
from abc import ABC, abstractmethod
from typing import List, Optional





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Union[str, Any]     )  ->Optional[int]:
										# test for the above condition
										self.test()





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->List[Any]:
										lowercase         =      0
										lowercase         =      False
										while not completed:
														if counter == 1:
																		self.reset()
														lowercase         =      self.advance()
														if not self.does_advance(lowerCAmelCase__     ):
																		raise Exception(
																		    "Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."     )

														lowercase     ,	lowercase     ,	lowercase         =      self.update(lowerCAmelCase__     )
														counter += 1

														if counter > 10000:
																		raise Exception("update() does not fulfill the constraint."     )

										if self.remaining() != 0:
														raise Exception("Custom Constraint is not defined correctly."     )





						@abstractmethod
						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										raise NotImplementedError(
										    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )





						@abstractmethod
						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:int     )  ->Optional[int]:
										raise NotImplementedError(
										    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )





						@abstractmethod
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:int     )  ->Optional[Any]:
										raise NotImplementedError(
										    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )





						@abstractmethod
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										raise NotImplementedError(
										    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )





						@abstractmethod
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Tuple:
										raise NotImplementedError(
										    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )





						@abstractmethod
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Optional[Any]=False     )  ->Tuple:
										raise NotImplementedError(
										    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[int]     )  ->Optional[Any]:
										super(lowerCAmelCase__		,    self     ).__init__()

										if not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) or len(lowerCAmelCase__     ) == 0:
														raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.'''     )
										if any((not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) or token_id < 0) for token_id in token_ids     ):
														raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'''     )

										lowercase         =      token_ids

										lowercase         =      len(self.token_ids     )
										lowercase         =      -1  # the index of the currently fulfilled step
										lowercase         =      False





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										if self.completed:
														return None
										return self.token_ids[self.fulfilled_idx + 1]





						def        SCREAMING_SNAKE_CASE(			self							:int		,    lowerCAmelCase__							:int     )  ->Tuple:
										if not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(lowerCAmelCase__     )}'''     )

										if self.completed:
														return False

										return token_id == self.token_ids[self.fulfilled_idx + 1]





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:int     )  ->Tuple:
										if not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(lowerCAmelCase__     )}'''     )

										lowercase         =      False
										lowercase         =      False
										lowercase         =      False

										if self.does_advance(lowerCAmelCase__     ):
														self.fulfilled_idx += 1
														lowercase         =      True
														if self.fulfilled_idx == (self.seqlen - 1):
																		lowercase         =      True
														lowercase         =      completed
										else:
														# failed to make progress.
														lowercase         =      True
														self.reset()
										return stepped, completed, reset





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase         =      False
										lowercase         =      0





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Optional[Any]:
										return self.seqlen - (self.fulfilled_idx + 1)





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:int=False     )  ->Tuple:
										lowercase         =      PhrasalConstraint(self.token_ids     )

										if stateful:
														lowercase         =      self.seqlen
														lowercase         =      self.fulfilled_idx
														lowercase         =      self.completed

										return new_constraint





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:str		,    lowerCAmelCase__							:List[List[int]]		,    lowerCAmelCase__							:List[str]=True     )  ->Tuple:
										lowercase         =      max([len(lowerCAmelCase__     ) for one in nested_token_ids]     )

										lowercase         =      {}
										for token_ids in nested_token_ids:
														lowercase         =      root
														for tidx, token_id in enumerate(lowerCAmelCase__     ):
																		if token_id not in level:
																						lowercase         =      {}

																		lowercase         =      level[token_id]

										if no_subsets and self.has_subsets(lowerCAmelCase__		,    lowerCAmelCase__     ):
														raise ValueError(
														    "Each list in `nested_token_ids` can't be a complete subset of another list, but is"
														    F''' {nested_token_ids}.'''     )

										lowercase         =      root





						def        SCREAMING_SNAKE_CASE(			self							:int		,    lowerCAmelCase__							:str     )  ->Tuple:
										lowercase         =      self.trie

										for current_token in current_seq:
														lowercase         =      start[current_token]

										lowercase         =      list(start.keys()     )

										return next_tokens





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										lowercase         =      self.next_tokens(lowerCAmelCase__     )

										return len(lowerCAmelCase__     ) == 0





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->List[str]:
										lowercase         =      list(root.values()     )
										if len(lowerCAmelCase__     ) == 0:
														return 1
										else:
														return sum([self.count_leaves(lowerCAmelCase__     ) for nn in next_nodes]     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Optional[int]     )  ->Optional[Any]:
										lowercase         =      self.count_leaves(lowerCAmelCase__     )
										return len(lowerCAmelCase__     ) != leaf_count





