


'''simple docstring'''


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  """▁"""

_UpperCamelCase 							=  {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_UpperCamelCase 							=  {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

_UpperCamelCase 							=  {
    """facebook/m2m100_418M""": 1024,
}

# fmt: off
_UpperCamelCase 							=  {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				VOCAB_FILES_NAMES
     lowerCamelCase__       						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__       						=				PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__       						=				['input_ids', 'attention_mask']

     lowerCamelCase__       						=				[]
     lowerCamelCase__       						=				[]


     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase="<s>"			,     lowerCamelCase="</s>"			,     lowerCamelCase="</s>"			,     lowerCamelCase="<pad>"			,     lowerCamelCase="<unk>"			,     lowerCamelCase="m2m100"			,     lowerCamelCase = None			,     lowerCamelCase=8			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[int]      =							{} if sp_model_kwargs is None else sp_model_kwargs

          __A	:       Tuple      =							language_codes
          __A	:       str      =							FAIRSEQ_LANGUAGE_CODES[language_codes]
          __A	:       Any      =							{lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}

          __A	:       Tuple      =							kwargs.get("additional_special_tokens"			,     []		)
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(lowerCamelCase		)
              for lang_code in fairseq_language_code
              if self.get_lang_token(lowerCamelCase		) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=lowerCamelCase			,     tgt_lang=lowerCamelCase			,     bos_token=lowerCamelCase			,     eos_token=lowerCamelCase			,     sep_token=lowerCamelCase			,     unk_token=lowerCamelCase			,     pad_token=lowerCamelCase			,     language_codes=lowerCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     num_madeup_words=lowerCamelCase			,     **lowerCamelCase			,     )

          __A	:       int      =							vocab_file
          __A	:       Dict      =							load_json(lowerCamelCase		)
          __A	:       Union[str, Any]      =							{v: k for k, v in self.encoder.items()}
          __A	:       Tuple      =							spm_file
          __A	:       Any      =							load_spm(lowerCamelCase			,     self.sp_model_kwargs		)

          __A	:       Any      =							len(self.encoder		)

          __A	:       List[Any]      =							{
              self.get_lang_token(lowerCamelCase		): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase		)
          }
          __A	:       List[str]      =							{lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase		)}
          __A	:       Tuple      =							{v: k for k, v in self.lang_token_to_id.items()}

          __A	:       List[Any]      =							src_lang if src_lang is not None else "en"
          __A	:       int      =							tgt_lang
          __A	:       Optional[int]      =							self.get_lang_id(self._src_lang		)
          self.set_src_lang_special_tokens(self._src_lang		)

          __A	:       List[Any]      =							num_madeup_words


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return len(self.encoder		) + len(self.lang_token_to_id		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return self._src_lang


     @src_lang.setter
     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       str      =							new_src_lang
          self.set_src_lang_special_tokens(self._src_lang		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          return self.sp_model.encode(lowerCamelCase			,     out_type=lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          if token in self.lang_token_to_id:
               return self.lang_token_to_id[token]
          return self.encoder.get(lowerCamelCase			,     self.encoder[self.unk_token]		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          if index in self.id_to_lang_token:
               return self.id_to_lang_token[index]
          return self.decoder.get(lowerCamelCase			,     self.unk_token		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Tuple      =							[]
          __A	:       Dict      =							""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(lowerCamelCase		) + token
                    __A	:       Tuple      =							[]
               else:
                    current_sub_tokens.append(lowerCamelCase		)
          out_string += self.sp_model.decode(lowerCamelCase		)
          return out_string.strip()


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = False		):


          '''simple docstring'''





          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase			,     token_ids_a=lowerCamelCase			,     already_has_special_tokens=lowerCamelCase		)

          __A	:       int      =							[1] * len(self.prefix_tokens		)
          __A	:       Union[str, Any]      =							[1] * len(self.suffix_tokens		)
          if token_ids_a is None:
               return prefix_ones + ([0] * len(lowerCamelCase		)) + suffix_ones
          return prefix_ones + ([0] * len(lowerCamelCase		)) + ([0] * len(lowerCamelCase		)) + suffix_ones


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							{self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab


     def __getstate__(   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.__dict__.copy()
          __A	:       Any      =							None
          return state


     def __setstate__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Dict      =							d

          # for backward compatibility
          if not hasattr(self			,     "sp_model_kwargs"		):
               __A	:       str      =							{}

