



from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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 numpy as np
	import tensorflow as tf

	from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
	from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileViTImageProcessor

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a   (				self		):
											a__					   =		self.config_class(**self.inputs_dict		)
											self.parent.assertTrue(hasattr(a_	,	"""hidden_sizes"""		)		)
											self.parent.assertTrue(hasattr(a_	,	"""neck_hidden_sizes"""		)		)
											self.parent.assertTrue(hasattr(a_	,	"""num_attention_heads"""		)		)




class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=13	,	a_=32	,	a_=2	,	a_=3	,	a_=640	,	a_=4	,	a_="silu"	,	a_=3	,	a_=32	,	a_=0.1	,	a_=0.1	,	a_=0.1	,	a_=0.02	,	a_=True	,	a_=True	,	a_=10	,	a_=None	,	):
											a__					   =		parent
											a__					   =		batch_size
											a__					   =		image_size
											a__					   =		patch_size
											a__					   =		num_channels
											a__					   =		last_hidden_size
											a__					   =		num_attention_heads
											a__					   =		hidden_act
											a__					   =		conv_kernel_size
											a__					   =		output_stride
											a__					   =		hidden_dropout_prob
											a__					   =		attention_probs_dropout_prob
											a__					   =		classifier_dropout_prob
											a__					   =		use_labels
											a__					   =		is_training
											a__					   =		num_labels
											a__					   =		initializer_range
											a__					   =		scope


							def _a   (				self		):
											a__					   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

											a__					   =		self.get_config()

											return config, pixel_values, labels, pixel_labels


							def _a   (				self		):
											return MobileViTConfig(
											    image_size=self.image_size	,	patch_size=self.patch_size	,	num_channels=self.num_channels	,	num_attention_heads=self.num_attention_heads	,	hidden_act=self.hidden_act	,	conv_kernel_size=self.conv_kernel_size	,	output_stride=self.output_stride	,	hidden_dropout_prob=self.hidden_dropout_prob	,	attention_probs_dropout_prob=self.attention_probs_dropout_prob	,	classifier_dropout_prob=self.classifier_dropout_prob	,	initializer_range=self.initializer_range	,	)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_		):
											a__					   =		TFMobileViTModel(config=a_		)
											a__					   =		model(a_	,	training=a_		)
											a__					   =		a__					   =		self.image_size // self.output_stride
											self.parent.assertEqual(
											    result.last_hidden_state.shape	,	(self.batch_size, self.last_hidden_size, expected_height, expected_width)		)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_		):
											a__					   =		self.num_labels
											a__					   =		TFMobileViTForImageClassification(a_		)
											a__					   =		model(a_	,	labels=a_	,	training=a_		)
											self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels)		)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_		):
											a__					   =		self.num_labels
											a__					   =		TFMobileViTForSemanticSegmentation(a_		)
											a__					   =		a__					   =		self.image_size // self.output_stride

											a__					   =		model(a_	,	training=a_		)
											self.parent.assertEqual(
											    result.logits.shape	,	(self.batch_size, self.num_labels, expected_height, expected_width)		)

											a__					   =		model(a_	,	labels=a_	,	training=a_		)
											self.parent.assertEqual(
											    result.logits.shape	,	(self.batch_size, self.num_labels, expected_height, expected_width)		)







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




@require_tf
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   List[str]       =       (
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							UpperCamelCase__					:   Tuple       =       (
							    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							UpperCamelCase__					:   Tuple       =       False
							UpperCamelCase__					:   int       =       False
							UpperCamelCase__					:   Any       =       False
							UpperCamelCase__					:   Dict       =       False
							UpperCamelCase__					:   int       =       False


							def _a   (				self		):
											a__					   =		TFMobileViTModelTester(self		)
											a__					   =		TFMobileViTConfigTester(self	,	config_class=a_	,	has_text_modality=a_		)


							def _a   (				self		):
											self.config_tester.run_common_tests()


							@unittest.skip(reason="""MobileViT does not use inputs_embeds"""		)
							def _a   (				self		):
											pass


