


'''simple docstring'''






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       Dict							,   A__ :       int							,   A__ :       str="attention" ):
		'''simple docstring'''





		lowerCAmelCase_      :				Any      =       params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
		lowerCAmelCase_      :				Dict      =       params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
		lowerCAmelCase_      :				Any      =       params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
		lowerCAmelCase_      :				Optional[Any]      =       params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
		return k, o, q, v







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       List[str]							,   A__ :       Tuple							,   A__ :       List[Any]=False ):
		'''simple docstring'''





		if split_mlp_wi:
				lowerCAmelCase_      :				Optional[Any]      =       params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
				lowerCAmelCase_      :				List[Any]      =       params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
				lowerCAmelCase_      :				Union[str, Any]      =       (wi_a, wi_a)
		else:
				lowerCAmelCase_      :				List[Any]      =       params[f'{prefix}/layers_{i}/mlp/wi/kernel']

		lowerCAmelCase_      :				Dict      =       params[f'{prefix}/layers_{i}/mlp/wo/kernel']
		return wi, wo







def     UpperCamelCase_      (   A__ :       str							,   A__ :       List[Any]							,   A__ :       int							,   A__ :       Dict ):
		'''simple docstring'''





		return params[f'{prefix}/layers_{i}/{layer_name}/scale']







def     UpperCamelCase_      (   A__ :       dict							,   *, A__ :       int							,   A__ :       bool ):
		'''simple docstring'''





		lowerCAmelCase_      :				str      =       traverse_util.flatten_dict(variables["""target"""] )
		lowerCAmelCase_      :				List[Any]      =       {"""/""".join(A__ ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		lowerCAmelCase_      :				Dict      =       """encoder/layers_0/mlp/wi_0/kernel""" in old
		print("""Split MLP:"""							,   A__ )

		lowerCAmelCase_      :				List[Any]      =       collections.OrderedDict()

		# Shared embeddings.
		lowerCAmelCase_      :				int      =       old["""token_embedder/embedding"""]

		# Encoder.
		for i in range(A__ ):
				# Block i, layer 0 (Self Attention).
				lowerCAmelCase_      :				List[str]      =       tax_layer_norm_lookup(A__							,   A__							,   """encoder"""							,   """pre_attention_layer_norm""" )
				lowerCAmelCase_      :				Union[str, Any]      =       tax_attention_lookup(A__							,   A__							,   """encoder"""							,   """attention""" )
				lowerCAmelCase_      :				int      =       layer_norm
				lowerCAmelCase_      :				int      =       k.T
				lowerCAmelCase_      :				str      =       o.T
				lowerCAmelCase_      :				Dict      =       q.T
				lowerCAmelCase_      :				Optional[Any]      =       v.T

				# Block i, layer 1 (MLP).
				lowerCAmelCase_      :				Optional[Any]      =       tax_layer_norm_lookup(A__							,   A__							,   """encoder"""							,   """pre_mlp_layer_norm""" )
				lowerCAmelCase_      :				Dict      =       tax_mlp_lookup(A__							,   A__							,   """encoder"""							,   A__ )
				lowerCAmelCase_      :				Dict      =       layer_norm
				if split_mlp_wi:
						lowerCAmelCase_      :				Dict      =       wi[0].T
						lowerCAmelCase_      :				Tuple      =       wi[1].T
				else:
						lowerCAmelCase_      :				int      =       wi.T
				lowerCAmelCase_      :				Union[str, Any]      =       wo.T

		lowerCAmelCase_      :				Optional[int]      =       old[
		    """encoder/relpos_bias/rel_embedding"""
		].T
		lowerCAmelCase_      :				Optional[int]      =       old["""encoder/encoder_norm/scale"""]

		if not is_encoder_only:
				# Decoder.
				for i in range(A__ ):
						# Block i, layer 0 (Self Attention).
						lowerCAmelCase_      :				Any      =       tax_layer_norm_lookup(A__							,   A__							,   """decoder"""							,   """pre_self_attention_layer_norm""" )
						lowerCAmelCase_      :				Tuple      =       tax_attention_lookup(A__							,   A__							,   """decoder"""							,   """self_attention""" )
						lowerCAmelCase_      :				Optional[Any]      =       layer_norm
						lowerCAmelCase_      :				Optional[Any]      =       k.T
						lowerCAmelCase_      :				Optional[int]      =       o.T
						lowerCAmelCase_      :				Optional[Any]      =       q.T
						lowerCAmelCase_      :				Union[str, Any]      =       v.T

