import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[Any] 						=						[]
    if isinstance(__A   , __A	):
        for v in tree.values():
            shapes.extend(_fetch_dims(__A	)	)
    elif isinstance(__A   , (list, tuple)	):
        for t in tree:
            shapes.extend(_fetch_dims(__A	)	)
    elif isinstance(__A   , torch.Tensor	):
        shapes.append(tree.shape	)
    else:
        raise ValueError("""Not supported"""	)

    return shapes







@torch.jit.ignore
def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						[]
    for d in reversed(__A	):
        idx.append(flat_idx % d	)
        _lowerCamelCase     :		List[str] 						=						flat_idx // d

    return tuple(reversed(__A	)	)







@torch.jit.ignore
def 						A__  (					__A   , __A   , __A   , __A = None   , __A = None   , ):







    '''simple docstring'''

    # start_edges and end_edges both indicate whether, starting from any given
    # dimension, the start/end index is at the top/bottom edge of the
    # corresponding tensor, modeled as a tree
    def reduce_edge_list(__A	) -> None:
        _lowerCamelCase     :		Optional[int] 						=						True
        for i in range(len(__A	)	):
            _lowerCamelCase     :		int 						=						-1 * (i + 1)
            l[reversed_idx] &= tally
            _lowerCamelCase     :		str 						=						l[reversed_idx]

    if start_edges is None:
        _lowerCamelCase     :		Optional[int] 						=						[s == 0 for s in start]
        reduce_edge_list(__A	)
    if end_edges is None:
        _lowerCamelCase     :		List[str] 						=						[e == (d - 1) for e, d in zip(__A   , __A	)]
        reduce_edge_list(__A	)

    # Base cases. Either start/end are empty and we're done, or the final,
    # one-dimensional tensor can be simply sliced
    if len(__A	) == 0:
        return [()]
    elif len(__A	) == 1:
        return [(slice(start[0]   , end[0] + 1	),)]

    _lowerCamelCase     :		List[Tuple[slice, ...]] 						=						[]
    _lowerCamelCase     :		List[slice] 						=						[]

    # Dimensions common to start and end can be selected directly
    for s, e in zip(__A   , __A	):
        if s == e:
            path_list.append(slice(__A   , s + 1	)	)
        else:
            break

    _lowerCamelCase     :		Tuple[slice, ...] 						=						tuple(__A	)
    _lowerCamelCase     :		Tuple 						=						len(__A	)

    # start == end, and we're done
    if divergence_idx == len(__A	):
        return [path]

    def upper() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        _lowerCamelCase     :		List[Any] 						=						start[divergence_idx]
        return tuple(
            path + (slice(__A   , sdi + 1	),) + s
            for s in _get_minimal_slice_set(
                start[divergence_idx + 1 :]   , [d - 1 for d in dims[divergence_idx + 1 :]]   , dims[divergence_idx + 1 :]   , start_edges=start_edges[divergence_idx + 1 :]   , end_edges=[True for _ in end_edges[divergence_idx + 1 :]]   , )	)

    def lower() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        _lowerCamelCase     :		List[str] 						=						end[divergence_idx]
        return tuple(
            path + (slice(__A   , edi + 1	),) + s
            for s in _get_minimal_slice_set(
                [0 for _ in start[divergence_idx + 1 :]]   , end[divergence_idx + 1 :]   , dims[divergence_idx + 1 :]   , start_edges=[True for _ in start_edges[divergence_idx + 1 :]]   , end_edges=end_edges[divergence_idx + 1 :]   , )	)

    # If both start and end are at the edges of the subtree rooted at
    # divergence_idx, we can just select the whole subtree at once
    if start_edges[divergence_idx] and end_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]   , end[divergence_idx] + 1	),)	)
    # If just start is at the edge, we can grab almost all of the subtree,
    # treating only the ragged bottom edge as an edge case
    elif start_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]   , end[divergence_idx]	),)	)
        slices.extend(lower()	)
    # Analogous to the previous case, but the top is ragged this time
    elif end_edges[divergence_idx]:
        slices.extend(upper()	)
        slices.append(path + (slice(start[divergence_idx] + 1   , end[divergence_idx] + 1	),)	)
    # If both sides of the range are ragged, we need to handle both sides
    # separately. If there's contiguous meat in between them, we can index it
    # in one big chunk
    else:
        slices.extend(upper()	)
        _lowerCamelCase     :		Dict 						=						end[divergence_idx] - start[divergence_idx]
        if middle_ground > 1:
            slices.append(path + (slice(start[divergence_idx] + 1   , end[divergence_idx]	),)	)
        slices.extend(lower()	)

    return slices







@torch.jit.ignore
def 						A__  (					__A   , __A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						t.shape[:no_batch_dims]
    _lowerCamelCase     :		str 						=						list(_flat_idx_to_idx(__A   , __A	)	)
    # _get_minimal_slice_set is inclusive
    _lowerCamelCase     :		List[str] 						=						list(_flat_idx_to_idx(flat_end - 1   , __A	)	)

