



'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


lowerCAmelCase	:		Any				=   logging.get_logger(__name__)

lowerCAmelCase	:		Optional[Any]				=   {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase	:		Tuple				=   {
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase	:		str				=   {
    '''gpt2''': 1_0_2_4,
    '''gpt2-medium''': 1_0_2_4,
    '''gpt2-large''': 1_0_2_4,
    '''gpt2-xl''': 1_0_2_4,
    '''distilgpt2''': 1_0_2_4,
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Dict        =							VOCAB_FILES_NAMES
					a      :     Any        =							PRETRAINED_VOCAB_FILES_MAP
					a      :     Tuple        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a      :     Optional[Any]        =							["""input_ids""", """attention_mask"""]
					a      :     Union[str, Any]        =							GPTaTokenizer



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase="<|endoftext|>"			,				UpperCamelCase="<|endoftext|>"			,				UpperCamelCase="<|endoftext|>"			,				UpperCamelCase=False			,				**UpperCamelCase			,				)     ->			int:
						super().__init__(
						    UpperCamelCase			,				UpperCamelCase			,				tokenizer_file=UpperCamelCase			,				unk_token=UpperCamelCase			,				bos_token=UpperCamelCase			,				eos_token=UpperCamelCase			,				add_prefix_space=UpperCamelCase			,				**UpperCamelCase			,				)

						__lowerCAmelCase												=  kwargs.pop("add_bos_token"			,				UpperCamelCase     )

						__lowerCAmelCase												=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
						if pre_tok_state.get("add_prefix_space"			,				UpperCamelCase     ) != add_prefix_space:
							__lowerCAmelCase												=  getattr(UpperCamelCase			,				pre_tok_state.pop("type"     )     )
							__lowerCAmelCase												=  add_prefix_space
							__lowerCAmelCase												=  pre_tok_class(**UpperCamelCase     )

						__lowerCAmelCase												=  add_prefix_space



					def        UpperCAmelCase_ (    self			,				*UpperCamelCase			,				**UpperCamelCase     )     ->			BatchEncoding:
						__lowerCAmelCase												=  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        UpperCAmelCase_ (    self			,				*UpperCamelCase			,				**UpperCamelCase     )     ->			BatchEncoding:
						__lowerCAmelCase												=  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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Tuple[str]:
						__lowerCAmelCase												=  self._tokenizer.model.save(UpperCamelCase			,				name=UpperCamelCase     )
						return tuple(UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			List[int]:
						__lowerCAmelCase												=  []
						for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(UpperCamelCase			,				add_special_tokens=UpperCamelCase     ) + [self.eos_token_id]     )

						if len(UpperCamelCase     ) > self.model_max_length:
							__lowerCAmelCase												=  input_ids[-self.model_max_length :]
						return input_ids




'''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 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):



					@register_to_config
					def __init__(    self			,				UpperCamelCase = 16			,				UpperCamelCase = 88			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = 1			,				UpperCamelCase = 0.0			,				UpperCamelCase = 32			,				UpperCamelCase = None			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = "geglu"			,				UpperCamelCase = True			,				UpperCamelCase = True			,				)     ->			List[str]:
						super().__init__()
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  attention_head_dim
						__lowerCAmelCase												=  num_attention_heads * attention_head_dim

						__lowerCAmelCase												=  in_channels

						__lowerCAmelCase												=  torch.nn.GroupNorm(num_groups=UpperCamelCase			,				num_channels=UpperCamelCase			,				eps=1E-6			,				affine=UpperCamelCase     )
						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )

						# 3. Define transformers blocks
						__lowerCAmelCase												=  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												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=1			,				UpperCamelCase=None			,				UpperCamelCase = True			,				)     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  hidden_states.shape
						__lowerCAmelCase												=  batch_frames // num_frames

						__lowerCAmelCase												=  hidden_states

						__lowerCAmelCase												=  hidden_states[None, :].reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				2			,				1			,				3			,				4     )

						__lowerCAmelCase												=  self.norm(UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				3			,				4			,				2			,				1     ).reshape(batch_size * height * width			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  self.proj_in(UpperCamelCase     )

						# 2. Blocks
						for block in self.transformer_blocks:
							__lowerCAmelCase												=  block(
							    UpperCamelCase			,				encoder_hidden_states=UpperCamelCase			,				timestep=UpperCamelCase			,				cross_attention_kwargs=UpperCamelCase			,				class_labels=UpperCamelCase			,				)

						# 3. Output
						__lowerCAmelCase												=  self.proj_out(UpperCamelCase     )
						__lowerCAmelCase												=  (
						    hidden_states[None, None, :]
						    .reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						    .permute(0			,				3			,				4			,				1			,				2     )
						    .contiguous()
						)
						__lowerCAmelCase												=  hidden_states.reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  hidden_states + residual

						if not return_dict:
							return (output,)

						return TransformerTemporalModelOutput(sample=UpperCamelCase     )