						# Block i, layer 1 (Cross Attention).
						lowerCAmelCase_      :				str      =       tax_layer_norm_lookup(A__							,   A__							,   """decoder"""							,   """pre_cross_attention_layer_norm""" )
						lowerCAmelCase_      :				Optional[Any]      =       tax_attention_lookup(A__							,   A__							,   """decoder"""							,   """encoder_decoder_attention""" )
						lowerCAmelCase_      :				Union[str, Any]      =       layer_norm
						lowerCAmelCase_      :				Tuple      =       k.T
						lowerCAmelCase_      :				List[str]      =       o.T
						lowerCAmelCase_      :				str      =       q.T
						lowerCAmelCase_      :				List[str]      =       v.T

						# Block i, layer 2 (MLP).
						lowerCAmelCase_      :				Optional[int]      =       tax_layer_norm_lookup(A__							,   A__							,   """decoder"""							,   """pre_mlp_layer_norm""" )
						lowerCAmelCase_      :				Dict      =       tax_mlp_lookup(A__							,   A__							,   """decoder"""							,   A__ )
						lowerCAmelCase_      :				str      =       layer_norm
						if split_mlp_wi:
								lowerCAmelCase_      :				Any      =       wi[0].T
								lowerCAmelCase_      :				int      =       wi[1].T
						else:
								lowerCAmelCase_      :				Dict      =       wi.T
						lowerCAmelCase_      :				Dict      =       wo.T

				lowerCAmelCase_      :				str      =       old["""decoder/decoder_norm/scale"""]
				lowerCAmelCase_      :				Union[str, Any]      =       old[
				    """decoder/relpos_bias/rel_embedding"""
				].T

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						lowerCAmelCase_      :				Optional[Any]      =       old["""decoder/logits_dense/kernel"""].T

		return new







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       bool ):
		'''simple docstring'''





		lowerCAmelCase_      :				int      =       collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				lowerCAmelCase_      :				Dict      =       state_dict["""shared.weight"""]

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						lowerCAmelCase_      :				Tuple      =       state_dict["""shared.weight"""]

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print("""Using shared word embeddings as lm_head.""" )
						lowerCAmelCase_      :				List[str]      =       state_dict["""shared.weight"""]

		return state_dict







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Union[str, Any]							,   A__ :       Optional[int]							,   A__ :       str ):
		'''simple docstring'''





		lowerCAmelCase_      :				Optional[Any]      =       checkpoints.load_tax_checkpoint(A__ )
		lowerCAmelCase_      :				Any      =       convert_tax_to_pytorch(A__							,   num_layers=config.num_layers							,   is_encoder_only=A__ )
		lowerCAmelCase_      :				Dict      =       make_state_dict(A__							,   A__ )
		model.load_state_dict(A__							,   strict=A__ )







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Any							,   A__ :       List[str]							,   A__ :       bool = False ):
		'''simple docstring'''





		lowerCAmelCase_      :				Dict      =       TaConfig.from_json_file(A__ )
		print(f'Building PyTorch model from configuration: {config}' )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				lowerCAmelCase_      :				int      =       TaEncoderModel(A__ )
		else:
				lowerCAmelCase_      :				str      =       TaForConditionalGeneration(A__ )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(A__							,   A__							,   A__							,   A__ )

		# Save pytorch-model
		print(f'Save PyTorch model to {pytorch_dump_path}' )
		model.save_pretrained(A__ )

		# Verify that we can load the checkpoint.
		model.from_pretrained(A__ )
		print("""Done""" )


if __name__ == "__main__":
		__A :  List[str]    							=				argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
		# Required parameters
		parser.add_argument(
		    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
		)
		__A :  List[Any]    							=				parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)







'''simple docstring'''






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Union[str, Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  List[Any]=2   ,						lowerCamelCase   :  int=True   ,						lowerCamelCase   :  str=False   ,						lowerCamelCase   :  List[str]=10   ,						lowerCamelCase   :  Dict=3   ,						lowerCamelCase   :  str=32 * 4   ,						lowerCamelCase   :  Tuple=32 * 6   ,						lowerCamelCase   :  int=4   ,						lowerCamelCase   :  Optional[int]=32   ,						)       ->  List[Any]:
      lowerCAmelCase_      :				Tuple      =       parent
      lowerCAmelCase_      :				int      =       batch_size
      lowerCAmelCase_      :				Tuple      =       is_training
      lowerCAmelCase_      :				str      =       use_auxiliary_loss
      lowerCAmelCase_      :				Optional[Any]      =       num_queries
      lowerCAmelCase_      :				List[str]      =       num_channels
      lowerCAmelCase_      :				Optional[Any]      =       min_size
      lowerCAmelCase_      :				Dict      =       max_size
      lowerCAmelCase_      :				List[Any]      =       num_labels
      lowerCAmelCase_      :				List[Any]      =       mask_feature_size

