
'''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__( snake_case__     ):


			'''simple docstring'''






			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  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	,   __lowerCAmelCase	,   __lowerCAmelCase=13	,   __lowerCAmelCase=64	,   __lowerCAmelCase=3	,   __lowerCAmelCase=4	,   __lowerCAmelCase=[2, 2, 2, 2]	,   __lowerCAmelCase=[8, 4, 2, 1]	,   __lowerCAmelCase=[16, 32, 64, 128]	,   __lowerCAmelCase=[1, 4, 8, 16]	,   __lowerCAmelCase=[1, 2, 4, 8]	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=16	,   __lowerCAmelCase=3	,   __lowerCAmelCase=None	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  parent
					lowerCAmelCase        =  batch_size
					lowerCAmelCase        =  image_size
					lowerCAmelCase        =  num_channels
					lowerCAmelCase        =  num_encoder_blocks
					lowerCAmelCase        =  sr_ratios
					lowerCAmelCase        =  depths
					lowerCAmelCase        =  hidden_sizes
					lowerCAmelCase        =  downsampling_rates
					lowerCAmelCase        =  num_attention_heads
					lowerCAmelCase        =  is_training
					lowerCAmelCase        =  use_labels
					lowerCAmelCase        =  hidden_act
					lowerCAmelCase        =  hidden_dropout_prob
					lowerCAmelCase        =  attention_probs_dropout_prob
					lowerCAmelCase        =  initializer_range
					lowerCAmelCase        =  decoder_hidden_size
					lowerCAmelCase        =  num_labels
					lowerCAmelCase        =  scope



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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



			def 							a_	(       self):




					"""simple docstring"""


					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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  GLPNModel(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase        =  model(__lowerCAmelCase)
					lowerCAmelCase        =  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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.num_labels
					lowerCAmelCase        =  GLPNForDepthEstimation(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase        =  model(__lowerCAmelCase)
					self.parent.assertEqual(result.predicted_depth.shape	,   (self.batch_size, self.image_size, self.image_size))
					lowerCAmelCase        =  model(__lowerCAmelCase	,   labels=__lowerCAmelCase)
					self.parent.assertEqual(result.predicted_depth.shape	,   (self.batch_size, self.image_size, self.image_size))







			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.prepare_config_and_inputs()
					lowerCAmelCase        =  config_and_inputs
					lowerCAmelCase        =  {'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class     a__( snake_case__			,						snake_case__			,						unittest.TestCase     ):


			'''simple docstring'''






			UpperCAmelCase_      :					List[Any]								=	(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
			UpperCAmelCase_      :					str								=	(
			    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
			)

			UpperCAmelCase_      :					List[str]								=	False
			UpperCAmelCase_      :					Dict								=	False
			UpperCAmelCase_      :					Union[str, Any]								=	False



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  GLPNModelTester(self)
					lowerCAmelCase        =  GLPNConfigTester(self	,   config_class=__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					pass



			@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods""")
			def 							a_	(       self):




					"""simple docstring"""


					pass



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase        =  ['pixel_values']
							self.assertListEqual(arg_names[:1]	,   __lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase        =  True

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

							lowerCAmelCase        =  sum(self.model_tester.depths)
							self.assertEqual(len(__lowerCAmelCase)	,   __lowerCAmelCase)

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

							self.assertEqual(len(__lowerCAmelCase)	,   __lowerCAmelCase)

							# verify the first attentions (first block, first layer)
							lowerCAmelCase        =  (self.model_tester.image_size // 4) ** 2
							lowerCAmelCase        =  (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)
							lowerCAmelCase        =  (self.model_tester.image_size // 32) ** 2
							lowerCAmelCase        =  (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]	,   )
							lowerCAmelCase        =  len(__lowerCAmelCase)

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

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

							lowerCAmelCase        =  outputs.attentions

							self.assertEqual(len(__lowerCAmelCase)	,   __lowerCAmelCase)
							# verify the first attentions (first block, first layer)
							lowerCAmelCase        =  (self.model_tester.image_size // 4) ** 2
							lowerCAmelCase        =  (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 							a_	(       self):




					"""simple docstring"""


					def check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()

							with torch.no_grad():
									lowerCAmelCase        =  model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase))

							lowerCAmelCase        =  outputs.hidden_states

							lowerCAmelCase        =  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,
							    ]	,   )

					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase        =  True
							check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)

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

							check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					if not self.model_tester.is_training:
							return

					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase        =  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":
									lowerCAmelCase        =  inputs_dict['pixel_values'].shape
									lowerCAmelCase        =  torch.zeros(
									    [self.model_tester.batch_size, height, width]	,   device=__lowerCAmelCase).long()
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.train()
							lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase	,   return_labels=__lowerCAmelCase)
							lowerCAmelCase        =  model(**__lowerCAmelCase).loss
							loss.backward()







			@slow
			def 							a_	(       self):




					"""simple docstring"""


					for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase        =  GLPNModel.from_pretrained(__lowerCAmelCase)
							self.assertIsNotNone(__lowerCAmelCase)







def     snake_case__							(      )   ->     List[Any]:

		'''simple docstring'''


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






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


			'''simple docstring'''






			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
					lowerCAmelCase        =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__lowerCAmelCase)

					lowerCAmelCase        =  prepare_img()
					lowerCAmelCase        =  image_processor(images=__lowerCAmelCase	,   return_tensors="""pt""").to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase        =  model(**__lowerCAmelCase)

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

					lowerCAmelCase        =  torch.tensor(
					    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]).to(__lowerCAmelCase)

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





'''simple docstring'''




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def     snake_case__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

		# Embeddings
		# Position IDs
		lowerCAmelCase        =  fnet_pretraining_model.state_dict()

		lowerCAmelCase        =  state_dict["""fnet.embeddings.position_ids"""]
		lowerCAmelCase        =  {"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

		# Encoder Layers
		for layer in range(config.num_hidden_layers			):
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

		# Pooler Layers
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_bias"""]			)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(_A			)

		# Save PreTrained
		print(f"Saving pretrained model to {save_path}"			)
		fnet_pretraining_model.save_pretrained(_A			)


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--fnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained FNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						__lowercase				   =  parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




