




'''simple docstring'''






import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


A_ 			=		logging.get_logger(__name__)

A_ 			=		"MMBTConfig"






class 					lowercase_			(	nn.Module       ):





     def __init__(    self    :				List[str]    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				int ):
           super().__init__()
           snake_case__					:      Any  			=						config
           snake_case__					:      Optional[Any]  			=						encoder
           snake_case__					:      Dict  			=						nn.Linear(config.modal_hidden_size    ,       config.hidden_size )
           snake_case__					:      Dict  			=						embeddings.position_embeddings
           snake_case__					:      Any  			=						embeddings.token_type_embeddings
           snake_case__					:      Optional[Any]  			=						embeddings.word_embeddings
           snake_case__					:      List[str]  			=						embeddings.LayerNorm
           snake_case__					:      Union[str, Any]  			=						nn.Dropout(p=config.hidden_dropout_prob )





     def  _lowerCAmelCase			(    self    :				Any    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				List[Any]=None    ,       __lowerCamelCase    :				int=None    ,       __lowerCamelCase    :				int=None ):
           snake_case__					:      Any  			=						self.proj_embeddings(self.encoder(__lowerCamelCase ) )
           snake_case__					:      Optional[int]  			=						token_embeddings.size(1 )

           if start_token is not None:
                 snake_case__					:      str  			=						self.word_embeddings(__lowerCamelCase )
                 seq_length += 1
                 snake_case__					:      List[str]  			=						torch.cat([start_token_embeds.unsqueeze(1 ), token_embeddings]    ,       dim=1 )

           if end_token is not None:
                 snake_case__					:      Tuple  			=						self.word_embeddings(__lowerCamelCase )
                 seq_length += 1
                 snake_case__					:      Optional[Any]  			=						torch.cat([token_embeddings, end_token_embeds.unsqueeze(1 )]    ,       dim=1 )

           if position_ids is None:
                 snake_case__					:      Any  			=						torch.arange(__lowerCamelCase    ,       dtype=torch.long    ,       device=input_modal.device )
                 snake_case__					:      str  			=						position_ids.unsqueeze(0 ).expand(input_modal.size(0 )    ,       __lowerCamelCase )

           if token_type_ids is None:
                 snake_case__					:      Optional[int]  			=						torch.zeros(
                     (input_modal.size(0 ), seq_length)    ,       dtype=torch.long    ,       device=input_modal.device )

           snake_case__					:      Any  			=						self.position_embeddings(__lowerCamelCase )
           snake_case__					:      Optional[Any]  			=						self.token_type_embeddings(__lowerCamelCase )
           snake_case__					:      Union[str, Any]  			=						token_embeddings + position_embeddings + token_type_embeddings
           snake_case__					:      Optional[int]  			=						self.LayerNorm(__lowerCamelCase )
           snake_case__					:      str  			=						self.dropout(__lowerCamelCase )
           return embeddings



A_ 			=		R"\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n"

A_ 			=		R"\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model's internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"






@add_start_docstrings(
    "The bare MMBT Model outputting raw hidden-states without any specific head on top."	,					lowerCAmelCase_	,					)
class 					lowercase_			(	nn.Module	,					lowerCAmelCase_       ):





     def __init__(    self    :				Tuple    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Any ):
           super().__init__()
           snake_case__					:      Any  			=						config
           snake_case__					:      Tuple  			=						transformer
           snake_case__					:      List[Any]  			=						ModalEmbeddings(__lowerCamelCase    ,       __lowerCamelCase    ,       transformer.embeddings )





     @add_start_docstrings_to_model_forward(__lowerCamelCase )
     @replace_return_docstrings(output_type=__lowerCamelCase    ,       config_class=_CONFIG_FOR_DOC )
     def  _lowerCAmelCase			(    self    :				List[str]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				Union[str, Any]=None    ,       __lowerCamelCase    :				Tuple=None    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				str=None    ,       __lowerCamelCase    :				int=None    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Dict=None    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				List[Any]=None    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				Dict=None    ,       __lowerCamelCase    :				Optional[Any]=None    ,       ):
           snake_case__					:      List[Any]  			=						output_attentions if output_attentions is not None else self.config.output_attentions
           snake_case__					:      List[str]  			=						(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           snake_case__					:      int  			=						return_dict if return_dict is not None else self.config.use_return_dict

