

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=3					,						_A=3_2					,						_A=3					,						_A=1_0					,						_A=[1_0, 2_0, 3_0, 4_0]					,						_A=[1, 1, 2, 1]					,						_A=True					,						_A=True					,						_A="relu"					,						_A=3					,						_A=None					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							embeddings_size
												__lowerCAmelCase					=							hidden_sizes
												__lowerCAmelCase					=							depths
												__lowerCAmelCase					=							is_training
												__lowerCAmelCase					=							use_labels
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							num_labels
												__lowerCAmelCase					=							scope
												__lowerCAmelCase					=							len(_A							)




							def        A__  ( self							):
												__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.num_labels							)

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




							def        A__  ( self							):
												return RegNetConfig(
												    num_channels=self.num_channels					,						embeddings_size=self.embeddings_size					,						hidden_sizes=self.hidden_sizes					,						depths=self.depths					,						hidden_act=self.hidden_act					,						num_labels=self.num_labels					,						)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							TFRegNetModel(config=_A							)
												__lowerCAmelCase					=							model(_A					,						training=_A							)
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape					,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,						)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							self.num_labels
												__lowerCAmelCase					=							TFRegNetForImageClassification(_A							)
												__lowerCAmelCase					=							model(_A					,						labels=_A					,						training=_A							)
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)							)




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





@require_tf
class     snake_case_			(				_a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
							__UpperCAmelCase       =(
							    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TFRegNetModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												return




							@unittest.skip(reason='RegNet does not use inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices('GPU'							)							) == 0					,						reason='TF does not support backprop for grouped convolutions on CPU.'					,						)
							@slow
							def        A__  ( self							):
												super().test_keras_fit()




							@unittest.skip(reason='RegNet does not support input and output embeddings'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__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.call							)
																	# 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							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A							)




							def        A__  ( self							):
												def check_hidden_states_output(_A					,						_A					,						_A							):
																	__lowerCAmelCase					=							model_class(_A							)
																	__lowerCAmelCase					=							model(**self._prepare_for_class(_A					,						_A							)					,						training=_A							)

																	__lowerCAmelCase					=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	__lowerCAmelCase					=							self.model_tester.num_stages
																	self.assertEqual(len(_A							)					,						expected_num_stages + 1							)

																	# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]							)					,						[self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,						)

												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							['basic', 'bottleneck']
												for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																						__lowerCAmelCase					=							layer_type
																						__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							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(_A					,						_A					,						_A					,						_A={}							):
																	__lowerCAmelCase					=							model(_A					,						return_dict=_A					,						**_A							)
																	__lowerCAmelCase					=							model(_A					,						return_dict=_A					,						**_A							).to_tuple()

																	def recursive_check(_A					,						_A							):
																						if isinstance(_A					,						(List, Tuple)							):
																											for tuple_iterable_value, dict_iterable_value in zip(_A					,						_A							):
																																recursive_check(_A					,						_A							)
																						elif tuple_object is None:
																											return
																						else:
																											self.assertTrue(
																											    all(tf.equal(_A					,						_A							)							)					,						msg=(
																											        'Tuple and dict output are not equal. Difference:'
																											        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}"""
																											    )					,						)

																						recursive_check(_A					,						_A							)

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	check_equivalence(_A					,						_A					,						_A							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	check_equivalence(_A					,						_A					,						_A							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	check_equivalence(_A					,						_A					,						_A					,						{'output_hidden_states': True}							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	check_equivalence(_A					,						_A					,						_A					,						{'output_hidden_states': True}							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_A							)




							@slow
							def        A__  ( self							):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__lowerCAmelCase					=							TFRegNetModel.from_pretrained(_A							)
																	self.assertIsNotNone(_A							)


def        __lowercase    (  ):

					"""simple docstring"""






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





@require_tf
@require_vision
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							@cached_property
							def        A__  ( self							):
												return (
												    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
												    if is_vision_available()
												    else None
												)




							@slow
							def        A__  ( self							):
												__lowerCAmelCase					=							TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

												__lowerCAmelCase					=							self.default_image_processor
												__lowerCAmelCase					=							prepare_img()
												__lowerCAmelCase					=							image_processor(images=_A					,						return_tensors='tf'							)

												# forward pass
												__lowerCAmelCase					=							model(**_A					,						training=_A							)

												# verify the logits
												__lowerCAmelCase					=							tf.TensorShape((1, 1_0_0_0)							)
												self.assertEqual(outputs.logits.shape					,						_A							)

												__lowerCAmelCase					=							tf.constant([-0.4180, -1.5051, -3.4836]							)

												tf.debugging.assert_near(outputs.logits[0, :3]					,						_A					,						atol=1e-4							)









import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:





		class     snake_case_			:


									"""simple docstring"""





									@staticmethod
									def        A__  ( *_A					,						**_A							):
														pass


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hashlib.mda(image.tobytes()      )
					return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DepthEstimationPipeline(model=_A					,						image_processor=_A							)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
												self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}					,						_A							)
												import datasets

												__lowerCAmelCase					=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'					,						'image'					,						split='test'							)
												__lowerCAmelCase					=							depth_estimator(
												    [
												        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
												        'http://images.cocodataset.org/val2017/000000039769.jpg',
												        # RGBA
												        dataset[0]['file'],
												        # LA
												        dataset[1]['file'],
												        # L
												        dataset[2]['file'],
												    ]							)
												self.assertEqual(
												    [
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												    ]					,						_A					,						)




							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF'							)
							def        A__  ( self							):
												pass




							@slow
							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'Intel/dpt-large'
												__lowerCAmelCase					=							pipeline('depth-estimation'					,						model=_A							)
												__lowerCAmelCase					=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
												__lowerCAmelCase					=							hashimage(outputs['depth']							)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)					,						29.304							)
												self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)					,						2.662							)




							@require_torch
							def        A__  ( self							):
												# This is highly irregular to have no small tests.
												self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)






