






"""simple docstring"""



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
						import torch





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   "char"
					SCREAMING_SNAKE_CASE_		:				Any							    =   "bpe"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   "wp"
__A		:       int  				=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   ["image_processor", "char_tokenizer"]
					SCREAMING_SNAKE_CASE_		:				Any							    =   "ViTImageProcessor"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   "MgpstrTokenizer"




					def __init__(				self     :						List[str]    ,						A     :						Union[str, Any]=None    ,						A     :						str=None    ,						**A     :						Optional[Any]   )  ->  str:
							lowercase_ : Optional[Any]							       =   None
							if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.'''    ,						A    ,						)
									lowercase_ : List[Any]							       =   kwargs.pop('''feature_extractor'''   )

							lowercase_ : str							       =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
									raise ValueError('''You need to specify an `image_processor`.'''   )
							if tokenizer is None:
									raise ValueError('''You need to specify a `tokenizer`.'''   )

							lowercase_ : List[str]							       =   tokenizer
							lowercase_ : Union[str, Any]							       =   AutoTokenizer.from_pretrained('''gpt2'''   )
							lowercase_ : List[Any]							       =   AutoTokenizer.from_pretrained('''bert-base-uncased'''   )

							super().__init__(A    ,						A   )




					def __call__(				self     :						List[Any]    ,						A     :						int=None    ,						A     :						Tuple=None    ,						A     :						List[Any]=None    ,						**A     :						Union[str, Any]   )  ->  int:
							if images is None and text is None:
									raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

							if images is not None:
									lowercase_ : Optional[Any]							       =   self.image_processor(A    ,						return_tensors=A    ,						**A   )
							if text is not None:
									lowercase_ : Union[str, Any]							       =   self.char_tokenizer(A    ,						return_tensors=A    ,						**A   )

							if text is None:
									return inputs
							elif images is None:
									return encodings
							else:
									lowercase_ : List[str]							       =   encodings['''input_ids''']
									return inputs




					def   A  (				self     :						Optional[Any]    ,						A     :						int   )  ->  Optional[int]:
							lowercase_       ,					lowercase_       ,					lowercase_ : Any							       =   sequences
							lowercase_ : Tuple							       =   char_preds.size(0   )

							lowercase_       ,					lowercase_ : Optional[int]							       =   self._decode_helper(A    ,						'''char'''   )
							lowercase_       ,					lowercase_ : Dict							       =   self._decode_helper(A    ,						'''bpe'''   )
							lowercase_       ,					lowercase_ : Optional[int]							       =   self._decode_helper(A    ,						'''wp'''   )

							lowercase_ : int							       =   []
							lowercase_ : Optional[int]							       =   []
							for i in range(A   ):
									lowercase_ : Dict							       =   [char_scores[i], bpe_scores[i], wp_scores[i]]
									lowercase_ : List[Any]							       =   [char_strs[i], bpe_strs[i], wp_strs[i]]
									lowercase_ : Dict							       =   scores.index(max(A   )   )
									final_strs.append(strs[max_score_index]   )
									final_scores.append(scores[max_score_index]   )

							lowercase_ : List[Any]							       =   {}
							lowercase_ : Any							       =   final_strs
							lowercase_ : List[Any]							       =   final_scores
							lowercase_ : Optional[Any]							       =   char_strs
							lowercase_ : Union[str, Any]							       =   bpe_strs
							lowercase_ : Dict							       =   wp_strs
							return out




					def   A  (				self     :						Dict    ,						A     :						List[Any]    ,						A     :						Dict   )  ->  Optional[Any]:
							if format == DecodeType.CHARACTER:
									lowercase_ : List[str]							       =   self.char_decode
									lowercase_ : str							       =   1
									lowercase_ : str							       =   '''[s]'''
							elif format == DecodeType.BPE:
									lowercase_ : Optional[int]							       =   self.bpe_decode
									lowercase_ : List[Any]							       =   2
									lowercase_ : Any							       =   '''#'''
							elif format == DecodeType.WORDPIECE:
									lowercase_ : Optional[Any]							       =   self.wp_decode
									lowercase_ : Optional[int]							       =   1_02
									lowercase_ : str							       =   '''[SEP]'''
							else:
									raise ValueError(F'''Format {format} is not supported.'''   )

