


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



class        A     (		__SCREAMING_SNAKE_CASE	):







     '''simple docstring'''







     def __init__(				self    :							List[str]):
      self.test()

     def 					UpperCAmelCase__       (				self    :							List[Any]):
      _lowercase:       int						=				0
      _lowercase:       Optional[int]						=				False
      while not completed:
       if counter == 1:
        self.reset()
       _lowercase:       Tuple						=				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:       Union[str, Any]						=				self.update(__lowerCamelCase)
       counter += 1

       if counter > 10_000:
        raise Exception("update() does not fulfill the constraint.")

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

     @abstractmethod
     def 					UpperCAmelCase__       (				self    :							Tuple):
      raise NotImplementedError(
          f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")

     @abstractmethod
     def 					UpperCAmelCase__       (				self    :							Tuple			,				_UpperCamelCase    :							int):
      raise NotImplementedError(
          f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")

     @abstractmethod
     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							int):
      raise NotImplementedError(
          f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")

     @abstractmethod
     def 					UpperCAmelCase__       (				self    :							int):
      raise NotImplementedError(
          f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")

     @abstractmethod
     def 					UpperCAmelCase__       (				self    :							int):
      raise NotImplementedError(
          f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")






     @abstractmethod
     def 					UpperCAmelCase__       (				self    :							Any			,				_UpperCamelCase    :							Optional[Any]=False):
      raise NotImplementedError(
          f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")






class        A     (		__SCREAMING_SNAKE_CASE	):







     '''simple docstring'''







     def __init__(				self    :							Dict			,				_UpperCamelCase    :							List[int]):
      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:       Optional[Any]						=				token_ids

      _lowercase:       Tuple						=				len(self.token_ids)
      _lowercase:       Optional[Any]						=				-1  # the index of the currently fulfilled step
      _lowercase:       Optional[int]						=				False

     def 					UpperCAmelCase__       (				self    :							str):
      if self.completed:
       return None
      return self.token_ids[self.fulfilled_idx + 1]

     def 					UpperCAmelCase__       (				self    :							int			,				_UpperCamelCase    :							int):
      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 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							int):
      if not isinstance(__lowerCamelCase			,				__lowerCamelCase):
       raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(__lowerCamelCase)}")

      _lowercase:       Union[str, Any]						=				False
      _lowercase:       Any						=				False
      _lowercase:       Dict						=				False

      if self.does_advance(__lowerCamelCase):
       self.fulfilled_idx += 1
       _lowercase:       str						=				True
       if self.fulfilled_idx == (self.seqlen - 1):
        _lowercase:       List[Any]						=				True
       _lowercase:       Dict						=				completed
      else:
       # failed to make progress.
       _lowercase:       Any						=				True
       self.reset()
      return stepped, completed, reset

     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       List[Any]						=				False
      _lowercase:       Union[str, Any]						=				0

     def 					UpperCAmelCase__       (				self    :							Dict):
      return self.seqlen - (self.fulfilled_idx + 1)






     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							str=False):
      _lowercase:       Dict						=				PhrasalConstraint(self.token_ids)

      if stateful:
       _lowercase:       List[Any]						=				self.seqlen
       _lowercase:       List[str]						=				self.fulfilled_idx
       _lowercase:       List[str]						=				self.completed

      return new_constraint






class        A     :







     '''simple docstring'''







     def __init__(				self    :							Optional[Any]			,				_UpperCamelCase    :							List[List[int]]			,				_UpperCamelCase    :							Optional[Any]=True):
      _lowercase:       List[Any]						=				max([len(__lowerCamelCase) for one in nested_token_ids])

      _lowercase:       Dict						=				{}
      for token_ids in nested_token_ids:
       _lowercase:       Optional[Any]						=				root
       for tidx, token_id in enumerate(__lowerCamelCase):
        if token_id not in level:
         _lowercase:       Dict						=				{}

        _lowercase:       int						=				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:       Dict						=				root

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							Dict):
      _lowercase:       Any						=				self.trie

      for current_token in current_seq:
       _lowercase:       int						=				start[current_token]