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Tuple		,    lowerCAmelCase__							:List[List[int]]     )  ->Optional[int]:
										super(lowerCAmelCase__		,    self     ).__init__()

										if not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) or len(lowerCAmelCase__     ) == 0:
														raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''     )
										if any(not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) for token_ids in nested_token_ids     ):
														raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'''     )
										if any(
										    any((not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) or token_id < 0) for token_id in token_ids     )
										    for token_ids in nested_token_ids     ):
														raise ValueError(
														    F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'''     )

										lowercase         =      DisjunctiveTrie(lowerCAmelCase__     )
										lowercase         =      nested_token_ids

										lowercase         =      self.trie.max_height
										lowercase         =      []
										lowercase         =      False





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->int:
										lowercase         =      self.trie.next_tokens(self.current_seq     )

										if len(lowerCAmelCase__     ) == 0:
														return None
										else:
														return token_list





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:int     )  ->Any:
										if not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCAmelCase__     )}'''     )

										lowercase         =      self.trie.next_tokens(self.current_seq     )

										return token_id in next_tokens





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:int     )  ->Tuple:
										if not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCAmelCase__     )}'''     )

										lowercase         =      False
										lowercase         =      False
										lowercase         =      False

										if self.does_advance(lowerCAmelCase__     ):
														self.current_seq.append(lowerCAmelCase__     )
														lowercase         =      True
										else:
														lowercase         =      True
														self.reset()

										lowercase         =      self.trie.reached_leaf(self.current_seq     )
										lowercase         =      completed

										return stepped, completed, reset





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Optional[Any]:
										lowercase         =      False
										lowercase         =      []





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										if self.completed:
														# since this can be completed without reaching max height
														return 0
										else:
														return self.seqlen - len(self.current_seq     )





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[int]=False     )  ->Tuple:
										lowercase         =      DisjunctiveConstraint(self.token_ids     )

										if stateful:
														lowercase         =      self.seqlen
														lowercase         =      self.current_seq
														lowercase         =      self.completed

										return new_constraint





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[str]		,    lowerCAmelCase__							:List[Constraint]     )  ->int:
										lowercase         =      constraints

										# max # of steps required to fulfill a given constraint
										lowercase         =      max([c.seqlen for c in constraints]     )
										lowercase         =      len(lowerCAmelCase__     )
										lowercase         =      False

										self.init_state()





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Union[str, Any]:
										lowercase         =      []
										lowercase         =      None
										lowercase         =      [constraint.copy(stateful=lowerCAmelCase__     ) for constraint in self.constraints]





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										lowercase         =      0
										if self.inprogress_constraint:
														# extra points for having a constraint mid-fulfilled
														add += self.max_seqlen - self.inprogress_constraint.remaining()

										return (len(self.complete_constraints     ) * self.max_seqlen) + add





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->int:
										lowercase         =      []
										if self.inprogress_constraint is None:
														for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
																		lowercase         =      constraint.advance()
																		if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
																						token_list.append(lowerCAmelCase__     )
																		elif isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
																						token_list.extend(lowerCAmelCase__     )
										else:
														lowercase         =      self.inprogress_constraint.advance()
														if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
																		token_list.append(lowerCAmelCase__     )
														elif isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
																		token_list.extend(lowerCAmelCase__     )

										if len(lowerCAmelCase__     ) == 0:
														return None
										else:
														return token_list





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Optional[List[int]]     )  ->Union[str, Any]:
										self.init_state()

										if token_ids is not None:
														for token in token_ids:
																		# completes or steps **one** constraint
																		lowercase     ,	lowercase         =      self.add(lowerCAmelCase__     )

																		# the entire list of constraints are fulfilled
																		if self.completed:
																						break





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:int     )  ->List[str]:
										if not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.'''     )

										lowercase     ,	lowercase         =      False, False

										if self.completed:
														lowercase         =      True
														lowercase         =      False
														return complete, stepped

										if self.inprogress_constraint is not None:
														# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
														# job, simply update the state

														lowercase     ,	lowercase     ,	lowercase         =      self.inprogress_constraint.update(lowerCAmelCase__     )
														if reset:
																		# 1. If the next token breaks the progress, then we must restart.
																		#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

																		#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
																		#     constraint, not the full list of constraints.

