
'''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     (	SCREAMING_SNAKE_CASE__    ):





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict							):


				"""simple docstring"""

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






class    A     :





	def __init__(					self			:       Optional[Any]      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Union[str, Any]=13      ,							__magic_name__			:       int=64      ,							__magic_name__			:       Optional[Any]=3      ,							__magic_name__			:       Any=4      ,							__magic_name__			:       List[Any]=[2, 2, 2, 2]      ,							__magic_name__			:       str=[8, 4, 2, 1]      ,							__magic_name__			:       Any=[16, 32, 64, 128]      ,							__magic_name__			:       Union[str, Any]=[1, 4, 8, 16]      ,							__magic_name__			:       List[Any]=[1, 2, 4, 8]      ,							__magic_name__			:       List[str]=True      ,							__magic_name__			:       Tuple=True      ,							__magic_name__			:       List[Any]="gelu"      ,							__magic_name__			:       List[str]=0.1      ,							__magic_name__			:       Optional[int]=0.1      ,							__magic_name__			:       Any=0.02      ,							__magic_name__			:       Any=16      ,							__magic_name__			:       str=3      ,							__magic_name__			:       str=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 							__SCREAMING_SNAKE_CASE  (					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 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]							):


				"""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 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]      ,							__magic_name__			:       Dict      ,							__magic_name__			:       List[Any]      ,							__magic_name__			:       Dict							):


				"""simple docstring"""

				lowerCAmelCase__								=							GLPNModel(config=__magic_name__							)
				model.to(__magic_name__							)
				model.eval()
				lowerCAmelCase__								=							model(__magic_name__							)
				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 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       List[Any]							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE  (					self			:       str							):


				"""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    A     (	SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase    ):
	snake_case__      :Optional[Any]        =     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
	snake_case__      :str        =     (
	    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
	)

	snake_case__      :Dict        =     False
	snake_case__      :Optional[Any]        =     False
	snake_case__      :Tuple        =     False





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				lowerCAmelCase__								=							GLPNModelTester(self							)
				lowerCAmelCase__								=							GLPNConfigTester(self      ,							config_class=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple							):


				"""simple docstring"""

				self.config_tester.run_common_tests()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_depth_estimation(*__magic_name__							)





	@unittest.skip("GLPN does not use inputs_embeds"							)
	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				pass





	@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"							)
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]							):


				"""simple docstring"""

				pass





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							lowerCAmelCase__								=							model_class(__magic_name__							)
							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]      ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""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(__magic_name__							)
							model.to(__magic_name__							)
							model.eval()
							with torch.no_grad():
										lowerCAmelCase__								=							model(**self._prepare_for_class(__magic_name__      ,							__magic_name__							)							)
							lowerCAmelCase__								=							outputs.attentions

							lowerCAmelCase__								=							sum(self.model_tester.depths							)
							self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)

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

							self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)

							# 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(__magic_name__							)

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

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

							lowerCAmelCase__								=							outputs.attentions

							self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)
							# 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 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]							):


				"""simple docstring"""

				def check_hidden_states_output(__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       Any							):
							lowerCAmelCase__								=							model_class(__magic_name__							)
							model.to(__magic_name__							)
							model.eval()

							with torch.no_grad():
										lowerCAmelCase__								=							model(**self._prepare_for_class(__magic_name__      ,							__magic_name__							)							)

							lowerCAmelCase__								=							outputs.hidden_states

							lowerCAmelCase__								=							self.model_tester.num_encoder_blocks
							self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)

							# 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(__magic_name__      ,							__magic_name__      ,							__magic_name__							)

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

							check_hidden_states_output(__magic_name__      ,							__magic_name__      ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""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(__magic_name__							):
										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=__magic_name__							).long()
							lowerCAmelCase__								=							model_class(__magic_name__							)
							model.to(__magic_name__							)
							model.train()
							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__      ,							return_labels=__magic_name__							)
							lowerCAmelCase__								=							model(**__magic_name__							).loss
							loss.backward()







	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict							):


				"""simple docstring"""

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







def   A     (      )					->       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    ):





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
				lowerCAmelCase__								=							GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__magic_name__							)

				lowerCAmelCase__								=							prepare_img()
				lowerCAmelCase__								=							image_processor(images=__magic_name__      ,							return_tensors="pt"							).to(__magic_name__							)

				# forward pass
				with torch.no_grad():
							lowerCAmelCase__								=							model(**__magic_name__							)

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

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

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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   : Any					=		{
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__   : str					=		["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
		from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
		from .tokenization_fsmt import FSMTTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
		import sys

		UpperCAmelCase__   : int					=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





