




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
 from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    self.config_class(**self.inputs_dict					)
          self.parent.assertTrue(hasattr(snake_case			,							'''hidden_sizes'''					)					)
          self.parent.assertTrue(hasattr(snake_case			,							'''num_attention_heads'''					)					)
          self.parent.assertTrue(hasattr(snake_case			,							'''num_encoder_blocks'''					)					)







class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Union[str, Any]			,							snake_case       :			Any			,							snake_case       :			str=13			,							snake_case       :			Tuple=64			,							snake_case       :			Optional[int]=3			,							snake_case       :			int=4			,							snake_case       :			List[Any]=[2, 2, 2, 2]			,							snake_case       :			Optional[int]=[8, 4, 2, 1]			,							snake_case       :			Optional[int]=[16, 32, 64, 128]			,							snake_case       :			Optional[Any]=[1, 4, 8, 16]			,							snake_case       :			Dict=[1, 2, 4, 8]			,							snake_case       :			Any=True			,							snake_case       :			List[Any]=True			,							snake_case       :			Any="gelu"			,							snake_case       :			int=0.1			,							snake_case       :			str=0.1			,							snake_case       :			Union[str, Any]=0.02			,							snake_case       :			Optional[Any]=16			,							snake_case       :			Union[str, Any]=3			,							snake_case       :			str=None			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    image_size
          __UpperCamelCase			=    num_channels
          __UpperCamelCase			=    num_encoder_blocks
          __UpperCamelCase			=    sr_ratios
          __UpperCamelCase			=    depths
          __UpperCamelCase			=    hidden_sizes
          __UpperCamelCase			=    downsampling_rates
          __UpperCamelCase			=    num_attention_heads
          __UpperCamelCase			=    is_training
          __UpperCamelCase			=    use_labels
          __UpperCamelCase			=    hidden_act
          __UpperCamelCase			=    hidden_dropout_prob
          __UpperCamelCase			=    attention_probs_dropout_prob
          __UpperCamelCase			=    initializer_range
          __UpperCamelCase			=    decoder_hidden_size
          __UpperCamelCase			=    num_labels
          __UpperCamelCase			=    scope







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

          __UpperCamelCase			=    None
          if self.use_labels:
              __UpperCamelCase			=    ids_tensor([self.batch_size, self.image_size, self.image_size]			,							self.num_labels					)

          __UpperCamelCase			=    self.get_config()
          return config, pixel_values, labels







      def    snake_case						(  self       :			List[str]					):
          return GLPNConfig(
              image_size=self.image_size			,							num_channels=self.num_channels			,							num_encoder_blocks=self.num_encoder_blocks			,							depths=self.depths			,							hidden_sizes=self.hidden_sizes			,							num_attention_heads=self.num_attention_heads			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							initializer_range=self.initializer_range			,							decoder_hidden_size=self.decoder_hidden_size			,							)







      def    snake_case						(  self       :			Tuple			,							snake_case       :			List[Any]			,							snake_case       :			Dict			,							snake_case       :			Dict					):
          __UpperCamelCase			=    GLPNModel(config=snake_case					)
          model.to(snake_case					)
          model.eval()
          __UpperCamelCase			=    model(snake_case					)
          __UpperCamelCase			=    __UpperCamelCase			=    self.image_size // (self.downsampling_rates[-1] * 2)
          self.parent.assertEqual(
              result.last_hidden_state.shape			,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)					)







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			List[str]			,							snake_case       :			Union[str, Any]			,							snake_case       :			Dict					):
          __UpperCamelCase			=    self.num_labels
          __UpperCamelCase			=    GLPNForDepthEstimation(snake_case					)
          model.to(snake_case					)
          model.eval()
          __UpperCamelCase			=    model(snake_case					)
          self.parent.assertEqual(result.predicted_depth.shape			,							(self.batch_size, self.image_size, self.image_size)					)
          __UpperCamelCase			=    model(snake_case			,							labels=snake_case					)
          self.parent.assertEqual(result.predicted_depth.shape			,							(self.batch_size, self.image_size, self.image_size)					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    self.prepare_config_and_inputs()
          __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    config_and_inputs
          __UpperCamelCase			=    {'''pixel_values''': pixel_values}
          return config, inputs_dict