							lowercase_       ,					lowercase_ : Union[str, Any]							       =   [], []
							lowercase_ : Dict							       =   pred_logits.size(0   )
							lowercase_ : Dict							       =   pred_logits.size(1   )
							lowercase_       ,					lowercase_ : Dict							       =   pred_logits.topk(1    ,						dim=-1    ,						largest=A    ,						sorted=A   )
							lowercase_ : List[str]							       =   preds_index.view(-1    ,						A   )[:, 1:]
							lowercase_ : Dict							       =   decoder(A   )
							lowercase_       ,					lowercase_ : Dict							       =   torch.nn.functional.softmax(A    ,						dim=2   ).max(dim=2   )
							lowercase_ : Optional[int]							       =   preds_max_prob[:, 1:]

							for index in range(A   ):
									lowercase_ : Union[str, Any]							       =   preds_str[index].find(A   )
									lowercase_ : Union[str, Any]							       =   preds_str[index][:pred_eos]
									lowercase_ : Dict							       =   preds_index[index].cpu().tolist()
									lowercase_ : int							       =   pred_index.index(A   ) if eos_token in pred_index else -1
									lowercase_ : List[str]							       =   preds_max_prob[index][: pred_eos_index + 1]
									lowercase_ : Optional[int]							       =   pred_max_prob.cumprod(dim=0   )[-1] if pred_max_prob.nelement() != 0 else 0.0
									dec_strs.append(A   )
									conf_scores.append(A   )

							return dec_strs, conf_scores




					def   A  (				self     :						str    ,						A     :						Optional[int]   )  ->  Any:
							lowercase_ : Tuple							       =   [seq.replace(''' '''    ,						''''''   ) for seq in self.char_tokenizer.batch_decode(A   )]
							return decode_strs




					def   A  (				self     :						Tuple    ,						A     :						Union[str, Any]   )  ->  Optional[int]:
							return self.bpe_tokenizer.batch_decode(A   )






					def   A  (				self     :						Dict    ,						A     :						int   )  ->  str:
							lowercase_ : str							       =   [seq.replace(''' '''    ,						''''''   ) for seq in self.wp_tokenizer.batch_decode(A   )]
							return decode_strs








from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings






@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = "geglu",		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = True,		lowerCamelCase__ = "layer_norm",		lowerCamelCase__ = False,		):
        super().__init__()
        A :       Dict          =   only_cross_attention

        A :       Tuple          =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
        A :       Union[str, Any]          =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
            raise ValueError(
                f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''		)

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        if self.use_ada_layer_norm:
            A :       Dict          =   AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A :       List[str]          =   AdaLayerNormZero(lowerCamelCase__,		lowerCamelCase__		)
        else:
            A :       Tuple          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
        A :       Any          =   Attention(
            query_dim=lowerCamelCase__,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if only_cross_attention else None,		upcast_attention=lowerCamelCase__,		)

        # 2. Cross-Attn
        if cross_attention_dim is not None or double_self_attention:
            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
            # the second cross attention block.
            A :       int          =   (
                AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
                if self.use_ada_layer_norm
                else nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
            )
            A :       Dict          =   Attention(
                query_dim=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if not double_self_attention else None,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		upcast_attention=lowerCamelCase__,		)  # is self-attn if encoder_hidden_states is none
        else:
            A :       Dict          =   None
            A :       Dict          =   None

        # 3. Feed-forward
        A :       Optional[Any]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
        A :       int          =   FeedForward(lowerCamelCase__,		dropout=lowerCamelCase__,		activation_fn=lowerCamelCase__,		final_dropout=lowerCamelCase__		)

        # let chunk size default to None
        A :       Optional[Any]          =   None
        A :       int          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        # Sets chunk feed-forward
        A :       List[str]          =   chunk_size
        A :       int          =   dim






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 1. Self-Attention
        if self.use_ada_layer_norm:
            A :       Optional[int]          =   self.norma(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A     ,							A     ,							A     ,							A     ,							A :       Tuple          =   self.norma(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=hidden_states.dtype		)
        else:
            A :       Tuple          =   self.norma(lowerCamelCase__		)

        A :       Dict          =   cross_attention_kwargs if cross_attention_kwargs is not None else {}

        A :       str          =   self.attna(
            lowerCamelCase__,		encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        if self.use_ada_layer_norm_zero:
            A :       List[Any]          =   gate_msa.unsqueeze(1		) * attn_output
        A :       str          =   attn_output + hidden_states

        # 2. Cross-Attention
        if self.attna is not None:
            A :       Optional[int]          =   (
                self.norma(lowerCamelCase__,		lowerCamelCase__		) if self.use_ada_layer_norm else self.norma(lowerCamelCase__		)
            )

            A :       Optional[Any]          =   self.attna(
                lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
            A :       Dict          =   attn_output + hidden_states