      _lowercase:       str						=				list(start.keys())

      return next_tokens

     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							Optional[Any]):
      _lowercase:       Tuple						=				self.next_tokens(__lowerCamelCase)

      return len(__lowerCamelCase) == 0

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							int):
      _lowercase:       Dict						=				list(root.values())
      if len(__lowerCamelCase) == 0:
       return 1
      else:
       return sum([self.count_leaves(__lowerCamelCase) for nn in next_nodes])






     def 					UpperCAmelCase__       (				self    :							Optional[int]			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							str):
      _lowercase:       str						=				self.count_leaves(__lowerCamelCase)
      return len(__lowerCamelCase) != leaf_count






class        A     (		__SCREAMING_SNAKE_CASE	):







     '''simple docstring'''







     def __init__(				self    :							int			,				_UpperCamelCase    :							List[List[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:       Tuple						=				DisjunctiveTrie(__lowerCamelCase)
      _lowercase:       List[Any]						=				nested_token_ids

      _lowercase:       Union[str, Any]						=				self.trie.max_height
      _lowercase:       int						=				[]
      _lowercase:       Union[str, Any]						=				False

     def 					UpperCAmelCase__       (				self    :							int):
      _lowercase:       Any						=				self.trie.next_tokens(self.current_seq)

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

     def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							int):
      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:       int						=				self.trie.next_tokens(self.current_seq)

      return token_id in next_tokens

     def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							int):
      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:       List[str]						=				False
      _lowercase:       Tuple						=				False
      _lowercase:       Tuple						=				False

      if self.does_advance(__lowerCamelCase):
       self.current_seq.append(__lowerCamelCase)
       _lowercase:       Any						=				True
      else:
       _lowercase:       Any						=				True
       self.reset()

      _lowercase:       Union[str, Any]						=				self.trie.reached_leaf(self.current_seq)
      _lowercase:       Dict						=				completed

      return stepped, completed, reset

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      _lowercase:       int						=				False
      _lowercase:       Dict						=				[]

     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      if self.completed:
       # since this can be completed without reaching max height
       return 0
      else:
       return self.seqlen - len(self.current_seq)






     def 					UpperCAmelCase__       (				self    :							Optional[int]			,				_UpperCamelCase    :							Optional[int]=False):
      _lowercase:       List[Any]						=				DisjunctiveConstraint(self.token_ids)

      if stateful:
       _lowercase:       List[str]						=				self.seqlen
       _lowercase:       Union[str, Any]						=				self.current_seq
       _lowercase:       List[str]						=				self.completed

      return new_constraint






class        A     :







     '''simple docstring'''







     def __init__(				self    :							Optional[int]			,				_UpperCamelCase    :							List[Constraint]):
      _lowercase:       str						=				constraints

      # max # of steps required to fulfill a given constraint
      _lowercase:       Optional[int]						=				max([c.seqlen for c in constraints])
      _lowercase:       List[str]						=				len(__lowerCamelCase)
      _lowercase:       Union[str, Any]						=				False

      self.init_state()

     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      _lowercase:       Any						=				[]
      _lowercase:       Any						=				None
      _lowercase:       Union[str, Any]						=				[constraint.copy(stateful=__lowerCamelCase) for constraint in self.constraints]

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      _lowercase:       Optional[Any]						=				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 					UpperCAmelCase__       (				self    :							int):
      _lowercase:       Union[str, Any]						=				[]
      if self.inprogress_constraint is None:
       for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
        _lowercase:       Tuple						=				constraint.advance()
        if isinstance(__lowerCamelCase			,				__lowerCamelCase):
         token_list.append(__lowerCamelCase)
        elif isinstance(__lowerCamelCase			,				__lowerCamelCase):
         token_list.extend(__lowerCamelCase)
      else:
       _lowercase:       Dict						=				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 					UpperCAmelCase__       (				self    :							int			,				_UpperCamelCase    :							Optional[List[int]]):
      self.init_state()

      if token_ids is not None:
       for token in token_ids:
        # completes or steps **one** constraint
        _lowercase					,	_lowercase:       Union[str, Any]						=				self.add(__lowerCamelCase)

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

     def 					UpperCAmelCase__       (				self    :							Any			,				_UpperCamelCase    :							int):
      if not isinstance(__lowerCamelCase			,				__lowerCamelCase):
       raise ValueError(f"`token_id` should be an `int`, but is `{token_id}`.")

