
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,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)







@flax.struct.dataclass
class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							List[Any]     =	42
     UpperCAmelCase__       :							Tuple     =	42







class   lowerCamelCase	(						nn.Module						):
     UpperCAmelCase__       :							str     =	42
     UpperCAmelCase__       :							Tuple     =	(16, 32, 96, 2_56)
     UpperCAmelCase__       :							Any     =	jnp.floataa





     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[Any]:
         snake_case				  =      nn.Conv(
             self.block_out_channels[0]       ,					kernel_size=(3, 3)       ,					padding=((1, 1), (1, 1))       ,					dtype=self.dtype       ,					)

         snake_case				  =      []
         for i in range(len(self.block_out_channels     ) - 1     ):
             snake_case				  =      self.block_out_channels[i]
             snake_case				  =      self.block_out_channels[i + 1]
             snake_case				  =      nn.Conv(
                 __UpperCamelCase       ,					kernel_size=(3, 3)       ,					padding=((1, 1), (1, 1))       ,					dtype=self.dtype       ,					)
             blocks.append(__UpperCamelCase     )
             snake_case				  =      nn.Conv(
                 __UpperCamelCase       ,					kernel_size=(3, 3)       ,					strides=(2, 2)       ,					padding=((1, 1), (1, 1))       ,					dtype=self.dtype       ,					)
             blocks.append(__UpperCamelCase     )
         snake_case				  =      blocks

         snake_case				  =      nn.Conv(
             self.conditioning_embedding_channels       ,					kernel_size=(3, 3)       ,					padding=((1, 1), (1, 1))       ,					kernel_init=nn.initializers.zeros_init()       ,					bias_init=nn.initializers.zeros_init()       ,					dtype=self.dtype       ,					)





     def __call__(self   :					str       ,					_A   :					List[str]     )		->		str:
         snake_case				  =      self.conv_in(__UpperCamelCase     )
         snake_case				  =      nn.silu(__UpperCamelCase     )

         for block in self.blocks:
             snake_case				  =      block(__UpperCamelCase     )
             snake_case				  =      nn.silu(__UpperCamelCase     )

         snake_case				  =      self.conv_out(__UpperCamelCase     )

         return embedding







@flax_register_to_config
class   lowerCamelCase	(						nn.Module							,				A_							,				A_						):
     UpperCAmelCase__       :							List[Any]     =	32
     UpperCAmelCase__       :							int     =	4
     UpperCAmelCase__       :							Optional[int]     =	(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     UpperCAmelCase__       :							Any     =	False
     UpperCAmelCase__       :							Optional[int]     =	(3_20, 6_40, 12_80, 12_80)
     UpperCAmelCase__       :							Dict     =	2
     UpperCAmelCase__       :							str     =	8
     UpperCAmelCase__       :							Optional[Any]     =	None
     UpperCAmelCase__       :							str     =	12_80
     UpperCAmelCase__       :							List[Any]     =	0.0
     UpperCAmelCase__       :							Union[str, Any]     =	False
     UpperCAmelCase__       :							int     =	jnp.floataa
     UpperCAmelCase__       :							Dict     =	True
     UpperCAmelCase__       :							str     =	0
     UpperCAmelCase__       :							int     =	"rgb"
     UpperCAmelCase__       :							Dict     =	(16, 32, 96, 2_56)





     def 	UpperCAmelCase(self   :					str       ,					_A   :					jax.random.KeyArray     )		->		List[Any]:
         # init input tensors
         snake_case				  =      (1, self.in_channels, self.sample_size, self.sample_size)
         snake_case				  =      jnp.zeros(__UpperCamelCase       ,					dtype=jnp.floataa     )
         snake_case				  =      jnp.ones((1,)       ,					dtype=jnp.intaa     )
         snake_case				  =      jnp.zeros((1, 1, self.cross_attention_dim)       ,					dtype=jnp.floataa     )
         snake_case				  =      (1, 3, self.sample_size * 8, self.sample_size * 8)
         snake_case				  =      jnp.zeros(__UpperCamelCase       ,					dtype=jnp.floataa     )

         snake_case  ,							snake_case				  =      jax.random.split(__UpperCamelCase     )
         snake_case				  =      {"params": params_rng, "dropout": dropout_rng}

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





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         snake_case				  =      self.block_out_channels
         snake_case				  =      block_out_channels[0] * 4

         # 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				  =      self.num_attention_heads or self.attention_head_dim

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

         # time
         snake_case				  =      FlaxTimesteps(
             block_out_channels[0]       ,					flip_sin_to_cos=self.flip_sin_to_cos       ,					freq_shift=self.config.freq_shift     )
         snake_case				  =      FlaxTimestepEmbedding(__UpperCamelCase       ,					dtype=self.dtype     )

         snake_case				  =      FlaxControlNetConditioningEmbedding(
             conditioning_embedding_channels=block_out_channels[0]       ,					block_out_channels=self.conditioning_embedding_out_channels       ,					)

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

         if isinstance(__UpperCamelCase       ,					__UpperCamelCase     ):
             snake_case				  =      (num_attention_heads,) * len(self.down_block_types     )

         # down
         snake_case				  =      []
         snake_case				  =      []

         snake_case				  =      block_out_channels[0]

         snake_case				  =      nn.Conv(
             __UpperCamelCase       ,					kernel_size=(1, 1)       ,					padding="VALID"       ,					kernel_init=nn.initializers.zeros_init()       ,					bias_init=nn.initializers.zeros_init()       ,					dtype=self.dtype       ,					)
         controlnet_down_blocks.append(__UpperCamelCase     )

         for i, down_block_type in enumerate(self.down_block_types     ):
             snake_case				  =      output_channel
             snake_case				  =      block_out_channels[i]
             snake_case				  =      i == len(__UpperCamelCase     ) - 1