																		self.pending_constraints.append(self.inprogress_constraint.copy(stateful=lowerCAmelCase__     )     )
																		lowercase         =      None

														if complete:
																		# 2. If the next token completes the constraint, move it to completed list, set
																		#     inprogress to None. If there are no pending constraints either, then this full list of constraints
																		#     is complete.

																		self.complete_constraints.append(self.inprogress_constraint     )
																		lowercase         =      None

																		if len(self.pending_constraints     ) == 0:
																						# we're done!
																						lowercase         =      True

										else:
														# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
														# of constraints?

														for cidx, pending_constraint in enumerate(self.pending_constraints     ):
																		if pending_constraint.does_advance(lowerCAmelCase__     ):
																						lowercase     ,	lowercase     ,	lowercase         =      pending_constraint.update(lowerCAmelCase__     )

																						if not stepped:
																										raise Exception(
																										    "`constraint.update(token_id)` is not yielding incremental progress, "
																										    "even though `constraint.does_advance(token_id)` is true."     )

																						if complete:
																										self.complete_constraints.append(lowerCAmelCase__     )
																										lowercase         =      None

																						if not complete and stepped:
																										lowercase         =      pending_constraint

																						if complete or stepped:
																										# If we made any progress at all, then it's at least not a "pending constraint".

																										lowercase         =      (
																										    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																										)

																										if len(self.pending_constraints     ) == 0 and self.inprogress_constraint is None:
																														# If there's no longer any pending after this and no inprogress either, then we must be
																														# complete.

																														lowercase         =      True

																										break  # prevent accidentally stepping through multiple constraints with just one token.

										return complete, stepped





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:List[Any]=True     )  ->List[str]:
										lowercase         =      ConstraintListState(self.constraints     )  # we actually never though self.constraints objects
										# throughout this process. So it's at initialization state.

										if stateful:
														lowercase         =      [
														    constraint.copy(stateful=lowerCAmelCase__     ) for constraint in self.complete_constraints
														]
														if self.inprogress_constraint is not None:
																		lowercase         =      self.inprogress_constraint.copy(stateful=lowerCAmelCase__     )
														lowercase         =      [constraint.copy() for constraint in self.pending_constraints]

										return new_state


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[int]:
										lowercase         =      self.config_class(**self.inputs_dict     )
										self.parent.assertTrue(hasattr(lowerCAmelCase__		,    "tf_padding"     )     )
										self.parent.assertTrue(hasattr(lowerCAmelCase__		,    "depth_multiplier"     )     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Dict		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Union[str, Any]=13		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:Optional[int]=32		,    lowerCAmelCase__							:Any=0.25		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Optional[int]=8		,    lowerCAmelCase__							:List[str]=6		,    lowerCAmelCase__							:List[Any]=32		,    lowerCAmelCase__							:Optional[Any]=True		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:Dict="relu6"		,    lowerCAmelCase__							:Tuple=1280		,    lowerCAmelCase__							:Dict=0.1		,    lowerCAmelCase__							:List[str]=0.02		,    lowerCAmelCase__							:Dict=True		,    lowerCAmelCase__							:List[Any]=True		,    lowerCAmelCase__							:Tuple=10		,    lowerCAmelCase__							:int=None		,    )  ->List[str]:
										lowercase         =      parent
										lowercase         =      batch_size
										lowercase         =      num_channels
										lowercase         =      image_size
										lowercase         =      depth_multiplier
										lowercase         =      depth_divisible_by
										lowercase         =      min_depth
										lowercase         =      expand_ratio
										lowercase         =      tf_padding
										lowercase         =      output_stride
										lowercase         =      first_layer_is_expansion
										lowercase         =      finegrained_output
										lowercase         =      hidden_act
										lowercase         =      last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier     )
										lowercase         =      classifier_dropout_prob
										lowercase         =      use_labels
										lowercase         =      is_training
										lowercase         =      num_labels
										lowercase         =      initializer_range
										lowercase         =      scope