           if input_ids is not None and inputs_embeds is not None:
                 raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
           elif input_ids is not None:
                 snake_case__					:      int  			=						input_ids.size()
           elif inputs_embeds is not None:
                 snake_case__					:      Any  			=						inputs_embeds.size()[:-1]
           else:
                 raise ValueError('You have to specify either input_ids or inputs_embeds' )

           snake_case__					:      Optional[int]  			=						input_ids.device if input_ids is not None else inputs_embeds.device

           snake_case__					:      str  			=						self.modal_encoder(
               __lowerCamelCase    ,       start_token=__lowerCamelCase    ,       end_token=__lowerCamelCase    ,       position_ids=__lowerCamelCase    ,       token_type_ids=__lowerCamelCase    ,       )

           snake_case__					:      str  			=						modal_embeddings.size()[:-1]

           if token_type_ids is None:
                 snake_case__					:      Any  			=						torch.ones(__lowerCamelCase    ,       dtype=torch.long    ,       device=__lowerCamelCase )

           snake_case__					:      Optional[Any]  			=						self.transformer.embeddings(
               input_ids=__lowerCamelCase    ,       position_ids=__lowerCamelCase    ,       token_type_ids=__lowerCamelCase    ,       inputs_embeds=__lowerCamelCase )

           snake_case__					:      Any  			=						torch.cat([modal_embeddings, txt_embeddings]    ,       1 )

           snake_case__					:      Tuple  			=						embedding_output.size()[:-1]

           if attention_mask is None:
                 snake_case__					:      List[str]  			=						torch.ones(__lowerCamelCase    ,       device=__lowerCamelCase )
           else:
                 snake_case__					:      Union[str, Any]  			=						torch.cat(
                     [torch.ones(__lowerCamelCase    ,       device=__lowerCamelCase    ,       dtype=torch.long ), attention_mask]    ,       dim=1 )
           if encoder_attention_mask is None:
                 snake_case__					:      str  			=						torch.ones(__lowerCamelCase    ,       device=__lowerCamelCase )
           else:
                 snake_case__					:      Union[str, Any]  			=						torch.cat(
                     [torch.ones(__lowerCamelCase    ,       device=__lowerCamelCase ), encoder_attention_mask]    ,       dim=1 )

           snake_case__					:      Dict  			=						self.get_extended_attention_mask(__lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      int  			=						self.invert_attention_mask(__lowerCamelCase )
           snake_case__					:      Optional[Any]  			=						self.get_head_mask(__lowerCamelCase    ,       self.config.num_hidden_layers )

           snake_case__					:      int  			=						self.transformer.encoder(
               __lowerCamelCase    ,       attention_mask=__lowerCamelCase    ,       head_mask=__lowerCamelCase    ,       encoder_hidden_states=__lowerCamelCase    ,       encoder_attention_mask=__lowerCamelCase    ,       output_attentions=__lowerCamelCase    ,       output_hidden_states=__lowerCamelCase    ,       return_dict=__lowerCamelCase    ,       )

           snake_case__					:      List[Any]  			=						encoder_outputs[0]
           snake_case__					:      Optional[int]  			=						self.transformer.pooler(__lowerCamelCase )

           if not return_dict:
                 return (sequence_output, pooled_output) + encoder_outputs[1:]

           return BaseModelOutputWithPooling(
               last_hidden_state=__lowerCamelCase    ,       pooler_output=__lowerCamelCase    ,       hidden_states=encoder_outputs.hidden_states    ,       attentions=encoder_outputs.attentions    ,       )





     def  _lowerCAmelCase			(    self    :				Any ):
           return self.embeddings.word_embeddings





     def  _lowerCAmelCase			(    self    :				Optional[int]    ,       __lowerCamelCase    :				Tuple ):
           snake_case__					:      Union[str, Any]  			=						value






@add_start_docstrings(
    "\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    "	,					lowerCAmelCase_	,					lowerCAmelCase_	,					)
class 					lowercase_			(	nn.Module       ):





     def __init__(    self    :				Any    ,       __lowerCamelCase    :				List[str]    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Optional[Any] ):
           super().__init__()
           snake_case__					:      Union[str, Any]  			=						config.num_labels

           snake_case__					:      List[str]  			=						MMBTModel(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      Optional[Any]  			=						nn.Dropout(config.hidden_dropout_prob )
           snake_case__					:      Union[str, Any]  			=						nn.Linear(config.hidden_size    ,       config.num_labels )





