






"""simple docstring"""



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

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin

@dataclass
class 			a	(  _a      ):
    UpperCAmelCase_					:	Dict      	=42


class 			a	(  _a,							_a      ):







    @register_to_config
    def __init__(     self				,						_lowerCamelCase = 1_6				,						_lowerCamelCase = 8_8				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = 1				,						_lowerCamelCase = 0.0				,						_lowerCamelCase = 3_2				,						_lowerCamelCase = None				,						_lowerCamelCase = False				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = "geglu"				,						_lowerCamelCase = None				,						_lowerCamelCase = False				,						_lowerCamelCase = False				,						_lowerCamelCase = False				,						_lowerCamelCase = "layer_norm"				,						_lowerCamelCase = True				,						):
      super().__init__()
      lowercase				       =			use_linear_projection
      lowercase				       =			num_attention_heads
      lowercase				       =			attention_head_dim
      lowercase				       =			num_attention_heads * attention_head_dim

      # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
      # Define whether input is continuous or discrete depending on configuration
      lowercase				       =			(in_channels is not None) and (patch_size is None)
      lowercase				       =			num_vector_embeds is not None
      lowercase				       =			in_channels is not None and patch_size is not None

      if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
        lowercase				       =			(
            F'The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'
            """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
            """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
            """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
            """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
        )
        deprecate('norm_type!=num_embeds_ada_norm'				,						'1.0.0'				,						_SCREAMING_SNAKE_CASE				,						standard_warn=_SCREAMING_SNAKE_CASE			)
        lowercase				       =			"""ada_norm"""

      if self.is_input_continuous and self.is_input_vectorized:
        raise ValueError(
            F'Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'
            ' sure that either `in_channels` or `num_vector_embeds` is None.'			)
      elif self.is_input_vectorized and self.is_input_patches:
        raise ValueError(
            F'Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'
            ' sure that either `num_vector_embeds` or `num_patches` is None.'			)
      elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
        raise ValueError(
            F'Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'
            F' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'			)

      # 2. Define input layers
      if self.is_input_continuous:
        lowercase				       =			in_channels

        lowercase				       =			torch.nn.GroupNorm(num_groups=_SCREAMING_SNAKE_CASE				,						num_channels=_SCREAMING_SNAKE_CASE				,						eps=1e-6				,						affine=_SCREAMING_SNAKE_CASE			)
        if use_linear_projection:
          lowercase				       =			nn.Linear(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)
        else:
          lowercase				       =			nn.Convad(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						kernel_size=1				,						stride=1				,						padding=0			)
      elif self.is_input_vectorized:
        assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
        assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

        lowercase				       =			sample_size
        lowercase				       =			sample_size
        lowercase				       =			num_vector_embeds
        lowercase				       =			self.height * self.width

        lowercase				       =			ImagePositionalEmbeddings(
            num_embed=_SCREAMING_SNAKE_CASE				,						embed_dim=_SCREAMING_SNAKE_CASE				,						height=self.height				,						width=self.width			)
      elif self.is_input_patches:
        assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

        lowercase				       =			sample_size
        lowercase				       =			sample_size

        lowercase				       =			patch_size
        lowercase				       =			PatchEmbed(
            height=_SCREAMING_SNAKE_CASE				,						width=_SCREAMING_SNAKE_CASE				,						patch_size=_SCREAMING_SNAKE_CASE				,						in_channels=_SCREAMING_SNAKE_CASE				,						embed_dim=_SCREAMING_SNAKE_CASE				,						)

      # 3. Define transformers blocks
      lowercase				       =			nn.ModuleList(
          [
              BasicTransformerBlock(
                  _SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						dropout=_SCREAMING_SNAKE_CASE				,						cross_attention_dim=_SCREAMING_SNAKE_CASE				,						activation_fn=_SCREAMING_SNAKE_CASE				,						num_embeds_ada_norm=_SCREAMING_SNAKE_CASE				,						attention_bias=_SCREAMING_SNAKE_CASE				,						only_cross_attention=_SCREAMING_SNAKE_CASE				,						upcast_attention=_SCREAMING_SNAKE_CASE				,						norm_type=_SCREAMING_SNAKE_CASE				,						norm_elementwise_affine=_SCREAMING_SNAKE_CASE				,						)
              for d in range(_SCREAMING_SNAKE_CASE			)
          ]			)