        # 3. Feed-forward
        A :       str          =   self.norma(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Tuple          =   norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                raise ValueError(
                    f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''		)

            A :       Any          =   norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
            A :       Optional[Any]          =   torch.cat(
                [self.ff(lowerCamelCase__		) for hid_slice in norm_hidden_states.chunk(lowerCamelCase__,		dim=self._chunk_dim		)],		dim=self._chunk_dim,		)
        else:
            A :       Dict          =   self.ff(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Optional[Any]          =   gate_mlp.unsqueeze(1		) * ff_output

        A :       Optional[int]          =   ff_output + hidden_states

        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = 4,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = "geglu",		lowerCamelCase__ = False,		):
        super().__init__()
        A :       str          =   int(dim * mult		)
        A :       Optional[Any]          =   dim_out if dim_out is not None else dim

        if activation_fn == "gelu":
            A :       Dict          =   GELU(lowerCamelCase__,		lowerCamelCase__		)
        if activation_fn == "gelu-approximate":
            A :       Optional[int]          =   GELU(lowerCamelCase__,		lowerCamelCase__,		approximate="""tanh"""		)
        elif activation_fn == "geglu":
            A :       Any          =   GEGLU(lowerCamelCase__,		lowerCamelCase__		)
        elif activation_fn == "geglu-approximate":
            A :       Optional[Any]          =   ApproximateGELU(lowerCamelCase__,		lowerCamelCase__		)

        A :       Dict          =   nn.ModuleList([]		)
        # project in
        self.net.append(lowerCamelCase__		)
        # project dropout
        self.net.append(nn.Dropout(lowerCamelCase__		)		)
        # project out
        self.net.append(nn.Linear(lowerCamelCase__,		lowerCamelCase__		)		)
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
            self.net.append(nn.Dropout(lowerCamelCase__		)		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        for module in self.net:
            A :       int          =   module(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = "none"		):
        super().__init__()
        A :       Optional[int]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)
        A :       int          =   approximate






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__,		approximate=self.approximate		)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa		),		approximate=self.approximate		).to(dtype=gate.dtype		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        A :       Union[str, Any]          =   self.gelu(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		dim_out * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__		)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A     ,							A :       Any          =   self.proj(lowerCamelCase__		).chunk(2,		dim=-1		)
        return hidden_states * self.gelu(lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        return x * torch.sigmoid(1.702 * x		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Dict          =   nn.Embedding(lowerCamelCase__,		lowerCamelCase__		)
        A :       Tuple          =   nn.SiLU()
        A :       Tuple          =   nn.Linear(lowerCamelCase__,		embedding_dim * 2		)
        A :       List[str]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        A :       int          =   self.linear(self.silu(self.emb(lowerCamelCase__		)		)		)
        A     ,							A :       Optional[int]          =   torch.chunk(lowerCamelCase__,		2		)
        A :       Tuple          =   self.norm(lowerCamelCase__		) * (1 + scale) + shift
        return x






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()

        A :       Union[str, Any]          =   CombinedTimestepLabelEmbeddings(lowerCamelCase__,		lowerCamelCase__		)

        A :       List[Any]          =   nn.SiLU()
        A :       str          =   nn.Linear(lowerCamelCase__,		6 * embedding_dim,		bias=lowerCamelCase__		)
        A :       List[Any]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__,		eps=1e-6		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A :       Tuple          =   self.linear(self.silu(self.emb(lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=lowerCamelCase__		)		)		)
        A     ,							A     ,							A     ,							A     ,							A     ,							A :       Optional[Any]          =   emb.chunk(6,		dim=1		)
        A :       List[str]          =   self.norm(lowerCamelCase__		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = 1e-5		):
        super().__init__()
        A :       int          =   num_groups
        A :       Dict          =   eps

        if act_fn is None:
            A :       Union[str, Any]          =   None
        else:
            A :       Optional[int]          =   get_activation(lowerCamelCase__		)

        A :       Dict          =   nn.Linear(lowerCamelCase__,		out_dim * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self.act:
            A :       Dict          =   self.act(lowerCamelCase__		)
        A :       Optional[int]          =   self.linear(lowerCamelCase__		)
        A :       int          =   emb[:, :, None, None]
        A     ,							A :       Tuple          =   emb.chunk(2,		dim=1		)

        A :       Any          =   F.group_norm(lowerCamelCase__,		self.num_groups,		eps=self.eps		)
        A :       List[str]          =   x * (1 + scale) + shift
        return x