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

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
      lowerCAmelCase__      :			int     =       (
          {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
      )

      lowerCAmelCase__      :			Dict     =       False
      lowerCAmelCase__      :			List[str]     =       False
      lowerCAmelCase__      :			Optional[Any]     =       False







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    GLPNModelTester(self					)
          __UpperCamelCase			=    GLPNConfigTester(self			,							config_class=snake_case					)







      def    snake_case						(  self       :			List[str]					):
          self.config_tester.run_common_tests()







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case					)







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*snake_case					)







      @unittest.skip('''GLPN does not use inputs_embeds'''					)
      def    snake_case						(  self       :			Any					):
          pass







      @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''					)
      def    snake_case						(  self       :			List[str]					):
          pass







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase			=    ['''pixel_values''']
              self.assertListEqual(arg_names[:1]			,							snake_case					)







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCamelCase			=    True

          for model_class in self.all_model_classes:
              __UpperCamelCase			=    True
              __UpperCamelCase			=    False
              __UpperCamelCase			=    True
              __UpperCamelCase			=    model_class(snake_case					)
              model.to(snake_case					)
              model.eval()
              with torch.no_grad():
                  __UpperCamelCase			=    model(**self._prepare_for_class(snake_case			,							snake_case					)					)
              __UpperCamelCase			=    outputs.attentions

              __UpperCamelCase			=    sum(self.model_tester.depths					)
              self.assertEqual(len(snake_case					)			,							snake_case					)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              __UpperCamelCase			=    True
              __UpperCamelCase			=    model_class(snake_case					)
              model.to(snake_case					)
              model.eval()
              with torch.no_grad():
                  __UpperCamelCase			=    model(**self._prepare_for_class(snake_case			,							snake_case					)					)
              __UpperCamelCase			=    outputs.attentions

              self.assertEqual(len(snake_case					)			,							snake_case					)

              # verify the first attentions (first block, first layer)
              __UpperCamelCase			=    (self.model_tester.image_size // 4) ** 2
              __UpperCamelCase			=    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
              self.assertListEqual(
                  list(attentions[0].shape[-3:]					)			,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,							)

              # verify the last attentions (last block, last layer)
              __UpperCamelCase			=    (self.model_tester.image_size // 32) ** 2
              __UpperCamelCase			=    (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
              self.assertListEqual(
                  list(attentions[-1].shape[-3:]					)			,							[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,							)
              __UpperCamelCase			=    len(snake_case					)

              # Check attention is always last and order is fine
              __UpperCamelCase			=    True
              __UpperCamelCase			=    True
              __UpperCamelCase			=    model_class(snake_case					)
              model.to(snake_case					)
              model.eval()
              with torch.no_grad():
                  __UpperCamelCase			=    model(**self._prepare_for_class(snake_case			,							snake_case					)					)

              self.assertEqual(out_len + 1			,							len(snake_case					)					)

              __UpperCamelCase			=    outputs.attentions

              self.assertEqual(len(snake_case					)			,							snake_case					)
              # verify the first attentions (first block, first layer)
              __UpperCamelCase			=    (self.model_tester.image_size // 4) ** 2
              __UpperCamelCase			=    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]					)			,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,							)







      def    snake_case						(  self       :			List[Any]					):
          def check_hidden_states_output(snake_case       :			Tuple			,							snake_case       :			List[str]			,							snake_case       :			Optional[Any]					):
              __UpperCamelCase			=    model_class(snake_case					)
              model.to(snake_case					)
              model.eval()

              with torch.no_grad():
                  __UpperCamelCase			=    model(**self._prepare_for_class(snake_case			,							snake_case					)					)

              __UpperCamelCase			=    outputs.hidden_states

              __UpperCamelCase			=    self.model_tester.num_encoder_blocks
              self.assertEqual(len(snake_case					)			,							snake_case					)