    # Get an ordered list of slices to perform
    _lowerCamelCase     :		int 						=						_get_minimal_slice_set(
        __A   , __A   , __A   , )

    _lowerCamelCase     :		List[Any] 						=						[t[s] for s in slices]

    return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]	) for s in sliced_tensors]	)







def 						A__  (					__A   , __A   , __A   , __A   , __A = False   , __A = None   , __A = False   , ):







    '''simple docstring'''
    if not (len(__A	) > 0):
        raise ValueError("""Must provide at least one input"""	)

    _lowerCamelCase     :		Dict 						=						[shape[:no_batch_dims] for shape in _fetch_dims(__A	)]
    _lowerCamelCase     :		Union[str, Any] 						=						tuple([max(__A	) for s in zip(*__A	)]	)

    def _prep_inputs(__A	) -> torch.Tensor:
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]	) == no_batch_dims:
                _lowerCamelCase     :		Tuple 						=						t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
            _lowerCamelCase     :		int 						=						t.reshape(-1   , *t.shape[no_batch_dims:]	)
        else:
            _lowerCamelCase     :		Union[str, Any] 						=						t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
        return t

    _lowerCamelCase     :		Dict[str, Any] 						=						tensor_tree_map(_prep_inputs   , __A	)
    _lowerCamelCase     :		str 						=						None
    if _out is not None:
        _lowerCamelCase     :		int 						=						tensor_tree_map(lambda __A	: t.view([-1] + list(t.shape[no_batch_dims:]	)	)   , _out	)

    _lowerCamelCase     :		List[str] 						=						1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    _lowerCamelCase     :		Union[str, Any] 						=						flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

    def _select_chunk(__A	) -> torch.Tensor:
        return t[i : i + chunk_size] if t.shape[0] != 1 else t

    _lowerCamelCase     :		Any 						=						0
    _lowerCamelCase     :		List[Any] 						=						prepped_outputs
    for _ in range(__A	):
        # Chunk the input
        if not low_mem:
            _lowerCamelCase     :		Dict 						=						_select_chunk
        else:
            _lowerCamelCase     :		Optional[int] 						=						partial(
                _chunk_slice   , flat_start=__A   , flat_end=min(__A   , i + chunk_size	)   , no_batch_dims=len(__A	)   , )

        _lowerCamelCase     :		Dict[str, Any] 						=						tensor_tree_map(__A   , __A	)

        # Run the layer on the chunk
        _lowerCamelCase     :		Dict 						=						layer(**__A	)

        # Allocate space for the output
        if out is None:
            _lowerCamelCase     :		Dict 						=						tensor_tree_map(lambda __A	: t.new_zeros((flat_batch_dim,) + t.shape[1:]	)   , __A	)

        # Put the chunk in its pre-allocated space
        if isinstance(__A   , __A	):

            def assign(__A   , __A	) -> None:
                for k, v in da.items():
                    if isinstance(__A   , __A	):
                        assign(__A   , da[k]	)
                    else:
                        if _add_into_out:
                            v[i : i + chunk_size] += da[k]
                        else:
                            _lowerCamelCase     :		List[str] 						=						da[k]

            assign(__A   , __A	)
        elif isinstance(__A   , __A	):
            for xa, xa in zip(__A   , __A	):
                if _add_into_out:
                    xa[i : i + chunk_size] += xa
                else:
                    _lowerCamelCase     :		Optional[int] 						=						xa
        elif isinstance(__A   , torch.Tensor	):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                _lowerCamelCase     :		Tuple 						=						output_chunk
        else:
            raise ValueError("""Not supported"""	)

        i += chunk_size

    _lowerCamelCase     :		Optional[int] 						=						tensor_tree_map(lambda __A	: t.view(orig_batch_dims + t.shape[1:]	)   , __A	)

    return out




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				int = 512    ,  )				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						max_chunk_size
          _lowerCamelCase     :		Optional[int] 						=						None
          _lowerCamelCase     :		Optional[tuple] 						=						None



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Callable    ,  _UpperCamelCase     :				tuple    ,  _UpperCamelCase     :				int)				->int:


          """simple docstring"""


          logging.info("""Tuning chunk size...""")

