

"""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,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)







@flax.struct.dataclass
class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							42
  snake_case_												=							42







class   __lowercase      (     nn.Module       ):
  snake_case_												=							42
  snake_case_												=							(1_6, 3_2, 9_6, 2_5_6)
  snake_case_												=							jnp.floataa

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       nn.Conv(
       self.block_out_channels[0]       ,kernel_size=(3, 3)       ,padding=((1, 1), (1, 1))       ,dtype=self.dtype       ,)

   UpperCAmelCase__       :	Tuple        =       []
   for i in range(len(self.block_out_channels     ) - 1     ):
    UpperCAmelCase__       :	Union[str, Any]        =       self.block_out_channels[i]
    UpperCAmelCase__       :	Dict        =       self.block_out_channels[i + 1]
    UpperCAmelCase__       :	Optional[int]        =       nn.Conv(
        A       ,kernel_size=(3, 3)       ,padding=((1, 1), (1, 1))       ,dtype=self.dtype       ,)
    blocks.append(A     )
    UpperCAmelCase__       :	Optional[int]        =       nn.Conv(
        A       ,kernel_size=(3, 3)       ,strides=(2, 2)       ,padding=((1, 1), (1, 1))       ,dtype=self.dtype       ,)
    blocks.append(A     )
   UpperCAmelCase__       :	List[Any]        =       blocks

   UpperCAmelCase__       :	Union[str, Any]        =       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		:      Dict       ,A		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       self.conv_in(A     )
   UpperCAmelCase__       :	Tuple        =       nn.silu(A     )

   for block in self.blocks:
    UpperCAmelCase__       :	Tuple        =       block(A     )
    UpperCAmelCase__       :	str        =       nn.silu(A     )

   UpperCAmelCase__       :	Dict        =       self.conv_out(A     )

   return embedding







@flax_register_to_config
class   __lowercase      (     nn.Module						,      __lowerCamelCase						,      __lowerCamelCase       ):
  snake_case_												=							3_2
  snake_case_												=							4
  snake_case_												=							(
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "DownBlock2D",
  )
  snake_case_												=							False
  snake_case_												=							(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
  snake_case_												=							2
  snake_case_												=							8
  snake_case_												=							None
  snake_case_												=							1_2_8_0
  snake_case_												=							0.0
  snake_case_												=							False
  snake_case_												=							jnp.floataa
  snake_case_												=							True
  snake_case_												=							0
  snake_case_												=							"rgb"
  snake_case_												=							(1_6, 3_2, 9_6, 2_5_6)

  def  __lowercase							(	self		:      Tuple       ,A		:      jax.random.KeyArray     ):






   '''simple docstring'''
   # init input tensors
   UpperCAmelCase__       :	Optional[int]        =       (1, self.in_channels, self.sample_size, self.sample_size)
   UpperCAmelCase__       :	Optional[Any]        =       jnp.zeros(A       ,dtype=jnp.floataa     )
   UpperCAmelCase__       :	Union[str, Any]        =       jnp.ones((1,)       ,dtype=jnp.intaa     )
   UpperCAmelCase__       :	int        =       jnp.zeros((1, 1, self.cross_attention_dim)       ,dtype=jnp.floataa     )
   UpperCAmelCase__       :	Dict        =       (1, 3, self.sample_size * 8, self.sample_size * 8)
   UpperCAmelCase__       :	str        =       jnp.zeros(A       ,dtype=jnp.floataa     )

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       jax.random.split(A     )
   UpperCAmelCase__       :	Tuple        =       {"""params""": params_rng, """dropout""": dropout_rng}

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

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       self.block_out_channels
   UpperCAmelCase__       :	Dict        =       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.
   UpperCAmelCase__       :	Dict        =       self.num_attention_heads or self.attention_head_dim

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

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

   UpperCAmelCase__       :	Dict        =       FlaxControlNetConditioningEmbedding(
       conditioning_embedding_channels=block_out_channels[0]       ,block_out_channels=self.conditioning_embedding_out_channels       ,)

   UpperCAmelCase__       :	Any        =       self.only_cross_attention
   if isinstance(A       ,A     ):
    UpperCAmelCase__       :	Optional[int]        =       (only_cross_attention,) * len(self.down_block_types     )

   if isinstance(A       ,A     ):
    UpperCAmelCase__       :	Optional[Any]        =       (num_attention_heads,) * len(self.down_block_types     )

   # down
   UpperCAmelCase__       :	Dict        =       []
   UpperCAmelCase__       :	Optional[int]        =       []

   UpperCAmelCase__       :	Dict        =       block_out_channels[0]

   UpperCAmelCase__       :	Any        =       nn.Conv(
       A       ,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(A     )

   for i, down_block_type in enumerate(self.down_block_types     ):
    UpperCAmelCase__       :	Optional[Any]        =       output_channel
    UpperCAmelCase__       :	Any        =       block_out_channels[i]
    UpperCAmelCase__       :	List[str]        =       i == len(A     ) - 1

    if down_block_type == "CrossAttnDownBlock2D":
     UpperCAmelCase__       :	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]       ,dtype=self.dtype       ,)
    else:
     UpperCAmelCase__       :	List[str]        =       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     )

    for _ in range(self.layers_per_block     ):
     UpperCAmelCase__       :	Tuple        =       nn.Conv(
         A       ,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(A     )

    if not is_final_block:
     UpperCAmelCase__       :	Optional[int]        =       nn.Conv(
         A       ,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(A     )