      _lowercase					,	_lowercase:       Optional[int]						=				False, False

      if self.completed:
       _lowercase:       Optional[int]						=				True
       _lowercase:       str						=				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:       str						=				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:       List[Any]						=				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:       int						=				None

        if len(self.pending_constraints) == 0:
         # we're done!
         _lowercase:       Dict						=				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:       Dict						=				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:       Optional[int]						=				None

         if not complete and stepped:
          _lowercase:       Optional[Any]						=				pending_constraint

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

          _lowercase:       List[str]						=				(
              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:       int						=				True

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

      return complete, stepped






     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							Union[str, Any]=True):
      _lowercase:       List[Any]						=				ConstraintListState(self.constraints)  # we actually never though self.constraints objects
      # throughout this process. So it's at initialization state.

      if stateful:
       _lowercase:       Union[str, Any]						=				[
           constraint.copy(stateful=__lowerCamelCase) for constraint in self.complete_constraints
       ]
       if self.inprogress_constraint is not None:
        _lowercase:       Dict						=				self.inprogress_constraint.copy(stateful=__lowerCamelCase)
       _lowercase:       int						=				[constraint.copy() for constraint in self.pending_constraints]

      return new_state






"""simple docstring"""






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, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.config_class(**self.inputs_dict				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''tf_padding'''				)				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''depth_multiplier'''				)				)



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Tuple=1_3							,       __lowerCamelCase    :	List[Any]=3							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Dict=0.2_5							,       __lowerCamelCase    :	Optional[Any]=8							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[Any]=1_0_2_4							,       __lowerCamelCase    :	Union[str, Any]=3_2							,       __lowerCamelCase    :	int="relu6"							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	Tuple=0.0_2							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Union[str, Any]=1_0							,       __lowerCamelCase    :	Optional[Any]=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      num_channels
     _snake_case   	=      image_size
     _snake_case   	=      depth_multiplier
     _snake_case   	=      min_depth
     _snake_case   	=      tf_padding
     _snake_case   	=      int(last_hidden_size * depth_multiplier				)
     _snake_case   	=      output_stride
     _snake_case   	=      hidden_act
     _snake_case   	=      classifier_dropout_prob
     _snake_case   	=      use_labels
     _snake_case   	=      is_training
     _snake_case   	=      num_labels
     _snake_case   	=      initializer_range
     _snake_case   	=      scope



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels, pixel_labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     return MobileNetVaConfig(
         num_channels=self.num_channels							,       image_size=self.image_size							,       depth_multiplier=self.depth_multiplier							,       min_depth=self.min_depth							,       tf_padding=self.tf_padding							,       hidden_act=self.hidden_act							,       classifier_dropout_prob=self.classifier_dropout_prob							,       initializer_range=self.initializer_range							,       )



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      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,
         )							,       )



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.num_labels
     _snake_case   	=      MobileNetVaForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase							,       labels=__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)				)





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      config_and_inputs
     _snake_case   	=      {'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	List[Any]           =  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
   A__		:	Optional[int]           =  (
       {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A__		:	List[str]           =  False
   A__		:	Tuple           =  False
   A__		:	List[Any]           =  False
   A__		:	List[str]           =  False



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModelTester(self				)
     _snake_case   	=      MobileNetVaConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not output attentions'''				)
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     def check_hidden_states_output(__lowerCamelCase    :	Any							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Union[str, Any]				):
       _snake_case   	=      model_class(__lowerCamelCase				)
       model.to(__lowerCamelCase				)
       model.eval()

       with torch.no_grad():
         _snake_case   	=      model(**self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)				)

       _snake_case   	=      outputs.hidden_states

       _snake_case   	=      2_6
       self.assertEqual(len(__lowerCamelCase				)							,       __lowerCamelCase				)

     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      True
       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)

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

       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)





   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      MobileNetVaModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				int:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







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



   @cached_property
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     return (
         MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''				) if is_vision_available() else None
     )





   @slow
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''				).to(__lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				).to(__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
       _snake_case   	=      model(**__lowerCamelCase				)

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_0_0_1)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]				).to(__lowerCamelCase				)

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





