


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 					__lowercase					(       a_     ):



		"""simple docstring"""

		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     self.config_class(**self.inputs_dict  )
					self.parent.assertTrue(hasattr(A		,			"""hidden_sizes"""  )  )
					self.parent.assertTrue(hasattr(A		,			"""num_attention_heads"""  )  )
					self.parent.assertTrue(hasattr(A		,			"""num_encoder_blocks"""  )  )







class 					__lowercase					:



		"""simple docstring"""

		def __init__(   self		,			A		,			A=13		,			A=64		,			A=3		,			A=4		,			A=[2, 2, 2, 2]		,			A=[8, 4, 2, 1]		,			A=[16, 32, 64, 1_28]		,			A=[1, 4, 8, 16]		,			A=[1, 2, 4, 8]		,			A=True		,			A=True		,			A="gelu"		,			A=0.1		,			A=0.1		,			A=0.02		,			A=16		,			A=3		,			A=None		,			)     ->   str:







					'''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  )     ->   Union[str, Any]:







					'''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  )     ->   Any:







					'''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		,			A		,			A		,			A  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     GLPNModel(config=A  )
					model.to(A  )
					model.eval()
					lowerCamelCase								=     model(A  )
					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		,			A		,			A		,			A  )     ->   Any:







					'''simple docstring'''







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







		def 				__A      (   self  )     ->   Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase								=     self.prepare_config_and_inputs()
					lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     config_and_inputs
					lowerCamelCase								=     {"""pixel_values""": pixel_values}
					return config, inputs_dict







@require_torch
class 					__lowercase					(       a_ ,       a_ ,       unittest.TestCase     ):



		"""simple docstring"""

		UpperCamelCase    :				Tuple 					=		(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
		UpperCamelCase    :				Union[str, Any] 					=		(
		    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
		)

		UpperCamelCase    :				Dict 					=		False
		UpperCamelCase    :				str 					=		False
		UpperCamelCase    :				List[Any] 					=		False







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     GLPNModelTester(self  )
					lowerCamelCase								=     GLPNConfigTester(self		,			config_class=A  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					self.config_tester.run_common_tests()







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*A  )







		@unittest.skip("""GLPN does not use inputs_embeds"""  )
		def 				__A      (   self  )     ->   Optional[Any]:







					'''simple docstring'''







					pass







		@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







					pass







		def 				__A      (   self  )     ->   int:







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								lowerCamelCase								=     model_class(A  )
								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]		,			A  )







		def 				__A      (   self  )     ->   Optional[Any]:







					'''simple docstring'''







					lowerCamelCase		,		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(A  )
								model.to(A  )
								model.eval()
								with torch.no_grad():
											lowerCamelCase								=     model(**self._prepare_for_class(A		,			A  )  )
								lowerCamelCase								=     outputs.attentions

								lowerCamelCase								=     sum(self.model_tester.depths  )
								self.assertEqual(len(A  )		,			A  )

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

								self.assertEqual(len(A  )		,			A  )

								# 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(A  )

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

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

								lowerCamelCase								=     outputs.attentions

								self.assertEqual(len(A  )		,			A  )
								# 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  )     ->   List[str]:







					'''simple docstring'''







					def check_hidden_states_output(A		,			A		,			A  ):
								lowerCamelCase								=     model_class(A  )
								model.to(A  )
								model.eval()

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

								lowerCamelCase								=     outputs.hidden_states

								lowerCamelCase								=     self.model_tester.num_encoder_blocks
								self.assertEqual(len(A  )		,			A  )

								# 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		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()

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

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

								check_hidden_states_output(A		,			A		,			A  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					if not self.model_tester.is_training:
								return

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







		@slow
		def 				__A      (   self  )     ->   int:







					'''simple docstring'''







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






def   __lowerCamelCase	(       ):





			'''simple docstring'''




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







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



		"""simple docstring"""

		@slow
		def 				__A      (   self  )     ->   int:







					'''simple docstring'''







					lowerCamelCase								=     GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
					lowerCamelCase								=     GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(A  )

					lowerCamelCase								=     prepare_img()
					lowerCamelCase								=     image_processor(images=A		,			return_tensors="""pt"""  ).to(A  )

					# forward pass
					with torch.no_grad():
								lowerCamelCase								=     model(**A  )

					# verify the predicted depth
					lowerCamelCase								=     torch.Size([1, 4_80, 6_40]  )
					self.assertEqual(outputs.predicted_depth.shape		,			A  )

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

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





import unittest

from cer import CER


UpperCAmelCase      :						Union[str, Any]    =		CER()







class 					__lowercase					(       unittest.TestCase     ):



		"""simple docstring"""

		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""White House"""]
					lowerCamelCase								=     ["""white house"""]
					# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1818181818  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""werewolf"""]
					# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1111111  ) < 1e-6  )

					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weae     wolf"""]
					# S = 1, D = 1, I = 0, N = 8, CER = 0.25
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.25  ) < 1e-6  )

					# consecutive whitespaces case 1
					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )

					# consecutive whitespaces case 2
					lowerCamelCase								=     ["""were   wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )







		def 				__A      (   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weaewolf"""]
					# S = 1, D = 0, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereawolf"""]
					# S = 0, D = 1, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereolf"""]
					# S = 0, D = 0, I = 1, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf""", """I am your father"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )

					lowerCamelCase								=     ["""werewolf""", """I am your father""", """doge"""]
					lowerCamelCase								=     ["""werxwolf""", """I       am your father""", """doge"""]
					# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.03571428  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""My hovercraft""", """is full of eels"""]
					lowerCamelCase								=     ["""My hovercraft is full""", """ of eels"""]
					# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
					# whitespace at the front of " of eels" will be strip during preporcessing
					# so need to insert 2 whitespaces
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A		,			concatenate_texts=A  )
					self.assertTrue(abs(char_error_rate - 0.071428  ) < 1e-6  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     [""" 能吞虾玻璃而 不霜身体啦"""]
					# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.4545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃""", """而不伤身体"""]
					lowerCamelCase								=     ["""我    能 吞 下 玻 璃""", """而不伤身体"""]
					# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.454545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertFalse(A		,			0.0  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     [""""""]
					lowerCamelCase								=     ["""Hypothesis"""]
					with self.assertRaises(A  ):
								cer.compute(predictions=A		,			references=A  )


if __name__ == "__main__":
		unittest.main()

