
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)




@flax.struct.dataclass
class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				jnp.ndarray




@flax_register_to_config
class 			snake_case__   (							nn.Module	,    lowercase_	,    lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				int 				= 32
       lowerCamelCase							:				int 				= 4
       lowerCamelCase							:				int 				= 4
       lowerCamelCase							:				Tuple[str] 				= (
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "DownBlock2D",
       )
       lowerCamelCase							:				Tuple[str] 				= ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
       lowerCamelCase							:				Union[bool, Tuple[bool]] 				= False
       lowerCamelCase							:				Tuple[int] 				= (320, 640, 1280, 1280)
       lowerCamelCase							:				int 				= 2
       lowerCamelCase							:				Union[int, Tuple[int]] 				= 8
       lowerCamelCase							:				Optional[Union[int, Tuple[int]]] 				= None
       lowerCamelCase							:				int 				= 1280
       lowerCamelCase							:				float 				= 0.0
       lowerCamelCase							:				bool 				= False
       lowerCamelCase							:				jnp.dtype 				= jnp.floataa
       lowerCamelCase							:				bool 				= True
       lowerCamelCase							:				int 				= 0
       lowerCamelCase							:				bool 				= False



       def 					__lowercase						(							self			,						a__    ) ->      FrozenDict:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						(1, self.in_channels, self.sample_size, self.sample_size)
            __snake_case			:Any          =						jnp.zeros(a__			,						dtype=jnp.floataa    )
            __snake_case			:Optional[Any]          =						jnp.ones((1,)			,						dtype=jnp.intaa    )
            __snake_case			:Union[str, Any]          =						jnp.zeros((1, 1, self.cross_attention_dim)			,						dtype=jnp.floataa    )

            __snake_case			:List[Any]          =						jax.random.split(a__    )
            __snake_case			:Optional[int]          =						{"""params""": params_rng, """dropout""": dropout_rng}

            return self.init(a__			,						a__			,						a__			,						a__    )["params"]



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.block_out_channels
            __snake_case			:Optional[int]          =						block_out_channels[0] * 4

            if self.num_attention_heads is not None:
                 raise ValueError(
                     """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."""    )

            # If `num_attention_heads` is not defined (which is the case for most models)
            # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
            # The reason for this behavior is to correct for incorrectly named variables that were introduced
            # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
            # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
            # which is why we correct for the naming here.
            __snake_case			:Optional[int]          =						self.num_attention_heads or self.attention_head_dim

            # input
            __snake_case			:List[str]          =						nn.Conv(
                block_out_channels[0]			,						kernel_size=(3, 3)			,						strides=(1, 1)			,						padding=((1, 1), (1, 1))			,						dtype=self.dtype			,						)

            # time
            __snake_case			:Any          =						FlaxTimesteps(
                block_out_channels[0]			,						flip_sin_to_cos=self.flip_sin_to_cos			,						freq_shift=self.config.freq_shift    )
            __snake_case			:List[str]          =						FlaxTimestepEmbedding(a__			,						dtype=self.dtype    )

            __snake_case			:str          =						self.only_cross_attention
            if isinstance(a__			,						a__    ):
                 __snake_case			:str          =						(only_cross_attention,) * len(self.down_block_types    )

            if isinstance(a__			,						a__    ):
                 __snake_case			:Any          =						(num_attention_heads,) * len(self.down_block_types    )

            # down
            __snake_case			:Union[str, Any]          =						[]
            __snake_case			:Dict          =						block_out_channels[0]
            for i, down_block_type in enumerate(self.down_block_types    ):
                 __snake_case			:Dict          =						output_channel
                 __snake_case			:str          =						block_out_channels[i]
                 __snake_case			:int          =						i == len(a__    ) - 1

                 if down_block_type == "CrossAttnDownBlock2D":
                      __snake_case			:Tuple          =						FlaxCrossAttnDownBlockaD(
                          in_channels=a__			,						out_channels=a__			,						dropout=self.dropout			,						num_layers=self.layers_per_block			,						num_attention_heads=num_attention_heads[i]			,						add_downsample=not is_final_block			,						use_linear_projection=self.use_linear_projection			,						only_cross_attention=only_cross_attention[i]			,						use_memory_efficient_attention=self.use_memory_efficient_attention			,						dtype=self.dtype			,						)
                 else:
                      __snake_case			:Tuple          =						FlaxDownBlockaD(
                          in_channels=a__			,						out_channels=a__			,						dropout=self.dropout			,						num_layers=self.layers_per_block			,						add_downsample=not is_final_block			,						dtype=self.dtype			,						)

                 down_blocks.append(a__    )
            __snake_case			:int          =						down_blocks

