





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 UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	torch.FloatTensor





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   ):


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



       """simple docstring"""





       super().__init__()
       A_					      =					use_linear_projection
       A_					      =					num_attention_heads
       A_					      =					attention_head_dim
       A_					      =					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
       A_					      =					(in_channels is not None) and (patch_size is None)
       A_					      =					num_vector_embeds is not None
       A_					      =					in_channels is not None and patch_size is not None

       if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
            A_					      =					(
                f'The configuration file of this model: {self.__class__} is outdated.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'''   ,__snake_case   ,standard_warn=__snake_case   )
            A_					      =					'''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:
            A_					      =					in_channels

            A_					      =					torch.nn.GroupNorm(num_groups=__snake_case   ,num_channels=__snake_case   ,eps=1E-6   ,affine=__snake_case   )
            if use_linear_projection:
                 A_					      =					nn.Linear(__snake_case   ,__snake_case   )
            else:
                 A_					      =					nn.Convad(__snake_case   ,__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"

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

            A_					      =					ImagePositionalEmbeddings(
                num_embed=__snake_case   ,embed_dim=__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"

            A_					      =					sample_size
            A_					      =					sample_size

            A_					      =					patch_size
            A_					      =					PatchEmbed(
                height=__snake_case   ,width=__snake_case   ,patch_size=__snake_case   ,in_channels=__snake_case   ,embed_dim=__snake_case   ,)

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

       # 4. Define output layers
       A_					      =					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:
                 A_					      =					nn.Linear(__snake_case   ,__snake_case   )
            else:
                 A_					      =					nn.Convad(__snake_case   ,__snake_case   ,kernel_size=1   ,stride=1   ,padding=0   )
       elif self.is_input_vectorized:
            A_					      =					nn.LayerNorm(__snake_case   )
            A_					      =					nn.Linear(__snake_case   ,self.num_vector_embeds - 1   )
       elif self.is_input_patches:
            A_					      =					nn.LayerNorm(__snake_case   ,elementwise_affine=__snake_case   ,eps=1E-6   )
            A_					      =					nn.Linear(__snake_case   ,2 * inner_dim   )
            A_					      =					nn.Linear(__snake_case   ,patch_size * patch_size * self.out_channels   )




  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case = None   ,__snake_case = None   ,__snake_case = None   ,__snake_case = None   ,__snake_case = None   ,__snake_case = None   ,__snake_case = True   ,):



       """simple docstring"""





       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)
            A_					      =					(1 - attention_mask.to(hidden_states.dtype   )) * -1_0000.0
            A_					      =					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:
            A_					      =					(1 - encoder_attention_mask.to(hidden_states.dtype   )) * -1_0000.0
            A_					      =					encoder_attention_mask.unsqueeze(1   )

       # 1. Input
       if self.is_input_continuous:
            A_      ,						A_      ,						A_      ,						A_					      =					hidden_states.shape
            A_					      =					hidden_states

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

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

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

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

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

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










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



