


'''simple docstring'''
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 					_A						(						__lowerCamelCase							):



   '''simple docstring'''






   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	str:
          snake_case__					:	int          =					self.config_class(**self.inputs_dict						)
          self.parent.assertTrue(hasattr(lowerCamelCase					, """hidden_sizes"""						)						)
          self.parent.assertTrue(hasattr(lowerCamelCase					, """num_attention_heads"""						)						)
          self.parent.assertTrue(hasattr(lowerCamelCase					, """num_encoder_blocks"""						)						)







class 					_A						:



   '''simple docstring'''






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




   def   __lowerCAmelCase						(	self	:					List[str]						)->	Optional[Any]:
          snake_case__					:	Optional[int]          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

          snake_case__					:	Any          =					self.get_config()
          return config, pixel_values, labels




   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	Tuple:
          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   __lowerCAmelCase						(	self	:					int					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					str					, lowerCamelCase	:					Union[str, Any]						)->	Optional[Any]:
          snake_case__					:	Any          =					GLPNModel(config=lowerCamelCase						)
          model.to(lowerCamelCase						)
          model.eval()
          snake_case__					:	Optional[Any]          =					model(lowerCamelCase						)
          snake_case__					:	str          =					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   __lowerCAmelCase						(	self	:					int					, lowerCamelCase	:					int					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					Any						)->	Union[str, Any]:
          snake_case__					:	List[str]          =					self.num_labels
          snake_case__					:	List[Any]          =					GLPNForDepthEstimation(lowerCamelCase						)
          model.to(lowerCamelCase						)
          model.eval()
          snake_case__					:	List[Any]          =					model(lowerCamelCase						)
          self.parent.assertEqual(result.predicted_depth.shape					, (self.batch_size, self.image_size, self.image_size)						)
          snake_case__					:	Union[str, Any]          =					model(lowerCamelCase					, labels=lowerCamelCase						)
          self.parent.assertEqual(result.predicted_depth.shape					, (self.batch_size, self.image_size, self.image_size)						)




   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	str:
          snake_case__					:	Dict          =					self.prepare_config_and_inputs()
          snake_case__					:	Optional[Any]          =					config_and_inputs
          snake_case__					:	Optional[Any]          =					{"""pixel_values""": pixel_values}
          return config, inputs_dict







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



   '''simple docstring'''






   _lowercase    						= (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   _lowercase    						= (
       {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
   )

   _lowercase    						= False
   _lowercase    						= False
   _lowercase    						= False




   def   __lowerCAmelCase						(	self	:					Any						)->	Optional[int]:
          snake_case__					:	Optional[int]          =					GLPNModelTester(self						)
          snake_case__					:	Tuple          =					GLPNConfigTester(self					, config_class=lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					List[str]						)->	List[Any]:
          self.config_tester.run_common_tests()




   def   __lowerCAmelCase						(	self	:					List[str]						)->	Optional[int]:
          snake_case__					:	Dict          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Union[str, Any]:
          snake_case__					:	Tuple          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase						)




   @unittest.skip("""GLPN does not use inputs_embeds"""						)
   def   __lowerCAmelCase						(	self	:					List[Any]						)->	Union[str, Any]:
          pass




   @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""						)
   def   __lowerCAmelCase						(	self	:					List[Any]						)->	Tuple:
          pass




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Tuple:
          snake_case__					:	List[str]          =					self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case__					:	Any          =					["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	List[Any]:
          snake_case__					:	Tuple          =					self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__					:	Optional[int]          =					True

          for model_class in self.all_model_classes:
                 snake_case__					:	List[str]          =					True
                 snake_case__					:	List[Any]          =					False
                 snake_case__					:	Optional[Any]          =					True
                 snake_case__					:	List[Any]          =					model_class(lowerCamelCase						)
                 model.to(lowerCamelCase						)
                 model.eval()
                 with torch.no_grad():
                        snake_case__					:	List[str]          =					model(**self._prepare_for_class(lowerCamelCase					, lowerCamelCase						)						)
                 snake_case__					:	Any          =					outputs.attentions

                 snake_case__					:	Any          =					sum(self.model_tester.depths						)
                 self.assertEqual(len(lowerCamelCase						)					, lowerCamelCase						)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 snake_case__					:	List[Any]          =					True
                 snake_case__					:	Tuple          =					model_class(lowerCamelCase						)
                 model.to(lowerCamelCase						)
                 model.eval()
                 with torch.no_grad():
                        snake_case__					:	List[str]          =					model(**self._prepare_for_class(lowerCamelCase					, lowerCamelCase						)						)
                 snake_case__					:	int          =					outputs.attentions

                 self.assertEqual(len(lowerCamelCase						)					, lowerCamelCase						)

                 # verify the first attentions (first block, first layer)
                 snake_case__					:	List[Any]          =					(self.model_tester.image_size // 4) ** 2
                 snake_case__					:	Union[str, Any]          =					(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)
                 snake_case__					:	str          =					(self.model_tester.image_size // 32) ** 2
                 snake_case__					:	List[str]          =					(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]					, )
                 snake_case__					:	Dict          =					len(lowerCamelCase						)

                 # Check attention is always last and order is fine
                 snake_case__					:	Union[str, Any]          =					True
                 snake_case__					:	Optional[int]          =					True
                 snake_case__					:	List[str]          =					model_class(lowerCamelCase						)
                 model.to(lowerCamelCase						)
                 model.eval()
                 with torch.no_grad():
                        snake_case__					:	Union[str, Any]          =					model(**self._prepare_for_class(lowerCamelCase					, lowerCamelCase						)						)