          __A	:       int      =							load_spm(self.spm_file			,     self.sp_model_kwargs		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							Path(lowerCamelCase		)
          if not save_dir.is_dir():
               raise OSError(f"{save_directory} should be a directory"		)
          __A	:       str      =							save_dir / (
              (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
          )
          __A	:       Optional[Any]      =							save_dir / (
              (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
          )

          save_json(self.encoder			,     lowerCamelCase		)

          if os.path.abspath(self.spm_file		) != os.path.abspath(lowerCamelCase		) and os.path.isfile(self.spm_file		):
               copyfile(self.spm_file			,     lowerCamelCase		)
          elif not os.path.isfile(self.spm_file		):
               with open(lowerCamelCase			,     "wb"		) as fi:
                    __A	:       Tuple      =							self.sp_model.serialized_model_proto()
                    fi.write(lowerCamelCase		)

          return (str(lowerCamelCase		), str(lowerCamelCase		))


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = "en"			,     lowerCamelCase = None			,     lowerCamelCase = "ro"			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[int]      =							src_lang
          __A	:       Tuple      =							tgt_lang
          self.set_src_lang_special_tokens(self.src_lang		)
          return super().prepare_seqaseq_batch(lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		):


          '''simple docstring'''





          if src_lang is None or tgt_lang is None:
               raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"		)
          __A	:       str      =							src_lang
          __A	:       Tuple      =							self(lowerCamelCase			,     add_special_tokens=lowerCamelCase			,     **lowerCamelCase		)
          __A	:       Optional[Any]      =							self.get_lang_id(lowerCamelCase		)
          __A	:       Tuple      =							tgt_lang_id
          return inputs


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.set_src_lang_special_tokens(self.src_lang		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.set_tgt_lang_special_tokens(self.tgt_lang		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							self.get_lang_token(lowerCamelCase		)
          __A	:       str      =							self.lang_token_to_id[lang_token]
          __A	:       Union[str, Any]      =							[self.cur_lang_id]
          __A	:       Tuple      =							[self.eos_token_id]


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[str]      =							self.get_lang_token(lowerCamelCase		)
          __A	:       Optional[int]      =							self.lang_token_to_id[lang_token]
          __A	:       Optional[int]      =							[self.cur_lang_id]
          __A	:       List[Any]      =							[self.eos_token_id]


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          return self.lang_code_to_token[lang]


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.get_lang_token(lowerCamelCase		)
          return self.lang_token_to_id[lang_token]


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE							)
     spm.Load(str(SCREAMING_SNAKE_CASE							)							)
     return spm


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE       ,					"r"							) as f:
          return json.load(SCREAMING_SNAKE_CASE							)






def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					indent=2							)





'''simple docstring'''


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if hor == 128:
          __A	:       int      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Union[str, Any]      =							(32, 128, 256)
          __A	:       str      =							("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          __A	:       List[Any]      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Optional[Any]      =							(32, 64, 128, 256)
          __A	:       Dict      =							("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     __A	:       Union[str, Any]      =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
     __A	:       List[str]      =							model.state_dict()
     __A	:       Any      =							{
         "down_block_types": down_block_types,
         "block_out_channels": block_out_channels,
         "up_block_types": up_block_types,
         "layers_per_block": 1,
         "use_timestep_embedding": True,
         "out_block_type": "OutConv1DBlock",
         "norm_num_groups": 8,
         "downsample_each_block": False,
         "in_channels": 14,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "sample_size": 6_5536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)
     __A	:       Dict      =							dict(zip(model.state_dict().keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       Optional[int]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"							)
     with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							{
         "in_channels": 14,
         "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
         "up_block_types": (),
         "out_block_type": "ValueFunction",
         "mid_block_type": "ValueFunctionMidBlock1D",
         "block_out_channels": (32, 64, 128, 256),
         "layers_per_block": 1,
         "downsample_each_block": True,
         "sample_size": 6_5536,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "use_timestep_embedding": True,
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "norm_num_groups": 8,
         "act_fn": "mish",
     }

     __A	:       Optional[int]      =							torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
     __A	:       Dict      =							model
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)

     __A	:       int      =							dict(zip(state_dict.keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       str      =							state_dict.pop(SCREAMING_SNAKE_CASE							)

     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"							)
     with open("hub/hopper-medium-v2/value_function/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      unet(32)
      # unet(128)
      value_function()

