


from dataclasses import dataclass
from typing import Dict, Optional, Union

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin


@dataclass
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      42







class     __UpperCAmelCase							(       __A ,					__A							):


     """simple docstring"""


     @register_to_config
     def __init__(	self  ,       __A = 32  ,       __A = 64  ,       __A = 20  ,       __A = 768  ,       __A=77  ,       __A=4  ,       __A = 0.0  ,       __A = "silu"  ,       __A = None  ,       __A = None  ,       __A = "linear"  ,       __A = "prd"  ,       __A = None  ,       __A = None  ,       __A = None  ,       ):
        super().__init__()
        __a					=  num_attention_heads
        __a					=  attention_head_dim
        __a					=  num_attention_heads * attention_head_dim
        __a					=  additional_embeddings

        __a					=  time_embed_dim or inner_dim
        __a					=  embedding_proj_dim or embedding_dim
        __a					=  clip_embed_dim or embedding_dim

        __a					=  Timesteps(__A  ,       __A  ,       0    )
        __a					=  TimestepEmbedding(__A  ,       __A  ,       out_dim=__A  ,       act_fn=__A    )

        __a					=  nn.Linear(__A  ,       __A    )

        if embedding_proj_norm_type is None:
           __a					=  None
        elif embedding_proj_norm_type == "layer":
           __a					=  nn.LayerNorm(__A    )
        else:
           raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''    )

        __a					=  nn.Linear(__A  ,       __A    )

        if encoder_hid_proj_type is None:
           __a					=  None
        elif encoder_hid_proj_type == "linear":
           __a					=  nn.Linear(__A  ,       __A    )
        else:
           raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''    )

        __a					=  nn.Parameter(torch.zeros(1  ,       num_embeddings + additional_embeddings  ,       __A    )    )

        if added_emb_type == "prd":
           __a					=  nn.Parameter(torch.zeros(1  ,       1  ,       __A    )    )
        elif added_emb_type is None:
           __a					=  None
        else:
           raise ValueError(
               f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''    )

        __a					=  nn.ModuleList(
            [
                BasicTransformerBlock(
                    __A  ,       __A  ,       __A  ,       dropout=__A  ,       activation_fn="""gelu"""  ,       attention_bias=__A  ,       )
                for d in range(__A    )
            ]    )

        if norm_in_type == "layer":
           __a					=  nn.LayerNorm(__A    )
        elif norm_in_type is None:
           __a					=  None
        else:
           raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''    )

        __a					=  nn.LayerNorm(__A    )

        __a					=  nn.Linear(__A  ,       __A    )

        __a					=  torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]  ,       -10000.0    )
        causal_attention_mask.triu_(1    )
        __a					=  causal_attention_mask[None, ...]
        self.register_buffer("""causal_attention_mask"""  ,       __A  ,       persistent=__A    )

        __a					=  nn.Parameter(torch.zeros(1  ,       __A    )    )
        __a					=  nn.Parameter(torch.zeros(1  ,       __A    )    )


     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 	snake_case_  (	self    ):
        __a					=  {}

        def fn_recursive_add_processors(__A  ,       __A  ,       __A    ):
           if hasattr(__A  ,       """set_processor"""    ):
              __a					=  module.processor

           for sub_name, child in module.named_children():
              fn_recursive_add_processors(f'''{name}.{sub_name}'''  ,       __A  ,       __A    )

           return processors

        for name, module in self.named_children():
           fn_recursive_add_processors(__A  ,       __A  ,       __A    )

        return processors


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  len(self.attn_processors.keys()    )

        if isinstance(__A  ,       __A    ) and len(__A    ) != count:
           raise ValueError(
               f'''A dict of processors was passed, but the number of processors {len(__A    )} does not match the'''
               f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''    )

        def fn_recursive_attn_processor(__A  ,       __A  ,       __A    ):
           if hasattr(__A  ,       """set_processor"""    ):
              if not isinstance(__A  ,       __A    ):
                 module.set_processor(__A    )
              else:
                 module.set_processor(processor.pop(f'''{name}.processor'''    )    )

           for sub_name, child in module.named_children():
              fn_recursive_attn_processor(f'''{name}.{sub_name}'''  ,       __A  ,       __A    )

        for name, module in self.named_children():
           fn_recursive_attn_processor(__A  ,       __A  ,       __A    )


     def 	snake_case_  (	self    ):
        self.set_attn_processor(AttnProcessor()    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A = None  ,       __A = None  ,       __A = True  ,       ):
        __a					=  hidden_states.shape[0]