    def __lowercase       (       self   :  str		)       ->  List[Any]:
      lowerCAmelCase_      :				Union[str, Any]      =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
          lowerCamelCase		)

      lowerCAmelCase_      :				int      =       torch.ones([self.batch_size, self.min_size, self.max_size]   ,						device=lowerCamelCase		)

      lowerCAmelCase_      :				List[Any]      =       (
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,						device=lowerCamelCase		) > 0.5
      ).float()
      lowerCAmelCase_      :				Union[str, Any]      =       (torch.rand((self.batch_size, self.num_labels)   ,						device=lowerCamelCase		) > 0.5).long()

      lowerCAmelCase_      :				List[Any]      =       self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      return MaskFormerConfig.from_backbone_and_decoder_configs(
          backbone_config=SwinConfig(
              depths=[1, 1, 1, 1]   ,						)   ,						decoder_config=DetrConfig(
              decoder_ffn_dim=1_28   ,						num_queries=self.num_queries   ,						decoder_attention_heads=2   ,						d_model=self.mask_feature_size   ,						)   ,						mask_feature_size=self.mask_feature_size   ,						fpn_feature_size=self.mask_feature_size   ,						num_channels=self.num_channels   ,						num_labels=self.num_labels   ,						)

    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       self.prepare_config_and_inputs()
      lowerCAmelCase_      :				Union[str, Any]      =       {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Any      =       output.encoder_hidden_states
      lowerCAmelCase_      :				Dict      =       output.pixel_decoder_hidden_states
      lowerCAmelCase_      :				str      =       output.transformer_decoder_hidden_states

      self.parent.assertTrue(len(lowerCamelCase		)   ,						len(config.backbone_config.depths		)		)
      self.parent.assertTrue(len(lowerCamelCase		)   ,						len(config.backbone_config.depths		)		)
      self.parent.assertTrue(len(lowerCamelCase		)   ,						config.decoder_config.decoder_layers		)

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Tuple=False		)       ->  List[Any]:
      with torch.no_grad():
        lowerCAmelCase_      :				int      =       MaskFormerModel(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()

        lowerCAmelCase_      :				List[Any]      =       model(pixel_values=lowerCamelCase   ,						pixel_mask=lowerCamelCase		)
        lowerCAmelCase_      :				Union[str, Any]      =       model(lowerCamelCase   ,						output_hidden_states=lowerCamelCase		)
      # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
      # encoder and pixel decoder
      self.parent.assertEqual(
          output.transformer_decoder_last_hidden_state.shape   ,						(self.batch_size, self.num_queries, self.mask_feature_size)   ,						)
      # let's ensure the other two hidden state exists
      self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
      self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

      if output_hidden_states:
        self.check_output_hidden_state(lowerCamelCase   ,						lowerCamelCase		)





    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[Any]		)       ->  Dict:
      lowerCAmelCase_      :				Tuple      =       MaskFormerForInstanceSegmentation(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()

      def comm_check_on_output(lowerCamelCase   :  Optional[Any]		):
        # let's still check that all the required stuff is there
        self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None		)
        self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None		)
        self.parent.assertTrue(result.encoder_last_hidden_state is not None		)
        # okay, now we need to check the logits shape
        # due to the encoder compression, masks have a //4 spatial size
        self.parent.assertEqual(
            result.masks_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)   ,						)
        # + 1 for null class
        self.parent.assertEqual(
            result.class_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.num_labels + 1)		)

      with torch.no_grad():
        lowerCAmelCase_      :				Dict      =       model(pixel_values=lowerCamelCase   ,						pixel_mask=lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       model(lowerCamelCase		)

        comm_check_on_output(lowerCamelCase		)

        lowerCAmelCase_      :				Union[str, Any]      =       model(
            pixel_values=lowerCamelCase   ,						pixel_mask=lowerCamelCase   ,						mask_labels=lowerCamelCase   ,						class_labels=lowerCamelCase		)

      comm_check_on_output(lowerCamelCase		)

      self.parent.assertTrue(result.loss is not None		)
      self.parent.assertEqual(result.loss.shape   ,						torch.Size([1]		)		)







@require_torch
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    lowercase       			= (
        {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Tuple      =       MaskFormerModelTester(self		)
      lowerCAmelCase_      :				str      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      self.config_tester.run_common_tests()

    def __lowercase       (       self   :  Tuple		)       ->  List[Any]:
      lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(lowerCamelCase   ,						**lowerCamelCase   ,						output_hidden_states=lowerCamelCase		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  str:
      lowerCAmelCase_      :				Dict      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCamelCase		)