            # mid
            __snake_case			:Dict          =						FlaxUNetMidBlockaDCrossAttn(
                in_channels=block_out_channels[-1]			,						dropout=self.dropout			,						num_attention_heads=num_attention_heads[-1]			,						use_linear_projection=self.use_linear_projection			,						use_memory_efficient_attention=self.use_memory_efficient_attention			,						dtype=self.dtype			,						)

            # up
            __snake_case			:Optional[int]          =						[]
            __snake_case			:int          =						list(reversed(a__    )    )
            __snake_case			:Optional[Any]          =						list(reversed(a__    )    )
            __snake_case			:Optional[int]          =						list(reversed(a__    )    )
            __snake_case			:Dict          =						reversed_block_out_channels[0]
            for i, up_block_type in enumerate(self.up_block_types    ):
                 __snake_case			:Optional[int]          =						output_channel
                 __snake_case			:Optional[Any]          =						reversed_block_out_channels[i]
                 __snake_case			:Dict          =						reversed_block_out_channels[min(i + 1			,						len(a__    ) - 1    )]

                 __snake_case			:Optional[int]          =						i == len(a__    ) - 1

                 if up_block_type == "CrossAttnUpBlock2D":
                      __snake_case			:List[str]          =						FlaxCrossAttnUpBlockaD(
                          in_channels=a__			,						out_channels=a__			,						prev_output_channel=a__			,						num_layers=self.layers_per_block + 1			,						num_attention_heads=reversed_num_attention_heads[i]			,						add_upsample=not is_final_block			,						dropout=self.dropout			,						use_linear_projection=self.use_linear_projection			,						only_cross_attention=only_cross_attention[i]			,						use_memory_efficient_attention=self.use_memory_efficient_attention			,						dtype=self.dtype			,						)
                 else:
                      __snake_case			:str          =						FlaxUpBlockaD(
                          in_channels=a__			,						out_channels=a__			,						prev_output_channel=a__			,						num_layers=self.layers_per_block + 1			,						add_upsample=not is_final_block			,						dropout=self.dropout			,						dtype=self.dtype			,						)

                 up_blocks.append(a__    )
                 __snake_case			:Dict          =						output_channel
            __snake_case			:Optional[Any]          =						up_blocks

            # out
            __snake_case			:Union[str, Any]          =						nn.GroupNorm(num_groups=32			,						epsilon=1e-5    )
            __snake_case			:List[Any]          =						nn.Conv(
                self.out_channels			,						kernel_size=(3, 3)			,						strides=(1, 1)			,						padding=((1, 1), (1, 1))			,						dtype=self.dtype			,						)



       def __call__(							self			,						a__			,						a__			,						a__			,						a__=None			,						a__=None			,						a__ = True			,						a__ = False			,						) ->      Union[FlaxUNetaDConditionOutput, Tuple]:






            '''simple docstring'''





            if not isinstance(a__			,						jnp.ndarray    ):
                 __snake_case			:str          =						jnp.array([timesteps]			,						dtype=jnp.intaa    )
            elif isinstance(a__			,						jnp.ndarray    ) and len(timesteps.shape    ) == 0:
                 __snake_case			:Optional[int]          =						timesteps.astype(dtype=jnp.floataa    )
                 __snake_case			:Optional[int]          =						jnp.expand_dims(a__			,						0    )

            __snake_case			:Optional[Any]          =						self.time_proj(a__    )
            __snake_case			:Optional[int]          =						self.time_embedding(a__    )

            # 2. pre-process
            __snake_case			:Dict          =						jnp.transpose(a__			,						(0, 2, 3, 1)    )
            __snake_case			:str          =						self.conv_in(a__    )

            # 3. down
            __snake_case			:Union[str, Any]          =						(sample,)
            for down_block in self.down_blocks:
                 if isinstance(a__			,						a__    ):
                      __snake_case			:Dict          =						down_block(a__			,						a__			,						a__			,						deterministic=not train    )
                 else:
                      __snake_case			:int          =						down_block(a__			,						a__			,						deterministic=not train    )
                 down_block_res_samples += res_samples

            if down_block_additional_residuals is not None:
                 __snake_case			:List[str]          =						()

                 for down_block_res_sample, down_block_additional_residual in zip(
                     a__			,						a__    ):
                      down_block_res_sample += down_block_additional_residual
                      new_down_block_res_samples += (down_block_res_sample,)

                 __snake_case			:Union[str, Any]          =						new_down_block_res_samples

            # 4. mid
            __snake_case			:Union[str, Any]          =						self.mid_block(a__			,						a__			,						a__			,						deterministic=not train    )

            if mid_block_additional_residual is not None:
                 sample += mid_block_additional_residual

