder:
						__a           =			1
				else:
						__a           =			n
				return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

		def 			__UpperCAmelCase       (  self							, _a							, _a=True      ):
				__a           =			model_outputs['''output_ids''']
				__a           =			self.tokenizer.decode(
				    output_ids[0]							, skip_special_tokens=_a							, clean_up_tokenization_spaces=_a							, )
				__a           =			model_outputs['''conversation''']
				conversation.mark_processed()
				conversation.append_response(_a      )
				return conversation





		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.tokenizer.eos_token_id
				__a           =			[]
				for is_user, text in conversation.iter_texts():
						if eos_token_id is not None:
								input_ids.extend(self.tokenizer.encode(_a							, add_special_tokens=_a      ) + [eos_token_id]      )
						else:
								input_ids.extend(self.tokenizer.encode(_a							, add_special_tokens=_a      )      )

				if len(_a      ) > self.tokenizer.model_max_length:
						__a           =			input_ids[-self.tokenizer.model_max_length :]
				return input_ids














"""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


_snake_case          =   {
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

_snake_case          =   {
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	list(state_dict.keys()				)
  for name in state_dict_keys:
    _a  :       str             =	state_dict.pop(UpperCamelCase__				)
    # emb -> embedding
    if name.startswith("""emb."""				):
      _a  :       Dict             =	name.replace("""emb."""     ,   """embeddings."""				)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith("""blocks.0.ln0"""				):
      _a  :       Dict             =	name.replace("""blocks.0.ln0"""     ,   """blocks.0.pre_ln"""				)
    # att -> attention
    _a  :       Any             =	re.sub(R"""blocks\.(\d+)\.att"""     ,   R"""blocks.\1.attention"""     ,   UpperCamelCase__				)
    # ffn -> feed_forward
    _a  :       int             =	re.sub(R"""blocks\.(\d+)\.ffn"""     ,   R"""blocks.\1.feed_forward"""     ,   UpperCamelCase__				)
    # 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  :       Tuple             =	name.replace(""".time_mix_v"""     ,   """.time_mix_value"""				)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith(""".time_mix_r"""				):
      _a  :       Dict             =	name.replace(""".time_mix_r"""     ,   """.time_mix_receptance"""				)

    if name != "head.weight":
      _a  :       Optional[int]             =	"""rwkv.""" + name

    _a  :       Any             =	weight
  return state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=False     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
    _a  :       Tuple             =	5_0_2_7_7
    _a  :       str             =	AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
  else:
    _a  :       int             =	PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__				)
    _a  :       int             =	len(UpperCamelCase__				)
  tokenizer.save_pretrained(UpperCamelCase__				)

  # 2. Build the config
  _a  :       Optional[Any]             =	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  :       Tuple             =	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  :       List[Any]             =	RwkvConfig(
      vocab_size=UpperCamelCase__     ,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,   hidden_size=HIDEN_SIZE_MAPPING[size]     ,   )
  config.save_pretrained(UpperCamelCase__				)

  # 3. Download model file then convert state_dict
  _a  :       str             =	hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       int             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       List[str]             =	convert_state_dict(UpperCamelCase__				)

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

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

    # 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  :       Any             =	torch.load(os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}     ,   os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)

  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  :       Dict             =	AutoModelForCausalLM.from_pretrained(UpperCamelCase__				)
    model.push_to_hub(UpperCamelCase__     ,   max_shard_size="""2GB"""				)
    tokenizer.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
 _snake_case          =   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.',
 )

 _snake_case          =   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,
 )