          if min_chunk_size >= self.max_chunk_size:
              return min_chunk_size

          _lowerCamelCase     :		List[int] 						=						[2**l for l in range(int(math.log(self.max_chunk_size    ,  2)) + 1)]
          _lowerCamelCase     :		Dict 						=						[c for c in candidates if c > min_chunk_size]
          _lowerCamelCase     :		str 						=						[min_chunk_size] + candidates
          candidates[-1] += 4

          def test_chunk_size(_UpperCamelCase     :				int) -> bool:
              try:
                  with torch.no_grad():
                      fn(*_UpperCamelCase    ,  chunk_size=_UpperCamelCase)
                  return True
              except RuntimeError:
                  return False

          _lowerCamelCase     :		int 						=						0
          _lowerCamelCase     :		Union[str, Any] 						=						len(_UpperCamelCase) - 1
          while i > min_viable_chunk_size_index:
              _lowerCamelCase     :		Optional[int] 						=						test_chunk_size(candidates[i])
              if not viable:
                  _lowerCamelCase     :		str 						=						(min_viable_chunk_size_index + i) // 2
              else:
                  _lowerCamelCase     :		Optional[int] 						=						i
                  _lowerCamelCase     :		List[Any] 						=						(i + len(_UpperCamelCase) - 1) // 2

          return candidates[min_viable_chunk_size_index]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Iterable    ,  _UpperCamelCase     :				Iterable)				->bool:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						True
          for aa, aa in zip(_UpperCamelCase    ,  _UpperCamelCase):
              assert type(_UpperCamelCase) == type(_UpperCamelCase)
              if isinstance(_UpperCamelCase    ,  (list, tuple)):
                  consistent &= self._compare_arg_caches(_UpperCamelCase    ,  _UpperCamelCase)
              elif isinstance(_UpperCamelCase    ,  _UpperCamelCase):
                  _lowerCamelCase     :		Dict 						=						[v for _, v in sorted(aa.items()    ,  key=lambda _UpperCamelCase: x[0])]
                  _lowerCamelCase     :		Any 						=						[v for _, v in sorted(aa.items()    ,  key=lambda _UpperCamelCase: x[0])]
                  consistent &= self._compare_arg_caches(_UpperCamelCase    ,  _UpperCamelCase)
              else:
                  consistent &= aa == aa

          return consistent




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				Callable    ,  _UpperCamelCase     :				tuple    ,  _UpperCamelCase     :				int    ,  )				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						True
          _lowerCamelCase     :		tuple 						=						tree_map(lambda _UpperCamelCase: a.shape if isinstance(_UpperCamelCase    ,  torch.Tensor) else a    ,  _UpperCamelCase    ,  _UpperCamelCase)
          if self.cached_arg_data is not None:
              # If args have changed shape/value, we need to re-tune
              assert len(self.cached_arg_data) == len(_UpperCamelCase)
              _lowerCamelCase     :		Optional[Any] 						=						self._compare_arg_caches(self.cached_arg_data    ,  _UpperCamelCase)
          else:
              # Otherwise, we can reuse the precomputed value
              _lowerCamelCase     :		List[str] 						=						False

          if not consistent:
              _lowerCamelCase     :		Optional[Any] 						=						self._determine_favorable_chunk_size(
                  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  )
              _lowerCamelCase     :		str 						=						arg_data

          assert self.cached_chunk_size is not None

          return self.cached_chunk_size





import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCAmelCase			:							Tuple     =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A=False   , ):







    '''simple docstring'''
    output_path.parent.mkdir(parents=__A   , exist_ok=__A	)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , use_external_data_format=__A   , enable_onnx_checker=__A   , opset_version=__A   , )
    else:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , opset_version=__A   , )







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A = False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _lowerCamelCase     :		str 						=						"""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
    else:
        _lowerCamelCase     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						vae_decoder.decode
    onnx_export(
        __A   , model_args=(
            torch.randn(1   , __A   , 25   , 25	).to(device=__A   , dtype=__A	),
            False,
        )   , output_path=output_path / """vae_decoder""" / """model.onnx"""   , ordered_input_names=["""latent_sample""", """return_dict"""]   , output_names=["""sample"""]   , dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }   , opset=__A   , )
    del vae_decoder


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[int]     =argparse.ArgumentParser()

    parser.add_argument(
        "--model_path",
        type=str,
        required=True,
        help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
    )

    parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
    parser.add_argument(
        "--opset",
        default=14,
        type=int,
        help="The version of the ONNX operator set to use.",
    )
    parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

    lowerCAmelCase			:							Optional[Any]     =parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print("SD: Done: ONNX")