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Dict:
										lowercase         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

										lowercase         =      self.get_config()

										return config, pixel_values, labels, pixel_labels





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->List[Any]:
										return MobileNetVaConfig(
										    num_channels=self.num_channels		,    image_size=self.image_size		,    depth_multiplier=self.depth_multiplier		,    depth_divisible_by=self.depth_divisible_by		,    min_depth=self.min_depth		,    expand_ratio=self.expand_ratio		,    output_stride=self.output_stride		,    first_layer_is_expansion=self.first_layer_is_expansion		,    finegrained_output=self.finegrained_output		,    hidden_act=self.hidden_act		,    tf_padding=self.tf_padding		,    classifier_dropout_prob=self.classifier_dropout_prob		,    initializer_range=self.initializer_range		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Tuple     )  ->Any:
										lowercase         =      MobileNetVaModel(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__     )
										self.parent.assertEqual(
										    result.last_hidden_state.shape		,    (
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )		,    )
										self.parent.assertEqual(
										    result.pooler_output.shape		,    (self.batch_size, self.last_hidden_size)		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]     )  ->Union[str, Any]:
										lowercase         =      self.num_labels
										lowercase         =      MobileNetVaForImageClassification(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__		,    labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[int]     )  ->Union[str, Any]:
										lowercase         =      self.num_labels
										lowercase         =      MobileNetVaForSemanticSegmentation(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										lowercase         =      model(lowerCAmelCase__     )
										self.parent.assertEqual(
										    result.logits.shape		,    (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )		,    )
										lowercase         =      model(lowerCAmelCase__		,    labels=lowerCAmelCase__     )
										self.parent.assertEqual(
										    result.logits.shape		,    (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										lowercase         =      self.prepare_config_and_inputs()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      config_and_inputs
										lowercase         =      {"pixel_values": pixel_values}
										return config, inputs_dict





@require_torch
class   UpperCamelCase_      (      __a		,    __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[int]				   =					(
						    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase      :  List[Any]				   =					(
						    {
						        '''feature-extraction''': MobileNetVaModel,
						        '''image-classification''': MobileNetVaForImageClassification,
						        '''image-segmentation''': MobileNetVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase      :  Union[str, Any]				   =					False
						UpperCamelCase      :  int				   =					False
						UpperCamelCase      :  str				   =					False
						UpperCamelCase      :  Dict				   =					False





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase         =      MobileNetVaModelTester(self     )
										lowercase         =      MobileNetVaConfigTester(self		,    config_class=lowerCAmelCase__		,    has_text_modality=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Any:
										self.config_tester.run_common_tests()





						@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"     )
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[int]:
										pass





						@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"     )
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										pass





						@unittest.skip(reason="MobileNetV2 does not output attentions"     )
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->str:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Dict:
										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()

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

														lowercase         =      ["pixel_values"]
														self.assertListEqual(arg_names[:1]		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Optional[int]:
										def check_hidden_states_output(lowerCAmelCase__							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:int     ):
														lowercase         =      model_class(lowerCAmelCase__     )
														model.to(lowerCAmelCase__     )
														model.eval()

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

														lowercase         =      outputs.hidden_states

														lowercase         =      16
														self.assertEqual(len(lowerCAmelCase__     )		,    lowerCAmelCase__     )

										lowercase     ,	lowercase         =      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase         =      True
														check_hidden_states_output(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

														check_hidden_states_output(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Tuple:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->str:
										lowercase         =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__     )





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->str:
										for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowercase         =      MobileNetVaModel.from_pretrained(lowerCAmelCase__     )
														self.assertIsNotNone(lowerCAmelCase__     )




def  __snake_case							(	):







				'''simple docstring'''


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





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







						'''simple docstring'''



						@cached_property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Any:
										return (
										    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"     ) if is_vision_available() else None
										)





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Union[str, Any]:
										lowercase         =      MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"     ).to(lowerCAmelCase__     )

										lowercase         =      self.default_image_processor
										lowercase         =      prepare_img()
										lowercase         =      image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     ).to(lowerCAmelCase__     )

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

										# verify the logits
										lowercase         =      torch.Size((1, 1001)     )
										self.assertEqual(outputs.logits.shape		,    lowerCAmelCase__     )

										lowercase         =      torch.tensor([0.24_45, -1.19_93, 0.19_05]     ).to(lowerCAmelCase__     )

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





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[Any]:
										lowercase         =      MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"     )
										lowercase         =      model.to(lowerCAmelCase__     )

										lowercase         =      MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"     )

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

										# forward pass
										with torch.no_grad():
														lowercase         =      model(**lowerCAmelCase__     )
										lowercase         =      outputs.logits

										# verify the logits
										lowercase         =      torch.Size((1, 21, 65, 65)     )
										self.assertEqual(logits.shape		,    lowerCAmelCase__     )

										lowercase         =      torch.tensor(
										    [
										        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
										        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
										        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
										    ]		,    device=lowerCAmelCase__		,    )

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