    @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""		)
    def __lowercase       (       self   :  Any		)       ->  Optional[Any]:
      pass

    @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""		)
    def __lowercase       (       self   :  Union[str, Any]		)       ->  Optional[Any]:
      pass

    @unittest.skip(reason="""MaskFormer is not a generative model"""		)
    def __lowercase       (       self   :  Any		)       ->  str:
      pass

    @unittest.skip(reason="""MaskFormer does not use token embeddings"""		)
    def __lowercase       (       self   :  List[Any]		)       ->  Any:
      pass

    @require_torch_multi_gpu
    @unittest.skip(
        reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""		)
    def __lowercase       (       self   :  List[str]		)       ->  Optional[int]:
      pass

    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def __lowercase       (       self   :  str		)       ->  Optional[int]:
      pass

    def __lowercase       (       self   :  Tuple		)       ->  List[str]:
      lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[Any]      =       model_class(lowerCamelCase		)
        lowerCAmelCase_      :				int      =       inspect.signature(model.forward		)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCAmelCase_      :				List[str]      =       [*signature.parameters.keys()]

        lowerCAmelCase_      :				int      =       ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,						lowerCamelCase		)

    @slow
    def __lowercase       (       self   :  Optional[int]		)       ->  List[str]:
      for model_name in ["facebook/maskformer-swin-small-coco"]:
        lowerCAmelCase_      :				List[str]      =       MaskFormerModel.from_pretrained(lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)

    def __lowercase       (       self   :  Dict		)       ->  int:
      lowerCAmelCase_      :				Any      =       (self.model_tester.min_size,) * 2
      lowerCAmelCase_      :				Dict      =       {
          """pixel_values""": torch.randn((2, 3, *size)   ,						device=lowerCamelCase		),
          """mask_labels""": torch.randn((2, 10, *size)   ,						device=lowerCamelCase		),
          """class_labels""": torch.zeros(2   ,						10   ,						device=lowerCamelCase		).long(),
      }

      lowerCAmelCase_      :				Dict      =       MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       model(**lowerCamelCase		)
      self.assertTrue(outputs.loss is not None		)

    def __lowercase       (       self   :  int		)       ->  int:
      lowerCAmelCase_,							lowerCAmelCase_      :				Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(lowerCamelCase   ,						**lowerCamelCase   ,						output_hidden_states=lowerCamelCase		)

    def __lowercase       (       self   :  Dict		)       ->  List[str]:
      lowerCAmelCase_,							lowerCAmelCase_      :				Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		).to(lowerCamelCase		)
        lowerCAmelCase_      :				Tuple      =       model(**lowerCamelCase   ,						output_attentions=lowerCamelCase		)
        self.assertTrue(outputs.attentions is not None		)

    def __lowercase       (       self   :  int		)       ->  Union[str, Any]:
      if not self.model_tester.is_training:
        return
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase_      :				Optional[int]      =       self.all_model_classes[1]
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				str      =       self.model_tester.prepare_config_and_inputs()

      lowerCAmelCase_      :				Union[str, Any]      =       model_class(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.train()

      lowerCAmelCase_      :				str      =       model(lowerCamelCase   ,						mask_labels=lowerCamelCase   ,						class_labels=lowerCamelCase		).loss
      loss.backward()





    def __lowercase       (       self   :  Any		)       ->  Union[str, Any]:
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase_      :				List[str]      =       self.all_model_classes[1]
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase_      :				List[str]      =       True
      lowerCAmelCase_      :				List[str]      =       True

      lowerCAmelCase_      :				Dict      =       model_class(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.train()

      lowerCAmelCase_      :				Union[str, Any]      =       model(lowerCamelCase   ,						mask_labels=lowerCamelCase   ,						class_labels=lowerCamelCase		)

      lowerCAmelCase_      :				Optional[Any]      =       outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      lowerCAmelCase_      :				List[str]      =       outputs.pixel_decoder_hidden_states[0]
      pixel_decoder_hidden_states.retain_grad()
      # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
      lowerCAmelCase_      :				Optional[int]      =       outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      lowerCAmelCase_      :				Tuple      =       outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=lowerCamelCase		)

      self.assertIsNotNone(encoder_hidden_states.grad		)
      self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
      self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
      self.assertIsNotNone(attentions.grad		)







__A :  List[Any]    							=				1E-4







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image






@require_vision
@slow
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @cached_property
    def __lowercase       (       self   :  Union[str, Any]		)       ->  List[Any]:
      return (
          MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
          if is_vision_available()
          else None
      )