      # 4. Define output layers
      lowercase				       =			in_channels if out_channels is None else out_channels
      if self.is_input_continuous:
        # TODO: should use out_channels for continuous projections
        if use_linear_projection:
          lowercase				       =			nn.Linear(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)
        else:
          lowercase				       =			nn.Convad(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						kernel_size=1				,						stride=1				,						padding=0			)
      elif self.is_input_vectorized:
        lowercase				       =			nn.LayerNorm(_SCREAMING_SNAKE_CASE			)
        lowercase				       =			nn.Linear(_SCREAMING_SNAKE_CASE				,						self.num_vector_embeds - 1			)
      elif self.is_input_patches:
        lowercase				       =			nn.LayerNorm(_SCREAMING_SNAKE_CASE				,						elementwise_affine=_SCREAMING_SNAKE_CASE				,						eps=1e-6			)
        lowercase				       =			nn.Linear(_SCREAMING_SNAKE_CASE				,						2 * inner_dim			)
        lowercase				       =			nn.Linear(_SCREAMING_SNAKE_CASE				,						patch_size * patch_size * self.out_channels			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = True				,						):






      if attention_mask is not None and attention_mask.ndim == 2:
        # assume that mask is expressed as:
        #   (1 = keep,      0 = discard)
        # convert mask into a bias that can be added to attention scores:
        #       (keep = +0,     discard = -10000.0)
        lowercase				       =			(1 - attention_mask.to(hidden_states.dtype			)) * -1_0_0_0_0.0
        lowercase				       =			attention_mask.unsqueeze(1			)

      # convert encoder_attention_mask to a bias the same way we do for attention_mask
      if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
        lowercase				       =			(1 - encoder_attention_mask.to(hidden_states.dtype			)) * -1_0_0_0_0.0
        lowercase				       =			encoder_attention_mask.unsqueeze(1			)

      # 1. Input
      if self.is_input_continuous:
        lowercase				       =			hidden_states.shape
        lowercase				       =			hidden_states

        lowercase				       =			self.norm(_SCREAMING_SNAKE_CASE			)
        if not self.use_linear_projection:
          lowercase				       =			self.proj_in(_SCREAMING_SNAKE_CASE			)
          lowercase				       =			hidden_states.shape[1]
          lowercase				       =			hidden_states.permute(0				,						2				,						3				,						1			).reshape(_SCREAMING_SNAKE_CASE				,						height * width				,						_SCREAMING_SNAKE_CASE			)
        else:
          lowercase				       =			hidden_states.shape[1]
          lowercase				       =			hidden_states.permute(0				,						2				,						3				,						1			).reshape(_SCREAMING_SNAKE_CASE				,						height * width				,						_SCREAMING_SNAKE_CASE			)
          lowercase				       =			self.proj_in(_SCREAMING_SNAKE_CASE			)
      elif self.is_input_vectorized:
        lowercase				       =			self.latent_image_embedding(_SCREAMING_SNAKE_CASE			)
      elif self.is_input_patches:
        lowercase				       =			self.pos_embed(_SCREAMING_SNAKE_CASE			)

      # 2. Blocks
      for block in self.transformer_blocks:
        lowercase				       =			block(
            _SCREAMING_SNAKE_CASE				,						attention_mask=_SCREAMING_SNAKE_CASE				,						encoder_hidden_states=_SCREAMING_SNAKE_CASE				,						encoder_attention_mask=_SCREAMING_SNAKE_CASE				,						timestep=_SCREAMING_SNAKE_CASE				,						cross_attention_kwargs=_SCREAMING_SNAKE_CASE				,						class_labels=_SCREAMING_SNAKE_CASE				,						)

      # 3. Output
      if self.is_input_continuous:
        if not self.use_linear_projection:
          lowercase				       =			hidden_states.reshape(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			).permute(0				,						3				,						1				,						2			).contiguous()
          lowercase				       =			self.proj_out(_SCREAMING_SNAKE_CASE			)
        else:
          lowercase				       =			self.proj_out(_SCREAMING_SNAKE_CASE			)
          lowercase				       =			hidden_states.reshape(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			).permute(0				,						3				,						1				,						2			).contiguous()

        lowercase				       =			hidden_states + residual
      elif self.is_input_vectorized:
        lowercase				       =			self.norm_out(_SCREAMING_SNAKE_CASE			)
        lowercase				       =			self.out(_SCREAMING_SNAKE_CASE			)
        # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
        lowercase				       =			logits.permute(0				,						2				,						1			)