            # 5. up
            for up_block in self.up_blocks:
                 __snake_case			:Optional[int]          =						down_block_res_samples[-(self.layers_per_block + 1) :]
                 __snake_case			:Union[str, Any]          =						down_block_res_samples[: -(self.layers_per_block + 1)]
                 if isinstance(a__			,						a__    ):
                      __snake_case			:List[Any]          =						up_block(
                          a__			,						temb=a__			,						encoder_hidden_states=a__			,						res_hidden_states_tuple=a__			,						deterministic=not train			,						)
                 else:
                      __snake_case			:Dict          =						up_block(a__			,						temb=a__			,						res_hidden_states_tuple=a__			,						deterministic=not train    )

        # 6. post-process
            __snake_case			:Optional[Any]          =						self.conv_norm_out(a__    )
            __snake_case			:Any          =						nn.silu(a__    )
            __snake_case			:Dict          =						self.conv_out(a__    )
            __snake_case			:List[str]          =						jnp.transpose(a__			,						(0, 3, 1, 2)    )

            if not return_dict:
                 return (sample,)

            return FlaxUNetaDConditionOutput(sample=a__    )










import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     1  # (0 is vertical, 1 is horizontal)






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case       , __snake_case			:List[Any]          =						get_dataset(snake_case__	,snake_case__  )
     print("""Processing..."""  )
     __snake_case       , __snake_case       , __snake_case			:Tuple          =						update_image_and_anno(snake_case__	,snake_case__	,snake_case__  )

     for index, image in enumerate(snake_case__  ):
          # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
          __snake_case			:str          =						random_chars(32  )
          __snake_case			:Optional[Any]          =						paths[index].split(os.sep  )[-1].rsplit("""."""	,1  )[0]
          __snake_case			:List[str]          =						f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
          cva.imwrite(f'''/{file_root}.jpg'''	,snake_case__	,[cva.IMWRITE_JPEG_QUALITY, 85]  )
          print(f'''Success {index+1}/{len(snake_case__  )} with {file_name}'''  )
          __snake_case			:Optional[int]          =						[]
          for anno in new_annos[index]:
               __snake_case			:str          =						f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
               annos_list.append(snake_case__  )
          with open(f'''/{file_root}.txt'''	,"""w"""  ) as outfile:
               outfile.write("""\n""".join(line for line in annos_list  )  )






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						[]
     __snake_case			:Dict          =						[]
     for label_file in glob.glob(os.path.join(snake_case__	,"""*.txt"""  )  ):
          __snake_case			:Tuple          =						label_file.split(os.sep  )[-1].rsplit("""."""	,1  )[0]
          with open(snake_case__  ) as in_file:
               __snake_case			:Optional[int]          =						in_file.readlines()
          __snake_case			:Any          =						os.path.join(snake_case__	,f'''{label_name}.jpg'''  )

          __snake_case			:List[str]          =						[]
          for obj_list in obj_lists:
               __snake_case			:int          =						obj_list.rstrip("""\n"""  ).split(""" """  )
               boxes.append(
                   [
                       int(obj[0]  ),
                       float(obj[1]  ),
                       float(obj[2]  ),
                       float(obj[3]  ),
                       float(obj[4]  ),
                   ]  )
          if not boxes:
               continue
          img_paths.append(snake_case__  )
          labels.append(snake_case__  )
     return img_paths, labels






def   UpperCamelCase  (       snake_case__     :   list	,snake_case__     :   list	,snake_case__     :   int = 1  ):




     '''simple docstring'''






     __snake_case			:Dict          =						[]
     __snake_case			:Any          =						[]
     __snake_case			:int          =						[]
     for idx in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						[]
          __snake_case			:List[str]          =						img_list[idx]
          path_list.append(snake_case__  )
          __snake_case			:Union[str, Any]          =						anno_list[idx]
          __snake_case			:List[Any]          =						cva.imread(snake_case__  )
          if flip_type == 1:
               __snake_case			:Optional[Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Union[str, Any]          =						1 - bbox[1]
                    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]  )
          elif flip_type == 0:
               __snake_case			:Union[str, Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Optional[Any]          =						1 - bbox[2]
                    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]  )
          new_annos_lists.append(snake_case__  )
          new_imgs_list.append(snake_case__  )
     return new_imgs_list, new_annos_lists, path_list






def   UpperCamelCase  (       snake_case__     :   int = 32  ):




     '''simple docstring'''






     assert number_char > 1, "The number of character should greater than 1"
     __snake_case			:Optional[Any]          =						ascii_lowercase + digits
     return "".join(random.choice(snake_case__  ) for _ in range(snake_case__  )  )


if __name__ == "__main__":
     main()
     print("""DONE ✅""")