    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Optional[int]      =       MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       self.default_image_processor
      lowerCAmelCase_      :				Optional[int]      =       prepare_img()
      lowerCAmelCase_      :				Dict      =       image_processor(lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
      # check size
      self.assertEqual(lowerCamelCase   ,						(1, 3, 8_00, 10_88)		)

      with torch.no_grad():
        lowerCAmelCase_      :				List[str]      =       model(**lowerCamelCase		)

      lowerCAmelCase_      :				Any      =       torch.tensor(
          [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]		).to(lowerCamelCase		)
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)

      lowerCAmelCase_      :				Union[str, Any]      =       torch.tensor(
          [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]		).to(lowerCamelCase		)
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)

      lowerCAmelCase_      :				Optional[Any]      =       torch.tensor(
          [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]		).to(lowerCamelCase		)
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      lowerCAmelCase_      :				Union[str, Any]      =       (
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
          .to(lowerCamelCase		)
          .eval()
      )
      lowerCAmelCase_      :				Union[str, Any]      =       self.default_image_processor
      lowerCAmelCase_      :				Optional[Any]      =       prepare_img()
      lowerCAmelCase_      :				Optional[int]      =       image_processor(lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Optional[int]      =       inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
      # check size
      self.assertEqual(lowerCamelCase   ,						(1, 3, 8_00, 10_88)		)

      with torch.no_grad():
        lowerCAmelCase_      :				List[str]      =       model(**lowerCamelCase		)
      # masks_queries_logits
      lowerCAmelCase_      :				Dict      =       outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   ,						)
      lowerCAmelCase_      :				Any      =       [
          [-1.3_737_124, -1.7_724_937, -1.9_364_233],
          [-1.5_977_281, -1.9_867_939, -2.1_523_695],
          [-1.5_795_398, -1.9_269_832, -2.093_942],
      ]
      lowerCAmelCase_      :				Optional[int]      =       torch.tensor(lowerCamelCase		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)
      # class_queries_logits
      lowerCAmelCase_      :				Optional[Any]      =       outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
      lowerCAmelCase_      :				Tuple      =       torch.tensor(
          [
              [1.6_512E00, -5.2_572E00, -3.3_519E00],
              [3.6_169E-02, -5.9_025E00, -2.9_313E00],
              [1.0_766E-04, -7.7_630E00, -5.1_263E00],
          ]		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  List[str]:
      lowerCAmelCase_      :				List[str]      =       (
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""		)
          .to(lowerCamelCase		)
          .eval()
      )
      lowerCAmelCase_      :				Tuple      =       self.default_image_processor
      lowerCAmelCase_      :				int      =       prepare_img()
      lowerCAmelCase_      :				Any      =       image_processor(lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)
      lowerCAmelCase_      :				List[str]      =       inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
      # check size
      self.assertEqual(lowerCamelCase   ,						(1, 3, 8_00, 10_88)		)

      with torch.no_grad():
        lowerCAmelCase_      :				Optional[Any]      =       model(**lowerCamelCase		)
      # masks_queries_logits
      lowerCAmelCase_      :				List[Any]      =       outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   ,						)
      lowerCAmelCase_      :				Optional[Any]      =       [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
      lowerCAmelCase_      :				Union[str, Any]      =       torch.tensor(lowerCamelCase		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)
      # class_queries_logits
      lowerCAmelCase_      :				Dict      =       outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
      lowerCAmelCase_      :				int      =       torch.tensor(
          [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,						lowerCamelCase   ,						atol=lowerCamelCase		)		)





    def __lowercase       (       self   :  Union[str, Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Dict      =       (
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
          .to(lowerCamelCase		)
          .eval()
      )
      lowerCAmelCase_      :				List[str]      =       self.default_image_processor

      lowerCAmelCase_      :				int      =       image_processor(
          [np.zeros((3, 8_00, 13_33)		), np.zeros((3, 8_00, 13_33)		)]   ,						segmentation_maps=[np.zeros((3_84, 3_84)		).astype(np.floataa		), np.zeros((3_84, 3_84)		).astype(np.floataa		)]   ,						return_tensors="""pt"""   ,						)

      lowerCAmelCase_      :				List[str]      =       inputs["""pixel_values"""].to(lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       [el.to(lowerCamelCase		) for el in inputs["""mask_labels"""]]
      lowerCAmelCase_      :				Union[str, Any]      =       [el.to(lowerCamelCase		) for el in inputs["""class_labels"""]]

      with torch.no_grad():
        lowerCAmelCase_      :				Any      =       model(**lowerCamelCase		)

      self.assertTrue(outputs.loss is not None		)