							@unittest.skip(reason="""MobileViT does not support input and output embeddings"""		)
							def _a   (				self		):
											pass


							@unittest.skip(reason="""MobileViT does not output attentions"""		)
							def _a   (				self		):
											pass


							def _a   (				self		):
											a__   ,      a__					   =		self.model_tester.prepare_config_and_inputs_for_common()

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

															a__					   =		["""pixel_values"""]
															self.assertListEqual(arg_names[:1]	,	a_		)


							def _a   (				self		):
											a__					   =		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_		):
															a__					   =		model_class(a_		)

															a__					   =		model(**self._prepare_for_class(a_	,	a_		)		)

															a__					   =		outputs.hidden_states

															a__					   =		5
															self.assertEqual(len(a_		)	,	a_		)

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															a__					   =		2
															for i in range(len(a_		)		):
																			self.assertListEqual(
																			    list(hidden_states[i].shape[-2:]		)	,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,	)
																			divisor *= 2

															self.assertEqual(self.model_tester.output_stride	,	divisor // 2		)

											a__   ,      a__					   =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															a__					   =		True
															check_hidden_states_output(a_	,	a_	,	a_		)

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

															check_hidden_states_output(a_	,	a_	,	a_		)


							def _a   (				self		):
											a__					   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*a_		)


							def _a   (				self		):
											a__					   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*a_		)


							@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."""	,	)
							def _a   (				self		):
											super().test_dataset_conversion()


							def _a   (				self	,	a_	,	a_	,	a_=2E-1	,	a_=2E-1		):
											self.assertTrue(np.allclose(a_	,	a_	,	atol=a_	,	rtol=a_		)		)


							@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		):
											a__   ,      a__					   =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
															if model_class.__name__ != "TFMobileViTModel":
																			a__					   =		model_class(a_		)
																			if getattr(a_	,	"""hf_compute_loss"""	,	a_		):
																							super().test_keras_fit()


							def _a   (				self		):
											a__   ,      a__					   =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															# set an ignore index to correctly test the masked loss used in
															# `TFMobileViTForSemanticSegmentation`.
															if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																			a__					   =		5

															a__					   =		model_class(a_		)
															if getattr(a_	,	"""hf_compute_loss"""	,	a_		):
																			# The number of elements in the loss should be the same as the number of elements in the label
																			a__					   =		self._prepare_for_class(inputs_dict.copy()	,	a_	,	return_labels=a_		)
																			a__					   =		prepared_for_class[
																			    sorted(prepared_for_class.keys() - inputs_dict.keys()	,	reverse=a_		)[0]
																			]
																			a__					   =		added_label.shape.as_list()[:1]

																			# Test that model correctly compute the loss with kwargs
																			a__					   =		self._prepare_for_class(inputs_dict.copy()	,	a_	,	return_labels=a_		)
																			a__					   =		{"""input_ids""", """pixel_values""", """input_features"""}
																			a__					   =		possible_input_names.intersection(set(a_		)		).pop()
																			a__					   =		prepared_for_class.pop(a_		)

																			a__					   =		model(a_	,	**a_		)[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

																			# Test that model correctly compute the loss when we mask some positions
																			a__					   =		self._prepare_for_class(inputs_dict.copy()	,	a_	,	return_labels=a_		)
																			a__					   =		{"""input_ids""", """pixel_values""", """input_features"""}
																			a__					   =		possible_input_names.intersection(set(a_		)		).pop()
																			a__					   =		prepared_for_class.pop(a_		)
																			if "labels" in prepared_for_class:
																							a__					   =		prepared_for_class["""labels"""].numpy()
																							if len(labels.shape		) > 1 and labels.shape[1] != 1:
																											# labels[0] = -100
																											a__					   =		tf.convert_to_tensor(a_		)
																											a__					   =		model(a_	,	**a_		)[0]
																											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
																											self.assertTrue(not np.any(np.isnan(loss.numpy()		)		)		)

                # Test that model correctly compute the loss with a dict
																			a__					   =		self._prepare_for_class(inputs_dict.copy()	,	a_	,	return_labels=a_		)
																			a__					   =		model(a_		)[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