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

                 snake_case__					:	Tuple          =					outputs.attentions

                 self.assertEqual(len(lowerCamelCase						)					, lowerCamelCase						)
                 # verify the first attentions (first block, first layer)
                 snake_case__					:	Optional[Any]          =					(self.model_tester.image_size // 4) ** 2
                 snake_case__					:	int          =					(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   __lowerCAmelCase						(	self	:					int						)->	Tuple:
          def check_hidden_states_output(lowerCamelCase	:					Dict					, lowerCamelCase	:					int					, lowerCamelCase	:					Optional[int]						):
                 snake_case__					:	Optional[Any]          =					model_class(lowerCamelCase						)
                 model.to(lowerCamelCase						)
                 model.eval()

                 with torch.no_grad():
                        snake_case__					:	Any          =					model(**self._prepare_for_class(lowerCamelCase					, lowerCamelCase						)						)

                 snake_case__					:	Optional[Any]          =					outputs.hidden_states

                 snake_case__					:	Optional[Any]          =					self.model_tester.num_encoder_blocks
                 self.assertEqual(len(lowerCamelCase						)					, lowerCamelCase						)

                 # 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,
                     ]					, )

          snake_case__					:	Tuple          =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case__					:	Tuple          =					True
                 check_hidden_states_output(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 snake_case__					:	Optional[int]          =					True

                 check_hidden_states_output(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					int						)->	Union[str, Any]:
          if not self.model_tester.is_training:
                 return

          snake_case__					:	str          =					self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__					:	str          =					True

          for model_class in self.all_model_classes:
                 if model_class in get_values(lowerCamelCase						):
                        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":
                        snake_case__					:	List[str]          =					inputs_dict["""pixel_values"""].shape
                        snake_case__					:	str          =					torch.zeros(
                            [self.model_tester.batch_size, height, width]					, device=lowerCamelCase						).long()
                 snake_case__					:	Dict          =					model_class(lowerCamelCase						)
                 model.to(lowerCamelCase						)
                 model.train()
                 snake_case__					:	List[str]          =					self._prepare_for_class(lowerCamelCase					, lowerCamelCase					, return_labels=lowerCamelCase						)
                 snake_case__					:	Tuple          =					model(**lowerCamelCase						).loss
                 loss.backward()




   @slow
   def   __lowerCAmelCase						(	self	:					Tuple						)->	Tuple:
          for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__					:	Any          =					GLPNModel.from_pretrained(lowerCamelCase						)
                 self.assertIsNotNone(lowerCamelCase						)







def 				lowerCAmelCase__	(       ):






       """simple docstring"""





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







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



   '''simple docstring'''






   @slow
   def   __lowerCAmelCase						(	self	:					List[str]						)->	List[str]:
          snake_case__					:	Tuple          =					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
          snake_case__					:	str          =					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(lowerCamelCase						)

          snake_case__					:	int          =					prepare_img()
          snake_case__					:	str          =					image_processor(images=lowerCamelCase					, return_tensors="""pt"""						).to(lowerCamelCase						)

          # forward pass
          with torch.no_grad():
                 snake_case__					:	Union[str, Any]          =					model(**lowerCamelCase						)

          # verify the predicted depth
          snake_case__					:	Optional[int]          =					torch.Size([1, 480, 640]						)
          self.assertEqual(outputs.predicted_depth.shape					, lowerCamelCase						)

          snake_case__					:	Dict          =					torch.tensor(
              [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]						).to(lowerCamelCase						)

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











import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  if "model" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""model."""   ,   """""")
  if "norm1" in orig_key:
    __snake_case:		Tuple					    =		orig_key.replace("""norm1"""   ,   """attention.output.LayerNorm""")
  if "norm2" in orig_key:
    __snake_case:		Any					    =		orig_key.replace("""norm2"""   ,   """output.LayerNorm""")
  if "norm" in orig_key:
    __snake_case:		List[str]					    =		orig_key.replace("""norm"""   ,   """LayerNorm""")
  if "transformer" in orig_key:
    __snake_case:		str					    =		orig_key.split(""".""")[0].split("""_""")[-1]
    __snake_case:		int					    =		orig_key.replace(F'''transformer_{layer_num}'''   ,   F'''encoder.layer.{layer_num}''')
  if "mha.attn" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""mha.attn"""   ,   """attention.self""")
  if "mha" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mha"""   ,   """attention""")
  if "W_q" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""W_q"""   ,   """self.query""")
  if "W_k" in orig_key:
    __snake_case:		str					    =		orig_key.replace("""W_k"""   ,   """self.key""")
  if "W_v" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""W_v"""   ,   """self.value""")
  if "ff1" in orig_key:
    __snake_case:		int					    =		orig_key.replace("""ff1"""   ,   """intermediate.dense""")
  if "ff2" in orig_key:
    __snake_case:		Dict					    =		orig_key.replace("""ff2"""   ,   """output.dense""")
  if "ff" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""ff"""   ,   """output.dense""")
  if "mlm_class" in orig_key:
    __snake_case:		Union[str, Any]					    =		orig_key.replace("""mlm.mlm_class"""   ,   """cls.predictions.decoder""")
  if "mlm" in orig_key:
    __snake_case:		List[Any]					    =		orig_key.replace("""mlm"""   ,   """cls.predictions.transform""")
  if "cls" not in orig_key:
    __snake_case:		Dict					    =		"""nystromformer.""" + orig_key

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

    if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
      continue
    else:
      __snake_case:		Tuple					    =		val

  __snake_case:		str					    =		orig_state_dict["""cls.predictions.decoder.bias"""]
  __snake_case:		int					    =		(
      torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
  )

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		convert_checkpoint_helper(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  model.load_state_dict(SCREAMING_SNAKE_CASE__)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE__)

  print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''')


if __name__ == "__main__":
      __UpperCAmelCase			:  str										=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The json file for Nystromformer model config.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      __UpperCAmelCase			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






