'''simple docstring'''
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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						42






@flax_register_to_config
class 			__lowercase		(						nn.Module			,							_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						32
     SCREAMING_SNAKE_CASE__		   =						4
     SCREAMING_SNAKE_CASE__		   =						4
     SCREAMING_SNAKE_CASE__		   =						(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     SCREAMING_SNAKE_CASE__		   =						("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						(320, 640, 1280, 1280)
     SCREAMING_SNAKE_CASE__		   =						2
     SCREAMING_SNAKE_CASE__		   =						8
     SCREAMING_SNAKE_CASE__		   =						None
     SCREAMING_SNAKE_CASE__		   =						1280
     SCREAMING_SNAKE_CASE__		   =						0.0
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						jnp.floataa
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						0
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # init input tensors
         __UpperCamelCase	:	Tuple		    =  (1, self.in_channels, self.sample_size, self.sample_size)
         __UpperCamelCase	:	Optional[int]		    =  jnp.zeros(_lowerCamelCase     ,			dtype=jnp.floataa    )
         __UpperCamelCase	:	Optional[int]		    =  jnp.ones((1,)     ,			dtype=jnp.intaa    )
         __UpperCamelCase	:	List[Any]		    =  jnp.zeros((1, 1, self.cross_attention_dim)     ,			dtype=jnp.floataa    )

         __UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  jax.random.split(_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  {'params': params_rng, 'dropout': dropout_rng}

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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  self.block_out_channels
         __UpperCamelCase	:	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.
         __UpperCamelCase	:	int		    =  self.num_attention_heads or self.attention_head_dim

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

         # time
         __UpperCamelCase	:	Dict		    =  FlaxTimesteps(
             block_out_channels[0]     ,			flip_sin_to_cos=self.flip_sin_to_cos     ,			freq_shift=self.config.freq_shift    )
         __UpperCamelCase	:	Optional[int]		    =  FlaxTimestepEmbedding(_lowerCamelCase     ,			dtype=self.dtype    )

         __UpperCamelCase	:	Any		    =  self.only_cross_attention
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Any		    =  (only_cross_attention,) * len(self.down_block_types    )

         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Optional[int]		    =  (num_attention_heads,) * len(self.down_block_types    )

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

             if down_block_type == "CrossAttnDownBlock2D":
                 __UpperCamelCase	:	Optional[Any]		    =  FlaxCrossAttnDownBlockaD(
                     in_channels=_lowerCamelCase     ,			out_channels=_lowerCamelCase     ,			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:
                 __UpperCamelCase	:	Optional[Any]		    =  FlaxDownBlockaD(
                     in_channels=_lowerCamelCase     ,			out_channels=_lowerCamelCase     ,			dropout=self.dropout     ,			num_layers=self.layers_per_block     ,			add_downsample=not is_final_block     ,			dtype=self.dtype     ,			)

             down_blocks.append(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  down_blocks

         # mid
         __UpperCamelCase	:	Optional[Any]		    =  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
         __UpperCamelCase	:	str		    =  []
         __UpperCamelCase	:	Tuple		    =  list(reversed(_lowerCamelCase    )    )
         __UpperCamelCase	:	Optional[Any]		    =  list(reversed(_lowerCamelCase    )    )
         __UpperCamelCase	:	List[str]		    =  list(reversed(_lowerCamelCase    )    )
         __UpperCamelCase	:	Any		    =  reversed_block_out_channels[0]
         for i, up_block_type in enumerate(self.up_block_types    ):
             __UpperCamelCase	:	int		    =  output_channel
             __UpperCamelCase	:	List[Any]		    =  reversed_block_out_channels[i]
             __UpperCamelCase	:	List[Any]		    =  reversed_block_out_channels[min(i + 1     ,			len(_lowerCamelCase    ) - 1    )]

