
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				str							,A      :				List[str]							,A      :				Optional[Any]=13							,A      :				List[str]=32							,A      :				Optional[Any]=3							,A      :				Optional[Any]=4							,A      :				Tuple=[10, 20, 30, 40]							,A      :				Union[str, Any]=[2, 2, 3, 2]							,A      :				List[Any]=True							,A      :				Dict=True							,A      :				List[str]=37							,A      :				Union[str, Any]="gelu"							,A      :				Optional[int]=10							,A      :				Optional[Any]=0.02							,A      :				Union[str, Any]=3							,A      :				Union[str, Any]=None							,):
					__A         =				parent
					__A         =				batch_size
					__A         =				image_size
					__A         =				num_channels
					__A         =				num_stages
					__A         =				hidden_sizes
					__A         =				depths
					__A         =				is_training
					__A         =				use_labels
					__A         =				intermediate_size
					__A         =				hidden_act
					__A         =				type_sequence_label_size
					__A         =				initializer_range
					__A         =				scope






	def        UpperCamelCase_						( self      :				Dict				):
					__A         =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

					__A         =				None
					if self.use_labels:
									__A         =				ids_tensor([self.batch_size]							,self.type_sequence_label_size				)

					__A         =				self.get_config()

					return config, pixel_values, labels






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return ConvNextConfig(
					    num_channels=self.num_channels							,hidden_sizes=self.hidden_sizes							,depths=self.depths							,num_stages=self.num_stages							,hidden_act=self.hidden_act							,is_decoder=A							,initializer_range=self.initializer_range							,)






	def        UpperCamelCase_						( self      :				Tuple							,A      :				Union[str, Any]							,A      :				List[Any]							,A      :				Any				):
					__A         =				TFConvNextModel(config=A				)
					__A         =				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 // 32, self.image_size // 32)							,)






	def        UpperCamelCase_						( self      :				List[str]							,A      :				List[str]							,A      :				List[str]							,A      :				str				):
					__A         =				self.type_sequence_label_size
					__A         =				TFConvNextForImageClassification(A				)
					__A         =				model(A							,labels=A							,training=A				)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.type_sequence_label_size)				)







	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					__A         =				self.prepare_config_and_inputs()
					__A    ,							__A    ,							__A         =				config_and_inputs
					__A         =				{"pixel_values": pixel_values}
					return config, inputs_dict





@require_tf
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
	snake_case_      					=  (
	    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
	    if is_tf_available()
	    else {}
	)

	snake_case_      					=  False
	snake_case_      					=  False
	snake_case_      					=  False
	snake_case_      					=  False
	snake_case_      					=  False






	def        UpperCamelCase_						( self      :				Tuple				):
					__A         =				TFConvNextModelTester(self				)
					__A         =				ConfigTester(
					    self							,config_class=A							,has_text_modality=A							,hidden_size=37							,)






	@unittest.skip(reason="ConvNext does not use inputs_embeds"				)
	def        UpperCamelCase_						( self      :				Tuple				):
					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        UpperCamelCase_						( self      :				Tuple				):
					super().test_keras_fit()






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






	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					__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        UpperCamelCase_						( self      :				Optional[int]				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*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        UpperCamelCase_						( self      :				List[str]				):
					super().test_dataset_conversion()






	def        UpperCamelCase_						( self      :				str				):
					def check_hidden_states_output(A      :				Tuple							,A      :				Union[str, Any]							,A      :				Dict				):
									__A         =				model_class(A				)

									__A         =				model(**self._prepare_for_class(A							,A				)				)
									__A         =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									__A         =				self.model_tester.num_stages
									self.assertEqual(len(A				)							,expected_num_stages + 1				)

									# ConvNext'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 // 4, self.model_tester.image_size // 4]							,)

					__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        UpperCamelCase_						( self      :				Optional[Any]				):
					__A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

					def check_equivalence(A      :				List[Any]							,A      :				Any							,A      :				Union[str, Any]							,A      :				str={}				):
									__A         =				model(A							,return_dict=A							,**A				)
									__A         =				model(A							,return_dict=A							,**A				).to_tuple()

									def recursive_check(A      :				Dict							,A      :				Optional[int]				):
													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:
									__A         =				model_class(A				)

									__A         =				self._prepare_for_class(A							,A				)
									__A         =				self._prepare_for_class(A							,A				)
									check_equivalence(A							,A							,A				)

									__A         =				self._prepare_for_class(A							,A							,return_labels=A				)
									__A         =				self._prepare_for_class(A							,A							,return_labels=A				)
									check_equivalence(A							,A							,A				)

									__A         =				self._prepare_for_class(A							,A				)
									__A         =				self._prepare_for_class(A							,A				)
									check_equivalence(A							,A							,A							,{"output_hidden_states": True}				)

									__A         =				self._prepare_for_class(A							,A							,return_labels=A				)
									__A         =				self._prepare_for_class(A							,A							,return_labels=A				)
									check_equivalence(A							,A							,A							,{"output_hidden_states": True}				)






	def        UpperCamelCase_						( self      :				List[str]				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A				)







	@slow
	def        UpperCamelCase_						( self      :				Optional[int]				):
					__A         =				TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"				)
					self.assertIsNotNone(A				)





def 		UpperCAmelCase    (			)	->			str:







				"""simple docstring"""



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





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




	'''simple docstring'''





	@cached_property
	def        UpperCamelCase_						( self      :				List[str]				):
					return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"				) if is_vision_available() else None







	@slow
	def        UpperCamelCase_						( self      :				List[Any]				):
					__A         =				TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"				)

					__A         =				self.default_image_processor
					__A         =				prepare_img()
					__A         =				image_processor(images=A							,return_tensors="tf"				)

					# forward pass
					__A         =				model(**A				)

					# verify the logits
					__A         =				tf.TensorShape((1, 10_00)				)
					self.assertEqual(outputs.logits.shape							,A				)

					__A         =				tf.constant([-0.02_60, -0.47_39, 0.19_11]				)

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


from typing import Any

import numpy as np





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				np.ndarray							,A      :				int				):
					# 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        UpperCamelCase_						( *A      :				int				):
					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        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray				):
					__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        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray							,A      :				int							,A      :				int				):
					__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        UpperCamelCase_						( self      :				Optional[Any]							,A      :				np.ndarray				):
					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        UpperCamelCase_						( self      :				Dict				):
					# Stop simplex algorithm from cycling.
					for _ in range(1_00				):
									# 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        UpperCamelCase_						( self      :				Dict							,A      :				np.ndarray							,A      :				list[str]				):
					__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()
