import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_MAPPING,
					    DataaVecVisionForImageClassification,
					    DataaVecVisionForSemanticSegmentation,
					    DataaVecVisionModel,
					)
					from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor


class _a       :

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=100							,			__UpperCAmelCase=13							,			__UpperCAmelCase=30							,			__UpperCAmelCase=2							,			__UpperCAmelCase=3							,			__UpperCAmelCase=True							,			__UpperCAmelCase=True							,			__UpperCAmelCase=32							,			__UpperCAmelCase=4							,			__UpperCAmelCase=4							,			__UpperCAmelCase=37							,			__UpperCAmelCase="gelu"							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=10							,			__UpperCAmelCase=0.02							,			__UpperCAmelCase=3							,			__UpperCAmelCase=None							,			__UpperCAmelCase=[0, 1, 2, 3]							,			):
			__A							:   int	  =				parent
			__A							:   Optional[Any]	  =				100
			__A							:   List[str]	  =				batch_size
			__A							:   Union[str, Any]	  =				image_size
			__A							:   str	  =				patch_size
			__A							:   str	  =				num_channels
			__A							:   str	  =				is_training
			__A							:   str	  =				use_labels
			__A							:   Any	  =				hidden_size
			__A							:   Tuple	  =				num_hidden_layers
			__A							:   Any	  =				num_attention_heads
			__A							:   List[str]	  =				intermediate_size
			__A							:   Any	  =				hidden_act
			__A							:   List[str]	  =				hidden_dropout_prob
			__A							:   Any	  =				attention_probs_dropout_prob
			__A							:   Tuple	  =				type_sequence_label_size
			__A							:   Optional[Any]	  =				initializer_range
			__A							:   Tuple	  =				scope
			__A							:   Dict	  =				out_indices
			__A							:   str	  =				num_labels

			# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
			__A							:   int	  =				(image_size // patch_size) ** 2
			__A							:   Dict	  =				num_patches + 1

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

			__A							:   Tuple	  =				None
			__A							:   List[str]	  =				None
			if self.use_labels:
					__A							:   Optional[int]	  =				ids_tensor([self.batch_size]							,			self.type_sequence_label_size   )
					__A							:   Optional[Any]	  =				ids_tensor([self.batch_size, self.image_size, self.image_size]							,			self.num_labels   )

			__A							:   List[Any]	  =				self.get_config()

			return config, pixel_values, labels, pixel_labels

	def 					__UpperCAmelCase(       self   ):
			return DataaVecVisionConfig(
			    vocab_size=self.vocab_size							,			image_size=self.image_size							,			patch_size=self.patch_size							,			num_channels=self.num_channels							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			is_decoder=__UpperCAmelCase							,			initializer_range=self.initializer_range							,			out_indices=self.out_indices							,			)

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Dict	  =				DataaVecVisionModel(config=__UpperCAmelCase   )
			model.to(__UpperCAmelCase   )
			model.eval()
			__A							:   Any	  =				model(__UpperCAmelCase   )
			# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
			__A							:   Tuple	  =				(self.image_size // self.patch_size) ** 2
			self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, num_patches + 1, self.hidden_size)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[str]	  =				self.type_sequence_label_size
			__A							:   int	  =				DataaVecVisionForImageClassification(__UpperCAmelCase   )
			model.to(__UpperCAmelCase   )
			model.eval()
			__A							:   int	  =				model(__UpperCAmelCase							,			labels=__UpperCAmelCase   )
			self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				self.num_labels
			__A							:   Any	  =				DataaVecVisionForSemanticSegmentation(__UpperCAmelCase   )
			model.to(__UpperCAmelCase   )
			model.eval()
			__A							:   List[Any]	  =				model(__UpperCAmelCase   )
			self.parent.assertEqual(
			    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)   )
			__A							:   Any	  =				model(__UpperCAmelCase							,			labels=__UpperCAmelCase   )
			self.parent.assertEqual(
			    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)   )







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


@require_torch
class _a       (	lowerCAmelCase__   ,							lowerCAmelCase__   ,							unittest.TestCase ):

	'''simple docstring'''



	lowerCamelCase_   :      Optional[int]       =	(
	    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase_   :      Dict       =	(
	    {
	        """feature-extraction""": DataaVecVisionModel,
	        """image-classification""": DataaVecVisionForImageClassification,
	        """image-segmentation""": DataaVecVisionForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowerCamelCase_   :      Dict       =	False
	lowerCamelCase_   :      Tuple       =	False
	lowerCamelCase_   :      str       =	False

	def 					__UpperCAmelCase(       self   ):
			__A							:   Dict	  =				DataaVecVisionModelTester(self   )
			__A							:   Tuple	  =				ConfigTester(
			    self							,			config_class=__UpperCAmelCase							,			has_text_modality=__UpperCAmelCase							,			hidden_size=37   )

