


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
lowerCamelCase__							:						str							     =   """."""

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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				int:
  snake_case__						= SavedModel()
  snake_case__						= []

  with open(os.path.join(__UpperCamelCase							,   '''utils'''							,   '''tf_ops'''							,   '''onnx.json'''				)				) as f:
    snake_case__						= 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()				)

  snake_case__						= 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
  snake_case__						= sorted(__UpperCamelCase				)
  snake_case__						= []

  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__":
   lowerCamelCase__							:						str							     =   argparse.ArgumentParser()
   parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
   parser.add_argument(
       """--opset""", default=1_2, 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)"""
   )
   lowerCamelCase__							:						Any							     =   parser.parse_args()

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







'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A = None,							A = None,							A = None,							A = None,							A = False,							A = True,							):







             '''simple docstring'''







             for i, (image, scale, controlnet) in enumerate(zip(A,							A,							self.nets  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             samples_prev + samples_curr
                             for samples_prev, samples_curr in zip(A,							A  )
                         ]
                         mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample







       def        UpperCamelCase_							(      self,							A,							A = True,							A = None,							A = False,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						save_directory
             for controlnet in self.nets:
                   controlnet.save_pretrained(
                       A,							is_main_process=A,							save_function=A,							safe_serialization=A,							variant=A,							)

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pretrained_model_path + F"_{idx}"

             logger.info(F"{len(A  )} controlnets loaded from {pretrained_model_path}."  )

             if len(A  ) == 0:
                   raise ValueError(
                       F"No ControlNets found under {os.path.dirname(A  )}. Expected at least {pretrained_model_path + '_0'}."  )

             return cls(A  )