              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-3:]					)			,							[
                      self.model_tester.hidden_sizes[0],
                      self.model_tester.image_size // 4,
                      self.model_tester.image_size // 4,
                  ]			,							)

          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase			=    True
              check_hidden_states_output(snake_case			,							snake_case			,							snake_case					)

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

              check_hidden_states_output(snake_case			,							snake_case			,							snake_case					)







      def    snake_case						(  self       :			str					):
          if not self.model_tester.is_training:
              return

          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCamelCase			=    True

          for model_class in self.all_model_classes:
              if model_class in get_values(snake_case					):
                  continue
              # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
              # this can then be incorporated into _prepare_for_class in test_modeling_common.py
              if model_class.__name__ == "GLPNForDepthEstimation":
                  __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    inputs_dict['''pixel_values'''].shape
                  __UpperCamelCase			=    torch.zeros(
                      [self.model_tester.batch_size, height, width]			,							device=snake_case					).long()
              __UpperCamelCase			=    model_class(snake_case					)
              model.to(snake_case					)
              model.train()
              __UpperCamelCase			=    self._prepare_for_class(snake_case			,							snake_case			,							return_labels=snake_case					)
              __UpperCamelCase			=    model(**snake_case					).loss
              loss.backward()







      @slow
      def    snake_case						(  self       :			List[Any]					):
          for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase			=    GLPNModel.from_pretrained(snake_case					)
              self.assertIsNotNone(snake_case					)







def        __SCREAMING_SNAKE_CASE (	)							->						int:

    """simple docstring"""


    __UpperCamelCase			=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
    return image


@require_torch
@require_vision
@slow
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @slow
      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
          __UpperCamelCase			=    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(snake_case					)

          __UpperCamelCase			=    prepare_img()
          __UpperCamelCase			=    image_processor(images=snake_case			,							return_tensors='''pt'''					).to(snake_case					)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)

          # verify the predicted depth
          __UpperCamelCase			=    torch.Size([1, 480, 640]					)
          self.assertEqual(outputs.predicted_depth.shape			,							snake_case					)

          __UpperCamelCase			=    torch.tensor(
              [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					).to(snake_case					)

          self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]			,							snake_case			,							atol=1E-4					)					)








import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

		A				=checkpoint[list(checkpoint.keys()		)[-4]].data
		A				=checkpoint[list(checkpoint.keys()		)[-3]].data
		A				=checkpoint[list(checkpoint.keys()		)[-2]].data
		A				=checkpoint[list(checkpoint.keys()		)[-1]].data

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

				if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
						if has_attentions:
								set_attention_weights(block.attentions[j]  ,				a_  ,				a_		)
								module_index += 1

				if hasattr(a_  ,				"downsamplers"		) and block.downsamplers is not None:
						set_resnet_weights(block.resnet_down  ,				a_  ,				a_		)
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
						module_index += 1

		set_resnet_weights(new_model_architecture.mid_block.resnets[0]  ,				a_  ,				a_		)
		module_index += 1
		set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,				a_  ,				a_		)
		module_index += 1
		set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,				a_  ,				a_		)
		module_index += 1

		for i, block in enumerate(new_model_architecture.up_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
				if has_attentions:
						set_attention_weights(
						    block.attentions[0]  ,				a_  ,				a_		)  # why can there only be a single attention layer for up?
						module_index += 1

				if hasattr(a_  ,				"resnet_up"		) and block.resnet_up is not None:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
	    type=str,
	    required=False,
	    help="""Path to the checkpoint to convert.""",
	)

	parser.add_argument(
	    """--config_file""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
	    type=str,
	    required=False,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument(
	    """--dump_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
	    type=str,
	    required=False,
	    help="""Path to the output model.""",
	)

	__a							  =						parser.parse_args()

	__a							  =						torch.load(args.checkpoint_path, map_location="""cpu""")

	with open(args.config_file) as f:
		__a							  =						json.loads(f.read())

	__a							  =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	__a							  =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__a							  =						ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		__a							  =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)