																			# Test that model correctly compute the loss with a tuple
																			a__					   =		self._prepare_for_class(inputs_dict.copy()	,	a_	,	return_labels=a_		)

																			# Get keys that were added with the _prepare_for_class function
																			a__					   =		prepared_for_class.keys() - inputs_dict.keys()
																			a__					   =		inspect.signature(model.call		).parameters
																			a__					   =		list(signature.keys()		)

																			# Create a dictionary holding the location of the tensors in the tuple
																			a__					   =		{0: input_name}
																			for label_key in label_keys:
																							a__					   =		signature_names.index(a_		)
																							a__					   =		label_key
																			a__					   =		sorted(tuple_index_mapping.items()		)
																			# Initialize a list with their default values, update the values and convert to a tuple
																			a__					   =		[]

																			for name in signature_names:
																							if name != "kwargs":
																											list_input.append(signature[name].default		)

																			for index, value in sorted_tuple_index_mapping:
																							a__					   =		prepared_for_class[value]

																			a__					   =		tuple(a_		)

																			# Send to model
																			a__					   =		model(tuple_input[:-1]		)[0]

																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)







							@slow
							def _a   (				self		):
											for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__					   =		TFMobileViTModel.from_pretrained(a_		)
															self.assertIsNotNone(a_		)




def       A_   (						):

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




@require_tf
class 			__snake_case   ( unittest.TestCase):







							'''simple docstring'''






							@slow
							def _a   (				self		):
											a__					   =		TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""		)

											a__					   =		MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""		)
											a__					   =		prepare_img()
											a__					   =		image_processor(images=a_	,	return_tensors="""tf"""		)

											# forward pass
											a__					   =		model(**a_	,	training=a_		)

											# verify the logits
											a__					   =		tf.TensorShape((1, 1_000)		)
											self.assertEqual(outputs.logits.shape	,	a_		)

											a__					   =		tf.constant([-1.9_364, -1.2_327, -0.4_653]		)

											tf.debugging.assert_near(outputs.logits[0, :3]	,	a_	,	atol=1E-4	,	rtol=1E-0_4		)







							@slow
							def _a   (				self		):
											# `from_pt` will be removed
											a__					   =		TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""		)

											a__					   =		MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""		)

											a__					   =		prepare_img()
											a__					   =		image_processor(images=a_	,	return_tensors="""tf"""		)

											# forward pass
											a__					   =		model(inputs.pixel_values	,	training=a_		)
											a__					   =		outputs.logits

											# verify the logits
											a__					   =		tf.TensorShape((1, 21, 32, 32)		)
											self.assertEqual(logits.shape	,	a_		)

											a__					   =		tf.constant(
											    [
											        [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
											        [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
											        [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
											    ]		)

											tf.debugging.assert_near(logits[0, :3, :3, :3]	,	a_	,	rtol=1E-4	,	atol=1E-4		)






from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# Check if RHS is negative
											if np.any(tableau[:, -1]	,	where=tableau[:, -1] < 0		):
															raise ValueError("""RHS must be > 0"""		)

											a__					   =		tableau
											a__   ,      a__					   =		tableau.shape

											# Number of decision variables x1, x2, x3...
											a__					   =		n_vars

											# Number of artificial variables to be minimised
											a__					   =		len(np.where(tableau[self.n_vars : -1] == -1		)[0]		)

											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
											a__					   =		(self.n_art_vars > 0) + 1

											# Number of slack variables added to make inequalities into equalities
											a__					   =		self.n_rows - self.n_stages

											# Objectives for each stage
											a__					   =		["""max"""]

											# In two stage simplex, first minimise then maximise
											if self.n_art_vars:
															self.objectives.append("""min"""		)

											a__					   =		[""""""]

											# Index of current pivot row and column
											a__					   =		None
											a__					   =		None

											# Does objective row only contain (non)-negative values?
											a__					   =		False


							@staticmethod
							def _a   (				*a_		):
											if len(a_		) != 3:
															raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""		)

											if not all(x >= 0 and isinstance(a_	,	a_		) for x in args		):
															raise ValueError("""All arguments must be non-negative integers"""		)