   UpperCAmelCase__       :	str        =       down_blocks
   UpperCAmelCase__       :	Optional[Any]        =       controlnet_down_blocks

   # mid
   UpperCAmelCase__       :	Dict        =       block_out_channels[-1]
   UpperCAmelCase__       :	Optional[Any]        =       FlaxUNetMidBlockaDCrossAttn(
       in_channels=A       ,dropout=self.dropout       ,num_attention_heads=num_attention_heads[-1]       ,use_linear_projection=self.use_linear_projection       ,dtype=self.dtype       ,)

   UpperCAmelCase__       :	Union[str, Any]        =       nn.Conv(
       A       ,kernel_size=(1, 1)       ,padding="""VALID"""       ,kernel_init=nn.initializers.zeros_init()       ,bias_init=nn.initializers.zeros_init()       ,dtype=self.dtype       ,)




  def __call__(	self		:      List[str]       ,A		:      Any       ,A		:      Tuple       ,A		:      str       ,A		:      Optional[int]       ,A		:      float = 1.0       ,A		:      bool = True       ,A		:      bool = False       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.controlnet_conditioning_channel_order
   if channel_order == "bgr":
    UpperCAmelCase__       :	Optional[Any]        =       jnp.flip(A       ,axis=1     )

   # 1. time
   if not isinstance(A       ,jnp.ndarray     ):
    UpperCAmelCase__       :	Union[str, Any]        =       jnp.array([timesteps]       ,dtype=jnp.intaa     )
   elif isinstance(A       ,jnp.ndarray     ) and len(timesteps.shape     ) == 0:
    UpperCAmelCase__       :	Dict        =       timesteps.astype(dtype=jnp.floataa     )
    UpperCAmelCase__       :	Any        =       jnp.expand_dims(A       ,0     )

   UpperCAmelCase__       :	str        =       self.time_proj(A     )
   UpperCAmelCase__       :	List[Any]        =       self.time_embedding(A     )

   # 2. pre-process
   UpperCAmelCase__       :	Dict        =       jnp.transpose(A       ,(0, 2, 3, 1)     )
   UpperCAmelCase__       :	Tuple        =       self.conv_in(A     )

   UpperCAmelCase__       :	List[Any]        =       jnp.transpose(A       ,(0, 2, 3, 1)     )
   UpperCAmelCase__       :	Dict        =       self.controlnet_cond_embedding(A     )
   sample += controlnet_cond

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

   # 4. mid
   UpperCAmelCase__       :	Any        =       self.mid_block(A       ,A       ,A       ,deterministic=not train     )

   # 5. contronet blocks
   UpperCAmelCase__       :	Union[str, Any]        =       ()
   for down_block_res_sample, controlnet_block in zip(A       ,self.controlnet_down_blocks     ):
    UpperCAmelCase__       :	Tuple        =       controlnet_block(A     )
    controlnet_down_block_res_samples += (down_block_res_sample,)

   UpperCAmelCase__       :	int        =       controlnet_down_block_res_samples

   UpperCAmelCase__       :	Tuple        =       self.controlnet_mid_block(A     )

   # 6. scaling
   UpperCAmelCase__       :	List[Any]        =       [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=A       ,mid_block_res_sample=A     )










import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_      :      Optional[int]    =     '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
a_      :      List[str]    =     [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= SavedModel()
 a__												= []

 with open(os.path.join(__UpperCAmelCase							,  '''utils'''							,  '''tf_ops'''							,  '''onnx.json'''       )       ) as f:
  a__												= json.load(__UpperCAmelCase       )['''opsets''']

 for i in range(1							,  opset + 1       ):
  onnx_ops.extend(onnx_opsets[str(__UpperCAmelCase       )]       )

 with open(__UpperCAmelCase							,  '''rb'''       ) as f:
  saved_model.ParseFromString(f.read()       )

 a__												= set()

 # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
 for meta_graph in saved_model.meta_graphs:
  # Add operations in the graph definition
  model_op_names.update(node.op for node in meta_graph.graph_def.node       )

  # Go through the functions in the graph definition
  for func in meta_graph.graph_def.library.function:
   # Add operations in each function
   model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
 a__												= sorted(__UpperCAmelCase       )
 a__												= []

 for op in model_op_names:
  if op not in onnx_ops and op not in INTERNAL_OPS:
   incompatible_ops.append(__UpperCAmelCase       )

 if strict and len(__UpperCAmelCase       ) > 0:
  raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops       )
 elif len(__UpperCAmelCase       ) > 0:
  print(f"Found the following incompatible ops for the opset {opset}:"       )
  print(*__UpperCAmelCase							,  sep='''\n'''       )
 else:
  print(f"The saved model {saved_model_path} can properly be converted with ONNX."       )


if __name__ == "__main__":
     a_      :      Dict    =     argparse.ArgumentParser()
     parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
     parser.add_argument(
         '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
     )
     parser.add_argument(
         '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
     )
     parser.add_argument(
         '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
     )
     a_      :      str    =     parser.parse_args()

     if args.framework == "onnx":
          onnx_compliancy(args.saved_model_path, args.strict, args.opset)





