"""simple docstring"""



import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MaskFormerSwinBackbone
       from transformers.models.maskformer import MaskFormerSwinModel



class      _A	:




    """simple docstring"""




    def __init__(		self							:     str	,      A_							:     List[str]	,      A_							:     Dict=13	,      A_							:     Union[str, Any]=32	,      A_							:     int=2	,      A_							:     int=3	,      A_							:     Optional[Any]=16	,      A_							:     Optional[int]=[1, 2, 1]	,      A_							:     str=[2, 2, 4]	,      A_							:     Optional[Any]=2	,      A_							:     Any=2.0	,      A_							:     Optional[Any]=True	,      A_							:     Optional[Any]=0.0	,      A_							:     Union[str, Any]=0.0	,      A_							:     List[str]=0.1	,      A_							:     Optional[int]="gelu"	,      A_							:     Tuple=False	,      A_							:     Any=True	,      A_							:     Tuple=0.02	,      A_							:     Dict=1E-5	,      A_							:     str=True	,      A_							:     int=None	,      A_							:     Union[str, Any]=True	,      A_							:     int=10	,      A_							:     Optional[int]=8	,      A_							:     Union[str, Any]=["stage1", "stage2", "stage3"]	,      A_							:     List[Any]=[1, 2, 3]	,      )    ->       Optional[Any]:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							image_size
         __snake_case				=							patch_size
         __snake_case				=							num_channels
         __snake_case				=							embed_dim
         __snake_case				=							depths
         __snake_case				=							num_heads
         __snake_case				=							window_size
         __snake_case				=							mlp_ratio
         __snake_case				=							qkv_bias
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							drop_path_rate
         __snake_case				=							hidden_act
         __snake_case				=							use_absolute_embeddings
         __snake_case				=							patch_norm
         __snake_case				=							layer_norm_eps
         __snake_case				=							initializer_range
         __snake_case				=							is_training
         __snake_case				=							scope
         __snake_case				=							use_labels
         __snake_case				=							type_sequence_label_size
         __snake_case				=							encoder_stride
         __snake_case				=							out_features
         __snake_case				=							out_indices
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         __snake_case				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         __snake_case				=							None
         if self.use_labels:
              __snake_case				=							ids_tensor([self.batch_size]	,      self.type_sequence_label_size    )

         __snake_case				=							self.get_config()

         return config, pixel_values, labels
    def  lowercase					(		self							:     int    )    ->       int:
         return MaskFormerSwinConfig(
             image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      embed_dim=self.embed_dim	,      depths=self.depths	,      num_heads=self.num_heads	,      window_size=self.window_size	,      mlp_ratio=self.mlp_ratio	,      qkv_bias=self.qkv_bias	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      drop_path_rate=self.drop_path_rate	,      hidden_act=self.hidden_act	,      use_absolute_embeddings=self.use_absolute_embeddings	,      path_norm=self.patch_norm	,      layer_norm_eps=self.layer_norm_eps	,      initializer_range=self.initializer_range	,      encoder_stride=self.encoder_stride	,      out_features=self.out_features	,      out_indices=self.out_indices	,      )
    def  lowercase					(		self							:     Optional[int]	,      A_							:     Optional[Any]	,      A_							:     List[str]	,      A_							:     List[str]    )    ->       Union[str, Any]:
         __snake_case				=							MaskFormerSwinModel(config=A_    )
         model.to(A_    )
         model.eval()
         __snake_case				=							model(A_    )

         __snake_case				=							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
         __snake_case				=							int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, expected_seq_len, expected_dim)    )
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     str	,      A_							:     Optional[int]	,      A_							:     Tuple    )    ->       int:
         __snake_case				=							MaskFormerSwinBackbone(config=A_    )
         model.to(A_    )
         model.eval()
         __snake_case				=							model(A_    )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps    )	,      len(config.out_features    )    )
         self.parent.assertListEqual(list(result.feature_maps[0].shape    )	,      [13, 16, 16, 16]    )

         # verify channels
         self.parent.assertEqual(len(model.channels    )	,      len(config.out_features    )    )
         self.parent.assertListEqual(model.channels	,      [16, 32, 64]    )

         # verify ValueError
         with self.parent.assertRaises(A_    ):
              __snake_case				=							['''stem''']
              __snake_case				=							MaskFormerSwinBackbone(config=A_    )







