




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


__SCREAMING_SNAKE_CASE			    =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			    ={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE			    ={
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE			    ={
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

__SCREAMING_SNAKE_CASE			    ={
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}






class 			__magic_name__    (	__lowerCamelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				Optional[Any]						   =       VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__       :				Optional[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__       :				List[Any]						   =       PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE__       :				Tuple						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__       :				Any						   =       RealmTokenizer


				def __init__(				self: List[Any]		,       _lowerCamelCase: List[Any]=None		,       _lowerCamelCase: List[Any]=None		,       _lowerCamelCase: Optional[int]=True		,       _lowerCamelCase: List[Any]="[UNK]"		,       _lowerCamelCase: Dict="[SEP]"		,       _lowerCamelCase: Optional[Any]="[PAD]"		,       _lowerCamelCase: Tuple="[CLS]"		,       _lowerCamelCase: Optional[int]="[MASK]"		,       _lowerCamelCase: int=True		,       _lowerCamelCase: Any=None		,       **_lowerCamelCase: List[Any]		,       ):
											super().__init__(
											    _lowerCamelCase		,       tokenizer_file=_lowerCamelCase		,       do_lower_case=_lowerCamelCase		,       unk_token=_lowerCamelCase		,       sep_token=_lowerCamelCase		,       pad_token=_lowerCamelCase		,       cls_token=_lowerCamelCase		,       mask_token=_lowerCamelCase		,       tokenize_chinese_chars=_lowerCamelCase		,       strip_accents=_lowerCamelCase		,       **_lowerCamelCase		,       )

											SCREAMING_SNAKE_CASE_ 						=     json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
											if (
											    normalizer_state.get('''lowercase'''		,       _lowerCamelCase		) != do_lower_case
											    or normalizer_state.get('''strip_accents'''		,       _lowerCamelCase		) != strip_accents
											    or normalizer_state.get('''handle_chinese_chars'''		,       _lowerCamelCase		) != tokenize_chinese_chars
											):
																		SCREAMING_SNAKE_CASE_ 						=     getattr(_lowerCamelCase		,       normalizer_state.pop('''type'''		)		)
																		SCREAMING_SNAKE_CASE_ 						=     do_lower_case
																		SCREAMING_SNAKE_CASE_ 						=     strip_accents
																		SCREAMING_SNAKE_CASE_ 						=     tokenize_chinese_chars
																		SCREAMING_SNAKE_CASE_ 						=     normalizer_class(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_ 						=     do_lower_case


				def      _A				(				self: Optional[Any]		,       _lowerCamelCase: Tuple		,       **_lowerCamelCase: List[str]		):
											SCREAMING_SNAKE_CASE_ 						=     PaddingStrategy.MAX_LENGTH

											SCREAMING_SNAKE_CASE_ 						=     text
											SCREAMING_SNAKE_CASE_ 						=     kwargs.pop('''text_pair'''		,       _lowerCamelCase		)
											SCREAMING_SNAKE_CASE_ 						=     kwargs.pop('''return_tensors'''		,       _lowerCamelCase		)

											SCREAMING_SNAKE_CASE_ 						=     {
											    '''input_ids''': [],
											    '''attention_mask''': [],
											    '''token_type_ids''': [],
											}

											for idx, candidate_text in enumerate(_lowerCamelCase		):
																		if batch_text_pair is not None:
																									SCREAMING_SNAKE_CASE_ 						=     batch_text_pair[idx]
																		else:
																									SCREAMING_SNAKE_CASE_ 						=     None

																		SCREAMING_SNAKE_CASE_ 						=     super().__call__(_lowerCamelCase		,       _lowerCamelCase		,       return_tensors=_lowerCamelCase		,       **_lowerCamelCase		)

																		SCREAMING_SNAKE_CASE_ 						=     encoded_candidates.get('''input_ids'''		)
																		SCREAMING_SNAKE_CASE_ 						=     encoded_candidates.get('''attention_mask'''		)
																		SCREAMING_SNAKE_CASE_ 						=     encoded_candidates.get('''token_type_ids'''		)

																		if encoded_input_ids is not None:
																									output_data["input_ids"].append(_lowerCamelCase		)
																		if encoded_attention_mask is not None:
																									output_data["attention_mask"].append(_lowerCamelCase		)
																		if encoded_token_type_ids is not None:
																									output_data["token_type_ids"].append(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_ 						=     {key: item for key, item in output_data.items() if len(_lowerCamelCase		) != 0}

											return BatchEncoding(_lowerCamelCase		,       tensor_type=_lowerCamelCase		)


				def      _A				(				self: Dict		,       _lowerCamelCase: int		,       _lowerCamelCase: Any=None		):
											SCREAMING_SNAKE_CASE_ 						=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																		output += token_ids_a + [self.sep_token_id]

											return output


				def      _A				(				self: Tuple		,       _lowerCamelCase: List[int]		,       _lowerCamelCase: Optional[List[int]] = None		):
											SCREAMING_SNAKE_CASE_ 						=     [self.sep_token_id]
											SCREAMING_SNAKE_CASE_ 						=     [self.cls_token_id]
											if token_ids_a is None:
																		return len(cls + token_ids_a + sep		) * [0]
											return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]





				def      _A				(				self: str		,       _lowerCamelCase: str		,       _lowerCamelCase: Optional[str] = None		):
											SCREAMING_SNAKE_CASE_ 						=     self._tokenizer.model.save(_lowerCamelCase		,       name=_lowerCamelCase		)
											return tuple(_lowerCamelCase		)




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    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			if hor == 128:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 64, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			SCREAMING_SNAKE_CASE     						= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
			SCREAMING_SNAKE_CASE     						= model.state_dict()
			SCREAMING_SNAKE_CASE     						= {
			    '''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''',
			}
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			print(F"""length of state dict: {len(state_dict.keys()	)}"""	)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()	)}"""	)
			SCREAMING_SNAKE_CASE     						= dict(zip(model.state_dict().keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)
			hf_value_function.load_state_dict(a	)

			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(a		,						a	)


def    a      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= {
			    '''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''',
			}

			SCREAMING_SNAKE_CASE     						= torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''	)
			SCREAMING_SNAKE_CASE     						= model
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			print(F"""length of state dict: {len(state_dict.keys()	)}"""	)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()	)}"""	)

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

			hf_value_function.load_state_dict(a	)

			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(a		,						a	)


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()