


'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__A						=logging.get_logger(__name__)

__A						={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A						={
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

__A						={
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}


class 		_snake_case       (  a__						):
					lowerCAmelCase   :str				=       VOCAB_FILES_NAMES
					lowerCAmelCase   :List[Any]				=       PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase   :Optional[Any]				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase   :str				=       ["""input_ids""", """attention_mask"""]
					lowerCAmelCase   :Any				=       RobertaTokenizer




					def __init__(   self				,				_lowerCamelCase=None				,				_lowerCamelCase=None				,				_lowerCamelCase=None				,				_lowerCamelCase="replace"				,				_lowerCamelCase="<s>"				,				_lowerCamelCase="</s>"				,				_lowerCamelCase="</s>"				,				_lowerCamelCase="<s>"				,				_lowerCamelCase="<unk>"				,				_lowerCamelCase="<pad>"				,				_lowerCamelCase="<mask>"				,				_lowerCamelCase=False				,				_lowerCamelCase=True				,				**_lowerCamelCase				,				):
								super().__init__(
								    __UpperCAmelCase				,				__UpperCAmelCase				,				tokenizer_file=__UpperCAmelCase				,				errors=__UpperCAmelCase				,				bos_token=__UpperCAmelCase				,				eos_token=__UpperCAmelCase				,				sep_token=__UpperCAmelCase				,				cls_token=__UpperCAmelCase				,				unk_token=__UpperCAmelCase				,				pad_token=__UpperCAmelCase				,				mask_token=__UpperCAmelCase				,				add_prefix_space=__UpperCAmelCase				,				trim_offsets=__UpperCAmelCase				,				**__UpperCAmelCase				,				)

								UpperCAmelCase__	:       Dict   =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
								if pre_tok_state.get("""add_prefix_space"""				,				__UpperCAmelCase) != add_prefix_space:
											UpperCAmelCase__	:       Optional[int]   =  getattr(__UpperCAmelCase				,				pre_tok_state.pop("""type"""))
											UpperCAmelCase__	:       List[Any]   =  add_prefix_space
											UpperCAmelCase__	:       str   =  pre_tok_class(**__UpperCAmelCase)

								UpperCAmelCase__	:       List[str]   =  add_prefix_space

								UpperCAmelCase__	:       str   =  'post_processor'
								UpperCAmelCase__	:       Optional[Any]   =  getattr(self.backend_tokenizer				,				__UpperCAmelCase				,				__UpperCAmelCase)
								if tokenizer_component_instance:
											UpperCAmelCase__	:       Optional[Any]   =  json.loads(tokenizer_component_instance.__getstate__())

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
														UpperCAmelCase__	:       Any   =  tuple(state["""sep"""])
											if "cls" in state:
														UpperCAmelCase__	:       int   =  tuple(state["""cls"""])

											UpperCAmelCase__	:       List[Any]   =  False

											if state.get("""add_prefix_space"""				,				__UpperCAmelCase) != add_prefix_space:
														UpperCAmelCase__	:       Union[str, Any]   =  add_prefix_space
														UpperCAmelCase__	:       Any   =  True

											if state.get("""trim_offsets"""				,				__UpperCAmelCase) != trim_offsets:
														UpperCAmelCase__	:       Union[str, Any]   =  trim_offsets
														UpperCAmelCase__	:       Optional[int]   =  True

											if changes_to_apply:
														UpperCAmelCase__	:       str   =  getattr(__UpperCAmelCase				,				state.pop("""type"""))
														UpperCAmelCase__	:       Any   =  component_class(**__UpperCAmelCase)
														setattr(self.backend_tokenizer				,				__UpperCAmelCase				,				__UpperCAmelCase)




					@property
					def 					snake_case__    (   self):

								if self._mask_token is None:
											if self.verbose:
														logger.error("""Using mask_token, but it is not set yet.""")
											return None
								return str(self._mask_token)