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

             down_blocks.append(__UpperCamelCase     )

             for _ in range(self.layers_per_block     ):
                 snake_case				  =      nn.Conv(
                     __UpperCamelCase       ,					kernel_size=(1, 1)       ,					padding="VALID"       ,					kernel_init=nn.initializers.zeros_init()       ,					bias_init=nn.initializers.zeros_init()       ,					dtype=self.dtype       ,					)
                 controlnet_down_blocks.append(__UpperCamelCase     )

             if not is_final_block:
                 snake_case				  =      nn.Conv(
                     __UpperCamelCase       ,					kernel_size=(1, 1)       ,					padding="VALID"       ,					kernel_init=nn.initializers.zeros_init()       ,					bias_init=nn.initializers.zeros_init()       ,					dtype=self.dtype       ,					)
                 controlnet_down_blocks.append(__UpperCamelCase     )

         snake_case				  =      down_blocks
         snake_case				  =      controlnet_down_blocks

         # mid
         snake_case				  =      block_out_channels[-1]
         snake_case				  =      FlaxUNetMidBlockaDCrossAttn(
             in_channels=__UpperCamelCase       ,					dropout=self.dropout       ,					num_attention_heads=num_attention_heads[-1]       ,					use_linear_projection=self.use_linear_projection       ,					dtype=self.dtype       ,					)

         snake_case				  =      nn.Conv(
             __UpperCamelCase       ,					kernel_size=(1, 1)       ,					padding="VALID"       ,					kernel_init=nn.initializers.zeros_init()       ,					bias_init=nn.initializers.zeros_init()       ,					dtype=self.dtype       ,					)





     def __call__(self   :					Dict       ,					_A   :					List[Any]       ,					_A   :					Any       ,					_A   :					Optional[Any]       ,					_A   :					Optional[Any]       ,					_A   :					float = 1.0       ,					_A   :					bool = True       ,					_A   :					bool = False       ,					)		->		Union[str, Any]:
         snake_case				  =      self.controlnet_conditioning_channel_order
         if channel_order == "bgr":
             snake_case				  =      jnp.flip(__UpperCamelCase       ,					axis=1     )

         # 1. time
         if not isinstance(__UpperCamelCase       ,					jnp.ndarray     ):
             snake_case				  =      jnp.array([timesteps]       ,					dtype=jnp.intaa     )
         elif isinstance(__UpperCamelCase       ,					jnp.ndarray     ) and len(timesteps.shape     ) == 0:
             snake_case				  =      timesteps.astype(dtype=jnp.floataa     )
             snake_case				  =      jnp.expand_dims(__UpperCamelCase       ,					0     )

         snake_case				  =      self.time_proj(__UpperCamelCase     )
         snake_case				  =      self.time_embedding(__UpperCamelCase     )

         # 2. pre-process
         snake_case				  =      jnp.transpose(__UpperCamelCase       ,					(0, 2, 3, 1)     )
         snake_case				  =      self.conv_in(__UpperCamelCase     )

         snake_case				  =      jnp.transpose(__UpperCamelCase       ,					(0, 2, 3, 1)     )
         snake_case				  =      self.controlnet_cond_embedding(__UpperCamelCase     )
         sample += controlnet_cond

         # 3. down
         snake_case				  =      (sample,)
         for down_block in self.down_blocks:
             if isinstance(__UpperCamelCase       ,					__UpperCamelCase     ):
                 snake_case  ,							snake_case				  =      down_block(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					deterministic=not train     )
             else:
                 snake_case  ,							snake_case				  =      down_block(__UpperCamelCase       ,					__UpperCamelCase       ,					deterministic=not train     )
             down_block_res_samples += res_samples

         # 4. mid
         snake_case				  =      self.mid_block(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					deterministic=not train     )

         # 5. contronet blocks
         snake_case				  =      ()
         for down_block_res_sample, controlnet_block in zip(__UpperCamelCase       ,					self.controlnet_down_blocks     ):
             snake_case				  =      controlnet_block(__UpperCamelCase     )
             controlnet_down_block_res_samples += (down_block_res_sample,)

         snake_case				  =      controlnet_down_block_res_samples

         snake_case				  =      self.controlnet_mid_block(__UpperCamelCase     )

         # 6. scaling
         snake_case				  =      [sample * conditioning_scale for sample in down_block_res_samples]
         mid_block_res_sample *= conditioning_scale

         if not return_dict:
             return (down_block_res_samples, mid_block_res_sample)

         return FlaxControlNetOutput(
             down_block_res_samples=__UpperCamelCase       ,					mid_block_res_sample=__UpperCamelCase     )



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,					return_dict=_A     ).to(_A     )
         snake_case				  =      AutoTokenizer.from_pretrained("google/mt5-small"     )

         snake_case				  =      tokenizer("Hello there"       ,					return_tensors="pt"     ).input_ids
         snake_case				  =      tokenizer("Hi I am"       ,					return_tensors="pt"     ).input_ids

         snake_case				  =      model(input_ids.to(_A     )       ,					labels=labels.to(_A     )     ).loss
         snake_case				  =      -(labels.shape[-1] * loss.item())

         snake_case				  =      -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )