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

	def 					__UpperCAmelCase(       self   ):
			# Data2VecVision does not use inputs_embeds
			pass

	@require_torch_multi_gpu
	@unittest.skip(
	    reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"   )
	def 					__UpperCAmelCase(       self   ):
			pass

	def 					__UpperCAmelCase(       self   ):
			__A							:   Union[str, Any]	  =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A							:   Optional[Any]	  =				model_class(__UpperCAmelCase   )
					self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)   )
					__A							:   Tuple	  =				model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__UpperCAmelCase							,			nn.Linear   )   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Dict	  =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A							:   Dict	  =				model_class(__UpperCAmelCase   )
					__A							:   Any	  =				inspect.signature(model.forward   )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					__A							:   Tuple	  =				[*signature.parameters.keys()]

					__A							:   str	  =				["pixel_values"]
					self.assertListEqual(arg_names[:1]							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   str	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[Any]	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			if not self.model_tester.is_training:
					return

			__A							:   Union[str, Any]	  =				self.model_tester.prepare_config_and_inputs_for_common()
			__A							:   Optional[int]	  =				True

			for model_class in self.all_model_classes:
					if model_class in [*get_values(__UpperCAmelCase   )]:
							continue

					__A							:   Any	  =				model_class(__UpperCAmelCase   )
					model.to(__UpperCAmelCase   )
					model.train()
					__A							:   Optional[int]	  =				self._prepare_for_class(__UpperCAmelCase							,			__UpperCAmelCase							,			return_labels=__UpperCAmelCase   )
					__A							:   List[str]	  =				model(**__UpperCAmelCase   ).loss
					loss.backward()

	def 					__UpperCAmelCase(       self   ):
			__A							:   Union[str, Any]	  =				self.model_tester.prepare_config_and_inputs_for_common()
			if not self.model_tester.is_training:
					return

			__A							:   Union[str, Any]	  =				False
			__A							:   List[Any]	  =				True

			for model_class in self.all_model_classes:
					if model_class in [*get_values(__UpperCAmelCase   )] or not model_class.supports_gradient_checkpointing:
							continue
					# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
					# this can then be incorporated into _prepare_for_class in test_modeling_common.py
					elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
							__A							:   List[str]	  =				inputs_dict["pixel_values"].shape
							__A							:   List[str]	  =				torch.zeros(
							    [self.model_tester.batch_size, height, width]							,			device=__UpperCAmelCase   ).long()
					__A							:   Any	  =				model_class(__UpperCAmelCase   )
					model.gradient_checkpointing_enable()
					model.to(__UpperCAmelCase   )
					model.train()
					__A							:   Any	  =				self._prepare_for_class(__UpperCAmelCase							,			__UpperCAmelCase							,			return_labels=__UpperCAmelCase   )
					__A							:   Any	  =				model(**__UpperCAmelCase   ).loss
					loss.backward()

	def 					__UpperCAmelCase(       self   ):
			__A							:   Tuple	  =				self.model_tester.prepare_config_and_inputs_for_common()

			__A							:   Optional[Any]	  =				_config_zero_init(__UpperCAmelCase   )
			for model_class in self.all_model_classes:
					__A							:   Optional[int]	  =				model_class(config=__UpperCAmelCase   )
					for name, param in model.named_parameters():
							# we skip lambda parameters as these require special initial values
							# determined by config.layer_scale_init_value
							if "lambda" in name:
									continue
							if param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9).round() / 1e9).item()							,			[0.0, 1.0]							,			msg=F"Parameter {name} of model {model_class} seems not properly initialized"							,			)

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase=2e-4							,			__UpperCAmelCase="outputs"							,			__UpperCAmelCase=None   ):
			# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
			super().check_pt_tf_outputs(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase   )







	@slow
	def 					__UpperCAmelCase(       self   ):
			for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					__A							:   List[Any]	  =				DataaVecVisionModel.from_pretrained(__UpperCAmelCase   )
					self.assertIsNotNone(__UpperCAmelCase   )



def    lowerCamelCase_   (  )							->	Optional[int]:
		__A							:   Optional[int]	  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image





@require_torch
@require_vision
class _a       (	unittest.TestCase ):

	'''simple docstring'''



	@cached_property
	def 					__UpperCAmelCase(       self   ):
			return (
			    BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"   ) if is_vision_available() else None
			)







	@slow
	def 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"   ).to(
			    __UpperCAmelCase   )

			__A							:   List[Any]	  =				self.default_image_processor
			__A							:   List[Any]	  =				prepare_img()
			__A							:   Tuple	  =				image_processor(images=__UpperCAmelCase							,			return_tensors="pt"   ).to(__UpperCAmelCase   )

			# forward pass
			with torch.no_grad():
					__A							:   Any	  =				model(**__UpperCAmelCase   )
			__A							:   List[str]	  =				outputs.logits

			# verify the logits
			__A							:   Optional[int]	  =				torch.Size((1, 1_000)   )
			self.assertEqual(logits.shape							,			__UpperCAmelCase   )

			__A							:   Union[str, Any]	  =				torch.tensor([0.32_77, -0.13_95, 0.09_11]   ).to(__UpperCAmelCase   )

			self.assertTrue(torch.allclose(logits[0, :3]							,			__UpperCAmelCase							,			atol=1e-4   )   )

			__A							:   Dict	  =				[model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
			self.assertEqual(logits[0].topk(2   ).indices.cpu().tolist()							,			__UpperCAmelCase   )


from typing import Any

import numpy as np


class _a       :

	'''simple docstring'''



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

			__A							:   Any	  =				tableau
			__A  ,       __A							:   Dict	  =				tableau.shape

			# Number of decision variables x1, x2, x3...
			__A							:   Any	  =				n_vars

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

			# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
			__A							:   List[Any]	  =				(self.n_art_vars > 0) + 1

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

			# Objectives for each stage
			__A							:   List[Any]	  =				["max"]

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

			__A							:   Tuple	  =				[""]

			# Index of current pivot row and column
			__A							:   Tuple	  =				None
			__A							:   List[str]	  =				None

			# Does objective row only contain (non)-negative values?
			__A							:   Optional[int]	  =				False

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

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

			# decision | slack | artificial
			__A							:   List[Any]	  =				["x", "s", "a"]
			__A							:   List[str]	  =				[]
			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							,			__UpperCAmelCase   ):
			__A							:   Optional[Any]	  =				self.objectives[-1]

			# Find entries of highest magnitude in objective rows
			__A							:   Optional[int]	  =				(objective == "min") - (objective == "max")
			__A							:   Any	  =				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							:   Any	  =				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							:   Optional[int]	  =				slice(self.n_stages							,			self.n_rows   )

			# RHS
			__A							:   Optional[Any]	  =				tableau[s, -1]

			# Elements of pivot column within slice
			__A							:   List[Any]	  =				tableau[s, col_idx]

			# Array filled with nans
			__A							:   Union[str, Any]	  =				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							:   int	  =				np.divide(__UpperCAmelCase							,			__UpperCAmelCase							,			out=__UpperCAmelCase							,			where=divisor > 0   )

			# Arg of minimum quotient excluding the nan values. n_stages is added
			# to compensate for earlier exclusion of objective columns
			__A							:   Optional[Any]	  =				np.nanargmin(__UpperCAmelCase   ) + self.n_stages
			return row_idx, col_idx

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				tableau[row_idx].copy()

			__A							:   Tuple	  =				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							:   str	  =				piv_row
			return tableau

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			self.objectives.pop()

			if not self.objectives:
					return tableau

			# Slice containing ids for artificial columns
			__A							:   List[Any]	  =				slice(-self.n_art_vars - 1							,			-1   )

			# Delete the artificial variable columns
			__A							:   Tuple	  =				np.delete(__UpperCAmelCase							,			__UpperCAmelCase							,			axis=1   )

			# Delete the objective row of the first stage
			__A							:   List[str]	  =				np.delete(__UpperCAmelCase							,			0							,			axis=0   )

			__A							:   Union[str, Any]	  =				1
			self.n_rows -= 1
			__A							:   str	  =				0
			__A							:   str	  =				False
			return tableau

	def 					__UpperCAmelCase(       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							:   Optional[Any]	  =				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							:   Dict	  =				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							:   int	  =				self.change_stage(self.tableau   )
					else:
							__A							:   Dict	  =				self.pivot(self.tableau							,			__UpperCAmelCase							,			__UpperCAmelCase   )
			return {}







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				{"P": abs(tableau[0, -1]   )}

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

					# First entry in the nonzero ids
					__A							:   List[Any]	  =				nonzero[0][0]
					__A							:   Tuple	  =				tableau[nonzero_rowidx, i]

					# If there is only one nonzero value in column, which is one
					if n_nonzero == nonzero_val == 1:
							__A							:   Union[str, Any]	  =				tableau[nonzero_rowidx, -1]
							__A							:   Optional[int]	  =				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(__UpperCAmelCase							,			0   )
			return output_dict


if __name__ == "__main__":
					import doctest

					doctest.testmod()