             __UpperCamelCase	:	Dict		    =  i == len(_lowerCamelCase    ) - 1

             if up_block_type == "CrossAttnUpBlock2D":
                 __UpperCamelCase	:	Dict		    =  FlaxCrossAttnUpBlockaD(
                     in_channels=_lowerCamelCase     ,			out_channels=_lowerCamelCase     ,			prev_output_channel=_lowerCamelCase     ,			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:
                 __UpperCamelCase	:	List[str]		    =  FlaxUpBlockaD(
                     in_channels=_lowerCamelCase     ,			out_channels=_lowerCamelCase     ,			prev_output_channel=_lowerCamelCase     ,			num_layers=self.layers_per_block + 1     ,			add_upsample=not is_final_block     ,			dropout=self.dropout     ,			dtype=self.dtype     ,			)

             up_blocks.append(_lowerCamelCase    )
             __UpperCamelCase	:	Dict		    =  output_channel
         __UpperCamelCase	:	str		    =  up_blocks

         # out
         __UpperCamelCase	:	Optional[int]		    =  nn.GroupNorm(num_groups=3_2     ,			epsilon=1E-5    )
         __UpperCamelCase	:	Optional[Any]		    =  nn.Conv(
             self.out_channels     ,			kernel_size=(3, 3)     ,			strides=(1, 1)     ,			padding=((1, 1), (1, 1))     ,			dtype=self.dtype     ,			)







     def __call__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase = True     ,			_lowerCamelCase = False     ,			):
         # 1. time
         if not isinstance(_lowerCamelCase     ,			jnp.ndarray    ):
             __UpperCamelCase	:	Optional[int]		    =  jnp.array([timesteps]     ,			dtype=jnp.intaa    )
         elif isinstance(_lowerCamelCase     ,			jnp.ndarray    ) and len(timesteps.shape    ) == 0:
             __UpperCamelCase	:	Optional[Any]		    =  timesteps.astype(dtype=jnp.floataa    )
             __UpperCamelCase	:	Tuple		    =  jnp.expand_dims(_lowerCamelCase     ,			0    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.time_proj(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.time_embedding(_lowerCamelCase    )

         # 2. pre-process
         __UpperCamelCase	:	List[str]		    =  jnp.transpose(_lowerCamelCase     ,			(0, 2, 3, 1)    )
         __UpperCamelCase	:	Optional[int]		    =  self.conv_in(_lowerCamelCase    )

         # 3. down
         __UpperCamelCase	:	Any		    =  (sample,)
         for down_block in self.down_blocks:
             if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
                 __UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  down_block(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			deterministic=not train    )
             else:
                 __UpperCamelCase    ,					__UpperCamelCase	:	List[str]		    =  down_block(_lowerCamelCase     ,			_lowerCamelCase     ,			deterministic=not train    )
             down_block_res_samples += res_samples

         if down_block_additional_residuals is not None:
             __UpperCamelCase	:	Union[str, Any]		    =  ()

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

             __UpperCamelCase	:	Any		    =  new_down_block_res_samples

         # 4. mid
         __UpperCamelCase	:	Optional[Any]		    =  self.mid_block(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			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:
             __UpperCamelCase	:	List[Any]		    =  down_block_res_samples[-(self.layers_per_block + 1) :]
             __UpperCamelCase	:	List[str]		    =  down_block_res_samples[: -(self.layers_per_block + 1)]
             if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	List[Any]		    =  up_block(
                     _lowerCamelCase     ,			temb=_lowerCamelCase     ,			encoder_hidden_states=_lowerCamelCase     ,			res_hidden_states_tuple=_lowerCamelCase     ,			deterministic=not train     ,			)
             else:
                 __UpperCamelCase	:	int		    =  up_block(_lowerCamelCase     ,			temb=_lowerCamelCase     ,			res_hidden_states_tuple=_lowerCamelCase     ,			deterministic=not train    )

        # 6. post-process
         __UpperCamelCase	:	str		    =  self.conv_norm_out(_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  nn.silu(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  self.conv_out(_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  jnp.transpose(_lowerCamelCase     ,			(0, 3, 1, 2)    )

         if not return_dict:
             return (sample,)

         return FlaxUNetaDConditionOutput(sample=_lowerCamelCase    )




'''simple docstring'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url







@pytest.mark.parametrize('repo_id'				,     ['canonical_dataset_name', 'org-name/dataset-name']			)
@pytest.mark.parametrize('path'				,     ['filename.csv', 'filename with blanks.csv']			)
@pytest.mark.parametrize('revision'				,     [None, 'v2']			)
def  _UpperCamelCase (     _a	:	Any				,     _a	:	int				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  hf_hub_url(repo_id=_a				,     path=_a				,     revision=_a			)
    assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_a			)}"""