    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.prepare_config_and_inputs()
         __snake_case		,	__snake_case		,	__snake_case				=							config_and_inputs
         __snake_case				=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class      _A	(						_UpperCAmelCase							, _UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					(
        (
            MaskFormerSwinModel,
            MaskFormerSwinBackbone,
        )
        if is_torch_available()
        else ()
    )
    UpperCamelCase_	:					Union[str, Any]							=					{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
    UpperCamelCase_	:					Optional[Any]							=					False
    UpperCamelCase_	:					Union[str, Any]							=					False
    UpperCamelCase_	:					Optional[Any]							=					False
    UpperCamelCase_	:					Dict							=					False
    UpperCamelCase_	:					List[Any]							=					False
    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							MaskFormerSwinModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      embed_dim=37    )
    @require_torch_multi_gpu
    @unittest.skip(
        reason=(
            '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
            ''' `nn.DataParallel`'''
        )    )
    def  lowercase					(		self							:     Any    )    ->       Any:
         pass
    def  lowercase					(		self							:     str    )    ->       Tuple:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()
    def  lowercase					(		self							:     int    )    ->       Dict:
         return
    def  lowercase					(		self							:     Tuple    )    ->       Any:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*A_    )
    @unittest.skip('''Swin does not use inputs_embeds'''    )
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         pass
    @unittest.skip('''Swin does not support feedforward chunking'''    )
    def  lowercase					(		self							:     str    )    ->       Any:
         pass
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(A_    )
              self.assertIsInstance(model.get_input_embeddings()	,      (nn.Module)    )
              __snake_case				=							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(A_	,      nn.Linear    )    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Union[str, Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(A_    )
              __snake_case				=							inspect.signature(model.forward    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __snake_case				=							[*signature.parameters.keys()]

              __snake_case				=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]	,      A_    )
    @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''    )
    def  lowercase					(		self							:     Tuple    )    ->       Any:
         pass
    @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''    )
    def  lowercase					(		self							:     Any    )    ->       List[Any]:
         pass
    def  lowercase					(		self							:     Dict	,      A_							:     List[Any]	,      A_							:     Tuple	,      A_							:     List[Any]	,      A_							:     Union[str, Any]    )    ->       int:
         __snake_case				=							model_class(A_    )
         model.to(A_    )
         model.eval()

         with torch.no_grad():
              __snake_case				=							model(**self._prepare_for_class(A_	,      A_    )    )

         __snake_case				=							outputs.hidden_states

         __snake_case				=							getattr(
             self.model_tester	,      '''expected_num_hidden_layers'''	,      len(self.model_tester.depths    ) + 1    )
         self.assertEqual(len(A_    )	,      A_    )

         # Swin has a different seq_length
         __snake_case				=							(
             config.patch_size
             if isinstance(config.patch_size	,      collections.abc.Iterable    )
             else (config.patch_size, config.patch_size)
         )

         __snake_case				=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]    )	,      [num_patches, self.model_tester.embed_dim]	,      )
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         __snake_case				=							(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size	,      collections.abc.Iterable    )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
              __snake_case				=							True
              self.check_hidden_states_output(A_	,      A_	,      A_	,      A_    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __snake_case				=							True

              self.check_hidden_states_output(A_	,      A_	,      A_	,      A_    )
    def  lowercase					(		self							:     Any    )    ->       Dict:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case				=							3

         __snake_case				=							(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size	,      collections.abc.Iterable    )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         __snake_case				=							(
             config.patch_size
             if isinstance(config.patch_size	,      collections.abc.Iterable    )
             else (config.patch_size, config.patch_size)
         )

         __snake_case				=							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         __snake_case				=							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
              __snake_case				=							True
              self.check_hidden_states_output(A_	,      A_	,      A_	,      (padded_height, padded_width)    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __snake_case				=							True
              self.check_hidden_states_output(A_	,      A_	,      A_	,      (padded_height, padded_width)    )
    @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         pass
    @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''    )
    def  lowercase					(		self							:     Tuple    )    ->       Optional[int]:
         pass
    @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''    )
    def  lowercase					(		self							:     List[str]    )    ->       Union[str, Any]:
         pass







    def  lowercase					(		self							:     Optional[Any]    )    ->       int:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(A_							:     List[str]    ):
              __snake_case				=							0
              return t

         def check_equivalence(A_							:     Union[str, Any]	,      A_							:     List[Any]	,      A_							:     Optional[int]	,      A_							:     List[str]={}    ):
              with torch.no_grad():
                   __snake_case				=							model(**A_	,      return_dict=A_	,      **A_    )
                   __snake_case				=							model(**A_	,      return_dict=A_	,      **A_    ).to_tuple()

                   def recursive_check(A_							:     List[Any]	,      A_							:     List[str]    ):
                        if isinstance(A_	,      (List, Tuple)    ):
                             for tuple_iterable_value, dict_iterable_value in zip(A_	,      A_    ):
                                  recursive_check(A_	,      A_    )
                        elif isinstance(A_	,      A_    ):
                             for tuple_iterable_value, dict_iterable_value in zip(
                                 tuple_object.values()	,      dict_object.values()    ):
                                  recursive_check(A_	,      A_    )
                        elif tuple_object is None:
                             return
                        else:
                             self.assertTrue(
                                 torch.allclose(
                                     set_nan_tensor_to_zero(A_    )	,      set_nan_tensor_to_zero(A_    )	,      atol=1E-5    )	,      msg=(
                                     '''Tuple and dict output are not equal. Difference:'''
                                     f" {torch.max(torch.abs(tuple_object - dict_object    )    )}. Tuple has `nan`:"
                                     f" {torch.isnan(A_    ).any()} and `inf`: {torch.isinf(A_    )}. Dict has"
                                     f" `nan`: {torch.isnan(A_    ).any()} and `inf`: {torch.isinf(A_    )}."
                                 )	,      )

                   recursive_check(A_	,      A_    )