        # log(p(x_0))
        lowercase				       =			F.log_softmax(logits.double()				,						dim=1			).float()
      elif self.is_input_patches:
        # TODO: cleanup!
        lowercase				       =			self.transformer_blocks[0].norma.emb(
            _SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						hidden_dtype=hidden_states.dtype			)
        lowercase				       =			self.proj_out_a(F.silu(_SCREAMING_SNAKE_CASE			)			).chunk(2				,						dim=1			)
        lowercase				       =			self.norm_out(_SCREAMING_SNAKE_CASE			) * (1 + scale[:, None]) + shift[:, None]
        lowercase				       =			self.proj_out_a(_SCREAMING_SNAKE_CASE			)

        # unpatchify
        lowercase				       =			int(hidden_states.shape[1] ** 0.5			)
        lowercase				       =			hidden_states.reshape(
            shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)			)
        lowercase				       =			torch.einsum('nhwpqc->nchpwq'				,						_SCREAMING_SNAKE_CASE			)
        lowercase				       =			hidden_states.reshape(
            shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)			)

      if not return_dict:
        return (output,)

      return TransformeraDModelOutput(sample=_SCREAMING_SNAKE_CASE			)




'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xglm import XGLMTokenizer
else:
	__SCREAMING_SNAKE_CASE						=      None


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						=      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE						=      {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE						=      {
    "facebook/xglm-564M": 2048,
}




class     __snake_case			(    _a	):
		a_          =					VOCAB_FILES_NAMES
		a_          =					PRETRAINED_VOCAB_FILES_MAP
		a_          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_          =					["input_ids", "attention_mask"]
		a_          =					XGLMTokenizer




		def __init__(  self					:str			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:List[str]=None			,    _SCREAMING_SNAKE_CASE					:Dict="<s>"			,    _SCREAMING_SNAKE_CASE					:Dict="</s>"			,    _SCREAMING_SNAKE_CASE					:List[Any]="</s>"			,    _SCREAMING_SNAKE_CASE					:Tuple="<s>"			,    _SCREAMING_SNAKE_CASE					:int="<unk>"			,    _SCREAMING_SNAKE_CASE					:List[str]="<pad>"			,    **_SCREAMING_SNAKE_CASE					:Optional[int]			,    )					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     7
								UpperCamelCase_     :	int					     =     [F"""<madeupword{i}>""" for i in range(self.num_madeup_words)]

								UpperCamelCase_     :	Optional[Any]					     =     kwargs.get("""additional_special_tokens"""			,    [])
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    _SCREAMING_SNAKE_CASE			,    tokenizer_file=_SCREAMING_SNAKE_CASE			,    bos_token=_SCREAMING_SNAKE_CASE			,    eos_token=_SCREAMING_SNAKE_CASE			,    sep_token=_SCREAMING_SNAKE_CASE			,    cls_token=_SCREAMING_SNAKE_CASE			,    unk_token=_SCREAMING_SNAKE_CASE			,    pad_token=_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE			,    )

								UpperCamelCase_     :	int					     =     vocab_file
								UpperCamelCase_     :	Any					     =     False if not self.vocab_file else True




		def 	_snake_case	(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:List[int]			,    _SCREAMING_SNAKE_CASE					:Optional[List[int]] = None)					->			List[int]:






								'''simple docstring'''






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




		def 	_snake_case	(  self					:int			,    _SCREAMING_SNAKE_CASE					:List[int]			,    _SCREAMING_SNAKE_CASE					:Optional[List[int]] = None)					->			List[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     [self.sep_token_id]

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





		def 	_snake_case	(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:Optional[str] = None)					->			Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
														raise ValueError(
														    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
														    """tokenizer.""")

								if not os.path.isdir(_SCREAMING_SNAKE_CASE):
														logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""")
														return
								UpperCamelCase_     :	Dict					     =     os.path.join(
								    _SCREAMING_SNAKE_CASE			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

								if os.path.abspath(self.vocab_file) != os.path.abspath(_SCREAMING_SNAKE_CASE):
														copyfile(self.vocab_file			,    _SCREAMING_SNAKE_CASE)

								return (out_vocab_file,)