											# decision | slack | artificial
											a__					   =		["""x""", """s""", """a"""]
											a__					   =		[]
											for i in range(3		):
															for j in range(args[i]		):
																			titles.append(string_starts[i] + str(j + 1		)		)
											titles.append("""RHS"""		)
											return titles


							def _a   (				self	,	a_		):
											a__					   =		self.objectives[-1]

											# Find entries of highest magnitude in objective rows
											a__					   =		(objective == """min""") - (objective == """max""")
											a__					   =		np.argmax(sign * tableau[0, : self.n_vars]		)

											# Choice is only valid if below 0 for maximise, and above for minimise
											if sign * self.tableau[0, col_idx] <= 0:
															a__					   =		True
															return 0, 0

											# Pivot row is chosen as having the lowest quotient when elements of
											# the pivot column divide the right-hand side

											# Slice excluding the objective rows
											a__					   =		slice(self.n_stages	,	self.n_rows		)

											# RHS
											a__					   =		tableau[s, -1]

											# Elements of pivot column within slice
											a__					   =		tableau[s, col_idx]

											# Array filled with nans
											a__					   =		np.full(self.n_rows - self.n_stages	,	np.nan		)

											# If element in pivot column is greater than zeron_stages, return
											# quotient or nan otherwise
											a__					   =		np.divide(a_	,	a_	,	out=a_	,	where=divisor > 0		)

											# Arg of minimum quotient excluding the nan values. n_stages is added
											# to compensate for earlier exclusion of objective columns
											a__					   =		np.nanargmin(a_		) + self.n_stages
											return row_idx, col_idx


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		tableau[row_idx].copy()

											a__					   =		piv_row[col_idx]

											# Entry becomes 1
											piv_row *= 1 / piv_val

											# Variable in pivot column becomes basic, ie the only non-zero entry
											for idx, coeff in enumerate(tableau[:, col_idx]		):
															tableau[idx] += -coeff * piv_row
											a__					   =		piv_row
											return tableau


							def _a   (				self	,	a_		):
											self.objectives.pop()

											if not self.objectives:
															return tableau

											# Slice containing ids for artificial columns
											a__					   =		slice(-self.n_art_vars - 1	,	-1		)

											# Delete the artificial variable columns
											a__					   =		np.delete(a_	,	a_	,	axis=1		)

											# Delete the objective row of the first stage
											a__					   =		np.delete(a_	,	0	,	axis=0		)

											a__					   =		1
											self.n_rows -= 1
											a__					   =		0
											a__					   =		False
											return tableau


							def _a   (				self		):
											# Stop simplex algorithm from cycling.
											for _ in range(100		):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																			a__					   =		self.generate_col_titles(
																			    self.n_vars	,	self.n_slack	,	self.n_art_vars		)

																			# Find the values of each variable at optimal solution
																			return self.interpret_tableau(self.tableau	,	self.col_titles		)

															a__   ,      a__					   =		self.find_pivot(self.tableau		)

															# If there are no more negative values in objective row
															if self.stop_iter:
																			# Delete artificial variable columns and rows. Update attributes
																			a__					   =		self.change_stage(self.tableau		)
															else:
																			a__					   =		self.pivot(self.tableau	,	a_	,	a_		)
											return {}







							def _a   (				self	,	a_	,	a_		):
											a__					   =		{"""P""": abs(tableau[0, -1]		)}

											for i in range(self.n_vars		):
															# Gives ids of nonzero entries in the ith column
															a__					   =		np.nonzero(tableau[:, i]		)
															a__					   =		len(nonzero[0]		)

															# First entry in the nonzero ids
															a__					   =		nonzero[0][0]
															a__					   =		tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																			a__					   =		tableau[nonzero_rowidx, -1]
																			a__					   =		rhs_val

        # Check for basic variables
											for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																			output_dict.setdefault(a_	,	0		)
											return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()

