






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      :       Any				  =   "."

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Dict			,				__lowercase   :			int    )						->      str:





    """simple docstring"""

    __A         =	SavedModel()
    __A         =	[]

    with open(os.path.join(lowerCAmelCase__			,				"""utils"""			,				"""tf_ops"""			,				"""onnx.json"""    )    ) as f:
        __A         =	json.load(lowerCAmelCase__    )["""opsets"""]

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

    with open(lowerCAmelCase__			,				"""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(lowerCAmelCase__    )
    __A         =	[]

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

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


if __name__ == "__main__":
    __a      :       List[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=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      :       int				  =   parser.parse_args()

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












from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				42


class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''




     @register_to_config
     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			Tuple[str] = ("DownEncoderBlock2D",) ,	UpperCamelCase_       :			Tuple[str] = ("UpDecoderBlock2D",) ,	UpperCamelCase_       :			Tuple[int] = (64,) ,	UpperCamelCase_       :			int = 1 ,	UpperCamelCase_       :			str = "silu" ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			int = 256 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			float = 0.18215 ,	UpperCamelCase_       :			str = "group" ,	):


         """simple docstring"""



         super().__init__()

         # pass init params to Encoder
         __A         =	Encoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	down_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	double_z=UpperCamelCase_ ,	)

         __A         =	vq_embed_dim if vq_embed_dim is not None else latent_channels

         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
         __A         =	VectorQuantizer(UpperCamelCase_ ,	UpperCamelCase_ ,	beta=0.25 ,	remap=UpperCamelCase_ ,	sane_index_shape=UpperCamelCase_				)
         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)

         # pass init params to Decoder
         __A         =	Decoder(
             in_channels=UpperCamelCase_ ,	out_channels=UpperCamelCase_ ,	up_block_types=UpperCamelCase_ ,	block_out_channels=UpperCamelCase_ ,	layers_per_block=UpperCamelCase_ ,	act_fn=UpperCamelCase_ ,	norm_num_groups=UpperCamelCase_ ,	norm_type=UpperCamelCase_ ,	)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	self.encoder(UpperCamelCase_				)
         __A         =	self.quant_conv(UpperCamelCase_				)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=UpperCamelCase_				)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         if not force_not_quantize:
             __A    ,	__A    ,	__A         =	self.quantize(UpperCamelCase_				)
         else:
             __A         =	h
         __A         =	self.post_quant_conv(UpperCamelCase_				)
         __A         =	self.decoder(UpperCamelCase_ ,	quant if self.config.norm_type == """spatial""" else None				)

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	sample
         __A         =	self.encode(UpperCamelCase_				).latents
         __A         =	self.decode(UpperCamelCase_				).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)




