



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 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      42



class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__						):

   @register_to_config
   def __init__(   self      :							int				, __lowerCAmelCase      :							int = 16				, __lowerCAmelCase      :							int = 88				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							int = 1				, __lowerCAmelCase      :							float = 0.0				, __lowerCAmelCase      :							int = 32				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							str = "geglu"				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							str = "layer_norm"				, __lowerCAmelCase      :							bool = True				, )							->					List[Any]:


    """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"				, __lowerCAmelCase				, standard_warn=__lowerCAmelCase				)
     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=__lowerCAmelCase				, num_channels=__lowerCAmelCase				, eps=1E-6				, affine=__lowerCAmelCase				)
     if use_linear_projection:
      a							=		nn.Linear(__lowerCAmelCase				, __lowerCAmelCase				)
     else:
      a							=		nn.Convad(__lowerCAmelCase				, __lowerCAmelCase				, 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=__lowerCAmelCase				, embed_dim=__lowerCAmelCase				, 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=__lowerCAmelCase				, width=__lowerCAmelCase				, patch_size=__lowerCAmelCase				, in_channels=__lowerCAmelCase				, embed_dim=__lowerCAmelCase				, )

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

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




   def 		A				(   self      :							Any				, __lowerCAmelCase      :							torch.Tensor				, __lowerCAmelCase      :							Optional[torch.Tensor] = None				, __lowerCAmelCase      :							Optional[torch.LongTensor] = None				, __lowerCAmelCase      :							Optional[torch.LongTensor] = None				, __lowerCAmelCase      :							Dict[str, Any] = None				, __lowerCAmelCase      :							Optional[torch.Tensor] = None				, __lowerCAmelCase      :							Optional[torch.Tensor] = None				, __lowerCAmelCase      :							bool = True				, )							->					Dict:


    """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_0_0_0_0.0
     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_0_0_0_0.0
     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(__lowerCAmelCase				)
     if not self.use_linear_projection:
      a							=		self.proj_in(__lowerCAmelCase				)
      a							=		hidden_states.shape[1]
      a							=		hidden_states.permute(0				, 2				, 3				, 1				).reshape(__lowerCAmelCase				, height * width				, __lowerCAmelCase				)
     else:
      a							=		hidden_states.shape[1]
      a							=		hidden_states.permute(0				, 2				, 3				, 1				).reshape(__lowerCAmelCase				, height * width				, __lowerCAmelCase				)
      a							=		self.proj_in(__lowerCAmelCase				)
    elif self.is_input_vectorized:
     a							=		self.latent_image_embedding(__lowerCAmelCase				)
    elif self.is_input_patches:
     a							=		self.pos_embed(__lowerCAmelCase				)

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

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

     a							=		hidden_states + residual
    elif self.is_input_vectorized:
     a							=		self.norm_out(__lowerCAmelCase				)
     a							=		self.out(__lowerCAmelCase				)
     # (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(
         __lowerCAmelCase				, __lowerCAmelCase				, hidden_dtype=hidden_states.dtype				)
     a		,	a							=		self.proj_out_a(F.silu(__lowerCAmelCase				)				).chunk(2				, dim=1				)
     a							=		self.norm_out(__lowerCAmelCase				) * (1 + scale[:, None]) + shift[:, None]
     a							=		self.proj_out_a(__lowerCAmelCase				)

     # 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"				, __lowerCAmelCase				)
     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=__lowerCAmelCase				)






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A_		:  int										=  '''src/transformers'''

# Matches is_xxx_available()
A_		:  Union[str, Any]										=  re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
A_		:  int										=  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
A_		:  Tuple										=  re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


A_		:  str										=  '''
{0} = None
'''

A_		:  Tuple										=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


A_		:  Any										=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 if _re_test_backend.search(UpperCAmelCase__       ) is None:
  return None
 a							=		[b[0] for b in _re_backend.findall(UpperCAmelCase__       )]
 backends.sort()
 return "_and_".join(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 with open(os.path.join(UpperCAmelCase__			,       "__init__.py"       )			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
  a							=		f.readlines()

 # Get to the point we do the actual imports for type checking
 a							=		0
 while not lines[line_index].startswith("if TYPE_CHECKING"       ):
  line_index += 1

 a							=		{}
 # Go through the end of the file
 while line_index < len(UpperCAmelCase__       ):
  # If the line is an if is_backend_available, we grab all objects associated.
  a							=		find_backend(lines[line_index]       )
  if backend is not None:
   while not lines[line_index].startswith("    else:"       ):
    line_index += 1
   line_index += 1

   a							=		[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 8       ):
    a							=		lines[line_index]
    a							=		_re_single_line_import.search(UpperCAmelCase__       )
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(", "       )       )
    elif line.startswith(" " * 12       ):
     objects.append(line[12:-2]       )
    line_index += 1

   a							=		objects
  else:
   line_index += 1

 return backend_specific_objects


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 if name.isupper():
  return DUMMY_CONSTANT.format(UpperCAmelCase__       )
 elif name.islower():
  return DUMMY_FUNCTION.format(UpperCAmelCase__			,       UpperCAmelCase__       )
 else:
  return DUMMY_CLASS.format(UpperCAmelCase__			,       UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]=None       ):






 '''simple docstring'''

 if backend_specific_objects is None:
  a							=		read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 a							=		{}

 for backend, objects in backend_specific_objects.items():
  a							=		"[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"       )       ) + "]"
  a							=		"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(UpperCAmelCase__			,       UpperCAmelCase__       ) for o in objects]       )
  a							=		dummy_file

 return dummy_files


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int=False       ):






 '''simple docstring'''

 a							=		create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 a							=		{"torch": "pt"}

 # Locate actual dummy modules and read their content.
 a							=		os.path.join(UpperCAmelCase__			,       "utils"       )
 a							=		{
     backend: os.path.join(UpperCAmelCase__			,       F"""dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py"""       )
     for backend in dummy_files.keys()
 }

 a							=		{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(UpperCAmelCase__       ):
   with open(UpperCAmelCase__			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
    a							=		f.read()
  else:
   a							=		""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F"""Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py as the main """
        "__init__ has new objects."       )
    with open(dummy_file_paths[backend]			,       "w"			,       encoding="utf-8"			,       newline="\n"       ) as f:
     f.write(dummy_files[backend]       )
   else:
    raise ValueError(
        "The main __init__ has objects that are not present in "
        F"""transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py. Run `make fix-copies` """
        "to fix this."       )


if __name__ == "__main__":
     A_		:  Dict										=  argparse.ArgumentParser()
     parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
     A_		:  List[str]										=  parser.parse_args()

     check_dummies(args.fix_and_overwrite)