        __a					=  timestep
        if not torch.is_tensor(__A    ):
           __a					=  torch.tensor([timesteps]  ,       dtype=torch.long  ,       device=hidden_states.device    )
        elif torch.is_tensor(__A    ) and len(timesteps.shape    ) == 0:
           __a					=  timesteps[None].to(hidden_states.device    )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __a					=  timesteps * torch.ones(__A  ,       dtype=timesteps.dtype  ,       device=timesteps.device    )

        __a					=  self.time_proj(__A    )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        __a					=  timesteps_projected.to(dtype=self.dtype    )
        __a					=  self.time_embedding(__A    )

        if self.embedding_proj_norm is not None:
           __a					=  self.embedding_proj_norm(__A    )

        __a					=  self.embedding_proj(__A    )
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
           __a					=  self.encoder_hidden_states_proj(__A    )
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
           raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"""    )

        __a					=  self.proj_in(__A    )

        __a					=  self.positional_embedding.to(hidden_states.dtype    )

        __a					=  []
        __a					=  0

        if encoder_hidden_states is not None:
           additional_embeds.append(__A    )
           additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape    ) == 2:
           __a					=  proj_embeddings[:, None, :]

        if len(hidden_states.shape    ) == 2:
           __a					=  hidden_states[:, None, :]

        __a					=  additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
           __a					=  self.prd_embedding.to(hidden_states.dtype    ).expand(__A  ,       -1  ,       -1    )
           additional_embeds.append(__A    )

        __a					=  torch.cat(
            __A  ,       dim=1  ,       )

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        __a					=  additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
           __a					=  F.pad(
               __A  ,       (
                   0,
                   0,
                   additional_embeddings_len,
                   self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
               )  ,       value=0.0  ,       )

        __a					=  hidden_states + positional_embeddings

        if attention_mask is not None:
           __a					=  (1 - attention_mask.to(hidden_states.dtype    )) * -10000.0
           __a					=  F.pad(__A  ,       (0, self.additional_embeddings)  ,       value=0.0    )
           __a					=  (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype    )
           __a					=  attention_mask.repeat_interleave(self.config.num_attention_heads  ,       dim=0    )

        if self.norm_in is not None:
           __a					=  self.norm_in(__A    )

        for block in self.transformer_blocks:
           __a					=  block(__A  ,       attention_mask=__A    )

        __a					=  self.norm_out(__A    )

        if self.prd_embedding is not None:
           __a					=  hidden_states[:, -1]
        else:
           __a					=  hidden_states[:, additional_embeddings_len:]

        __a					=  self.proj_to_clip_embeddings(__A    )

        if not return_dict:
           return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  (prior_latents * self.clip_std) + self.clip_mean
        return prior_latents







import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'▁'

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'prophetnet.tokenizer'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}




def  a   (lowerCAmelCase__   ):
   __a					=  collections.OrderedDict()
   with open(lowerCAmelCase__		,				"""r"""		,				encoding="""utf-8"""   ) as reader:
      __a					=  reader.readlines()
   for index, token in enumerate(lowerCAmelCase__   ):
      __a					=  token.rstrip("""\n"""   )
      __a					=  index
   return vocab


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]


     def __init__(	self  ,       __A  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[UNK]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A = None  ,       **__A  ,       ):
        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__A  ,       eos_token=__A  ,       sep_token=__A  ,       unk_token=__A  ,       pad_token=__A  ,       cls_token=__A  ,       mask_token=__A  ,       sp_model_kwargs=self.sp_model_kwargs  ,       **__A  ,       )

        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(__A    )    )
        __a					=  vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        __a					=  {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

        for i in range(10    ):
           __a					=  f'''[unused{i}]'''
           __a					=  5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        __a					=  12
        __a					=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(__A    )


     def __getstate__(	self    ):
        __a					=  self.__dict__.copy()
        __a					=  None
        return state


     def __setstate__(	self  ,       __A    ):
        __a					=  d
        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

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

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(self.vocab_file    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):

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

        if token_ids_a is None:
           return ([0] * len(__A    )) + [1]
        return ([0] * len(__A    )) + [1] + ([0] * len(__A    )) + [1]


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]

        if token_ids_a is None:
           return len(token_ids_a + sep    ) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep    ) * [0]


     @property
     def 	snake_case_  (	self    ):
        return len(self.sp_model    ) + self.fairseq_offset


     def 	snake_case_  (	self    ):
        __a					=  {self.convert_ids_to_tokens(__A    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.encode(__A  ,       out_type=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        __a					=  self.sp_model.PieceToId(__A    )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 	snake_case_  (	self  ,       __A    ):
        if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """""".join(__A    ).replace(__A  ,       """ """    ).strip()
        return out_string


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if not os.path.isdir(__A    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  os.path.join(
            __A  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

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

        return (out_vocab_file,)


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):

        if token_ids_a is None:
           return token_ids_a + [self.sep_token_id]
        __a					=  [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep



