




import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Tuple							 =					RobertaTokenizer
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					RobertaTokenizerFast
				SCREAMING_SNAKE_CASE_       :							int							 =					True
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					{"""cls_token""": """<s>"""}





				def 							__UpperCAmelCase							(      self				:     str							)					->     Tuple:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a    =			[
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							a    =			dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase							)							)							)							)
							a    =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							a    =			{"unk_token": "<unk>"}

							a    =			os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["vocab_file"]							)
							a    =			os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["merges_file"]							)
							with open(self.vocab_file					,      "w"					,      encoding="utf-8"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							) + "\n"							)
							with open(self.merges_file					,      "w"					,      encoding="utf-8"							) as fp:
										fp.write("\n".join(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Any					,      **__lowerCamelCase				:     str							)					->     Any:
							kwargs.update(self.special_tokens_map							)
							return self.tokenizer_class.from_pretrained(self.tmpdirname					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      **__lowerCamelCase				:     List[Any]							)					->     List[Any]:
							kwargs.update(self.special_tokens_map							)
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     List[str]							)					->     Any:
							a    =			"lower newer"
							a    =			"lower newer"
							return input_text, output_text





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Optional[int]:
							a    =			self.tokenizer_class(self.vocab_file					,      self.merges_file					,      **self.special_tokens_map							)
							a    =			"lower newer"
							a    =			["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
							a    =			tokenizer.tokenize(__lowerCamelCase							)  # , add_prefix_space=True)
							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokens + [tokenizer.unk_token]
							a    =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							)					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Tuple:
							a    =			self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("Hello world!"					,      add_special_tokens=__lowerCamelCase							)					,      [0, 3_14_14, 2_32, 3_28, 2]							)
							self.assertListEqual(
							    tokenizer.encode("Hello world! cécé herlolip 418"					,      add_special_tokens=__lowerCamelCase							)					,      [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]					,      )





				@slow
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[Any]:
							a    =			self.tokenizer_class.from_pretrained("roberta-base"							)

							a    =			tokenizer.encode("sequence builders"					,      add_special_tokens=__lowerCamelCase							)
							a    =			tokenizer.encode("multi-sequence build"					,      add_special_tokens=__lowerCamelCase							)

							a    =			tokenizer.encode(
							    "sequence builders"					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)
							a    =			tokenizer.encode(
							    "sequence builders"					,      "multi-sequence build"					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)

							a    =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
							a    =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase					,      __lowerCamelCase							)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Union[str, Any]:
							a    =			self.get_tokenizer()

							a    =			"Encode this sequence."
							a    =			tokenizer.byte_encoder[" ".encode("utf-8"							)[0]]

							# Testing encoder arguments
							a    =			tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertNotEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertEqual(__lowerCamelCase					,      __lowerCamelCase							)

							tokenizer.add_special_tokens({"bos_token": "<s>"}							)
							a    =			tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
							self.assertNotEqual(__lowerCamelCase					,      __lowerCamelCase							)

							# Testing spaces after special tokens
							a    =			"<mask>"
							tokenizer.add_special_tokens(
							    {"mask_token": AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							)}							)  # mask token has a left space
							a    =			tokenizer.convert_tokens_to_ids(__lowerCamelCase							)

							a    =			"Encode <mask> sequence"
							a    =			"Encode <mask>sequence"

							a    =			tokenizer.encode(__lowerCamelCase							)
							a    =			encoded.index(__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokenizer.encode(__lowerCamelCase							)
							a    =			encoded.index(__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertNotEqual(__lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							pass





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Any:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a    =			self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase							)
													a    =			self.tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase							)
													a    =			"A, <mask> AllenNLP sentence."
													a    =			tokenizer_r.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase							)
													a    =			tokenizer_p.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase							)

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r["token_type_ids"]							)					,      sum(tokens_p["token_type_ids"]							)							)

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r["attention_mask"]							) / len(tokens_r["attention_mask"]							)					,      sum(tokens_p["attention_mask"]							) / len(tokens_p["attention_mask"]							)					,      )

													a    =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
													a    =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]							)

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p["input_ids"]					,      [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
													self.assertSequenceEqual(tokens_r["input_ids"]					,      [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

													self.assertSequenceEqual(
													    __lowerCamelCase					,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)
													self.assertSequenceEqual(
													    __lowerCamelCase					,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[int]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]					,      repeat=2							):
										a    =			self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)

										a    =			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
										a    =			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

										self.assertEqual(pre_tokenizer_state["add_prefix_space"]					,      __lowerCamelCase							)

										self.assertEqual(post_processor_state["add_prefix_space"]					,      __lowerCamelCase							)
										self.assertEqual(post_processor_state["trim_offsets"]					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a    =			"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													a    =			f"""{text_of_1_token} {text_of_1_token}"""

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			f""" {text}"""

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (1, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (1 + len(__lowerCamelCase							) + 1, 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )








'''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,
      )