					@mask_token.setter
					def 					snake_case__    (   self				,				_lowerCamelCase):
								UpperCAmelCase__	:       List[Any]   =  AddedToken(__UpperCAmelCase				,				lstrip=__UpperCAmelCase				,				rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase				,				__UpperCAmelCase) else value
								UpperCAmelCase__	:       List[str]   =  value




					def 					snake_case__    (   self				,				*_lowerCamelCase				,				**_lowerCamelCase):
								UpperCAmelCase__	:       Optional[Any]   =  kwargs.get("""is_split_into_words"""				,				__UpperCAmelCase)
								assert self.add_prefix_space or not is_split_into_words, (
								    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*__UpperCAmelCase				,				**__UpperCAmelCase)




					def 					snake_case__    (   self				,				*_lowerCamelCase				,				**_lowerCamelCase):
								UpperCAmelCase__	:       Any   =  kwargs.get("""is_split_into_words"""				,				__UpperCAmelCase)

								assert self.add_prefix_space or not is_split_into_words, (
								    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*__UpperCAmelCase				,				**__UpperCAmelCase)




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase = None):
								UpperCAmelCase__	:       Union[str, Any]   =  self._tokenizer.model.save(__UpperCAmelCase				,				name=__UpperCAmelCase)
								return tuple(__UpperCAmelCase)




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase=None):
								UpperCAmelCase__	:       str   =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
											return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase = None):
								UpperCAmelCase__	:       List[Any]   =  [self.sep_token_id]
								UpperCAmelCase__	:       int   =  [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 + sep + token_ids_a + sep) * [0]






"""simple docstring"""





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 1_6       ,					__UpperCAmelCase = 8_8       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = 0.0       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "geglu"       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         super().__init__()
         lowerCAmelCase__     :Dict											=				num_attention_heads
         lowerCAmelCase__     :Any											=				attention_head_dim
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads * attention_head_dim

         lowerCAmelCase__     :Any											=				in_channels

         lowerCAmelCase__     :str											=				torch.nn.GroupNorm(num_groups=__UpperCAmelCase       ,					num_channels=__UpperCAmelCase       ,					eps=1E-6       ,					affine=__UpperCAmelCase )
         lowerCAmelCase__     :int											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )

         # 3. Define transformers blocks
         lowerCAmelCase__     :List[Any]											=				nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					dropout=__UpperCAmelCase       ,					cross_attention_dim=__UpperCAmelCase       ,					activation_fn=__UpperCAmelCase       ,					attention_bias=__UpperCAmelCase       ,					double_self_attention=__UpperCAmelCase       ,					norm_elementwise_affine=__UpperCAmelCase       ,					)
                 for d in range(__UpperCAmelCase )
             ] )

         lowerCAmelCase__     :List[Any]											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				hidden_states.shape
         lowerCAmelCase__     :Tuple											=				batch_frames // num_frames

         lowerCAmelCase__     :str											=				hidden_states

         lowerCAmelCase__     :Union[str, Any]											=				hidden_states[None, :].reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :str											=				hidden_states.permute(0       ,					2       ,					1       ,					3       ,					4 )

         lowerCAmelCase__     :Optional[int]											=				self.norm(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.permute(0       ,					3       ,					4       ,					2       ,					1 ).reshape(batch_size * height * width       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				self.proj_in(__UpperCAmelCase )

         # 2. Blocks
         for block in self.transformer_blocks:
           lowerCAmelCase__     :Optional[int]											=				block(
               __UpperCAmelCase       ,					encoder_hidden_states=__UpperCAmelCase       ,					timestep=__UpperCAmelCase       ,					cross_attention_kwargs=__UpperCAmelCase       ,					class_labels=__UpperCAmelCase       ,					)

         # 3. Output
         lowerCAmelCase__     :Any											=				self.proj_out(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				(
             hidden_states[None, None, :]
             .reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
             .permute(0       ,					3       ,					4       ,					1       ,					2 )
             .contiguous()
         )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