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(A_    )
              model.to(A_    )
              model.eval()

              __snake_case				=							self._prepare_for_class(A_	,      A_    )
              __snake_case				=							self._prepare_for_class(A_	,      A_    )
              check_equivalence(A_	,      A_	,      A_    )

              __snake_case				=							self._prepare_for_class(A_	,      A_	,      return_labels=A_    )
              __snake_case				=							self._prepare_for_class(A_	,      A_	,      return_labels=A_    )
              check_equivalence(A_	,      A_	,      A_    )

              __snake_case				=							self._prepare_for_class(A_	,      A_    )
              __snake_case				=							self._prepare_for_class(A_	,      A_    )
              check_equivalence(A_	,      A_	,      A_	,      {'''output_hidden_states''': True}    )

              __snake_case				=							self._prepare_for_class(A_	,      A_	,      return_labels=A_    )
              __snake_case				=							self._prepare_for_class(A_	,      A_	,      return_labels=A_    )
              check_equivalence(A_	,      A_	,      A_	,      {'''output_hidden_states''': True}    )






@require_torch
class      _A	(						unittest.TestCase							, _UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					(MaskFormerSwinBackbone,) if is_torch_available() else ()
    UpperCamelCase_	:					int							=					MaskFormerSwinConfig
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __snake_case				=							MaskFormerSwinModelTester(self    )







    def  lowercase					(		self							:     Optional[int]    )    ->       List[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case				=							inputs_dict['''pixel_values'''].shape[0]

         for backbone_class in self.all_model_classes:
              __snake_case				=							backbone_class(A_    )
              backbone.to(A_    )
              backbone.eval()

              __snake_case				=							backbone(**A_    )

              # Test default outputs and verify feature maps
              self.assertIsInstance(outputs.feature_maps	,      A_    )
              self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
              for feature_map, n_channels in zip(outputs.feature_maps	,      backbone.channels    ):
                   self.assertTrue(feature_map.shape[:2]	,      (batch_size, n_channels)    )
              self.assertIsNone(outputs.hidden_states    )
              self.assertIsNone(outputs.attentions    )

              # Test output_hidden_states=True
              __snake_case				=							backbone(**A_	,      output_hidden_states=A_    )
              self.assertIsNotNone(outputs.hidden_states    )
              self.assertTrue(len(outputs.hidden_states    )	,      len(backbone.stage_names    )    )
              # We skip the stem layer
              for hidden_states, n_channels in zip(outputs.hidden_states[1:]	,      backbone.channels    ):
                   for hidden_state in hidden_states:
                        # Hidden states are in the format (batch_size, (height * width), n_channels)
                        __snake_case		,	__snake_case		,	__snake_case				=							hidden_state.shape
                        self.assertTrue((h_batch_size, h_n_channels)	,      (batch_size, n_channels)    )

            # Test output_attentions=True
              if self.has_attentions:
                   __snake_case				=							backbone(**A_	,      output_attentions=A_    )
                   self.assertIsNotNone(outputs.attentions    )
"""simple docstring"""



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowercase		:		int														=							version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case=False,      ):
     output_path.parent.mkdir(parents=snake_case,      exist_ok=snake_case)
     # 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(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      use_external_data_format=snake_case,      enable_onnx_checker=snake_case,      opset_version=snake_case,      )
     else:
          export(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      opset_version=snake_case,      )





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case = False):
     __snake_case				=							torch.floataa if fpaa else torch.floataa
     if fpaa and torch.cuda.is_available():
          __snake_case				=							'''cuda'''
     elif fpaa and not torch.cuda.is_available():
          raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
     else:
          __snake_case				=							'''cpu'''
     __snake_case				=							Path(snake_case)

     # VAE DECODER
     __snake_case				=							AutoencoderKL.from_pretrained(model_path + '''/vae''')
     __snake_case				=							vae_decoder.config.latent_channels
     # forward only through the decoder part
     __snake_case				=							vae_decoder.decode
     onnx_export(
         snake_case,      model_args=(
             torch.randn(1,      snake_case,      25,      25).to(device=snake_case,      dtype=snake_case),
             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=snake_case,      )
     del vae_decoder


if __name__ == "__main__":
       __lowercase		:		Union[str, Any]														=							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")

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