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				Union[str, Any]=None    ,       __lowerCamelCase    :				List[Any]=None    ,       __lowerCamelCase    :				Tuple=None    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				int=None    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				Union[str, Any]=None    ,       __lowerCamelCase    :				Dict=None    ,       ):
           snake_case__					:      List[str]  			=						return_dict if return_dict is not None else self.config.use_return_dict

           snake_case__					:      List[Any]  			=						self.mmbt(
               input_modal=__lowerCamelCase    ,       input_ids=__lowerCamelCase    ,       modal_start_tokens=__lowerCamelCase    ,       modal_end_tokens=__lowerCamelCase    ,       attention_mask=__lowerCamelCase    ,       token_type_ids=__lowerCamelCase    ,       modal_token_type_ids=__lowerCamelCase    ,       position_ids=__lowerCamelCase    ,       modal_position_ids=__lowerCamelCase    ,       head_mask=__lowerCamelCase    ,       inputs_embeds=__lowerCamelCase    ,       return_dict=__lowerCamelCase    ,       )

           snake_case__					:      str  			=						outputs[1]

           snake_case__					:      List[Any]  			=						self.dropout(__lowerCamelCase )
           snake_case__					:      List[str]  			=						self.classifier(__lowerCamelCase )

           snake_case__					:      Optional[Any]  			=						None
           if labels is not None:
                 if self.num_labels == 1:
                       #  We are doing regression
                       snake_case__					:      Optional[int]  			=						MSELoss()
                       snake_case__					:      Dict  			=						loss_fct(logits.view(-1 )    ,       labels.view(-1 ) )
                 else:
                       snake_case__					:      str  			=						CrossEntropyLoss()
                       snake_case__					:      Union[str, Any]  			=						loss_fct(logits.view(-1    ,       self.num_labels )    ,       labels.view(-1 ) )

           if not return_dict:
                 snake_case__					:      Optional[Any]  			=						(logits,) + outputs[2:]
                 return ((loss,) + output) if loss is not None else output

           return SequenceClassifierOutput(
               loss=__lowerCamelCase    ,       logits=__lowerCamelCase    ,       hidden_states=outputs.hidden_states    ,       attentions=outputs.attentions    ,       )






'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor






class 					lowercase_			(	unittest.TestCase       ):





     def __init__(    self    :				Optional[Any]    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Optional[Any]=7    ,       __lowerCamelCase    :				Optional[Any]=3    ,       __lowerCamelCase    :				int=18    ,       __lowerCamelCase    :				List[str]=30    ,       __lowerCamelCase    :				List[str]=400    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				List[str]=2    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				List[Any]=[0.5, 0.5, 0.5]    ,       __lowerCamelCase    :				Any=[0.5, 0.5, 0.5]    ,       ):
           snake_case__					:      Dict  			=						size if size is not None else {'shortest_edge': 30}
           snake_case__					:      Any  			=						parent
           snake_case__					:      int  			=						batch_size
           snake_case__					:      List[str]  			=						num_channels
           snake_case__					:      str  			=						image_size
           snake_case__					:      List[Any]  			=						min_resolution
           snake_case__					:      Dict  			=						max_resolution
           snake_case__					:      str  			=						do_resize
           snake_case__					:      str  			=						size
           snake_case__					:      List[str]  			=						size_divisor
           snake_case__					:      List[Any]  			=						do_normalize
           snake_case__					:      Optional[Any]  			=						image_mean
           snake_case__					:      Union[str, Any]  			=						image_std





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Union[str, Any]=False ):
           if not batched:
                 snake_case__					:      int  			=						self.size['shortest_edge']
                 snake_case__					:      Union[str, Any]  			=						image_inputs[0]
                 if isinstance(__lowerCamelCase    ,       Image.Image ):
                       snake_case__			,       snake_case__					:      Any  			=						image.size
                 else:
                       snake_case__			,       snake_case__					:      Optional[Any]  			=						image.shape[1], image.shape[2]
                 snake_case__					:      int  			=						size / min(__lowerCamelCase    ,       __lowerCamelCase )
                 if h < w:
                       snake_case__			,       snake_case__					:      Tuple  			=						size, scale * w
                 else:
                       snake_case__			,       snake_case__					:      List[str]  			=						scale * h, size

                 snake_case__					:      List[Any]  			=						int((1333 / 800) * size )
                 if max(__lowerCamelCase    ,       __lowerCamelCase ) > max_size:
                       snake_case__					:      Dict  			=						max_size / max(__lowerCamelCase    ,       __lowerCamelCase )
                       snake_case__					:      List[Any]  			=						newh * scale
                       snake_case__					:      Optional[Any]  			=						neww * scale

                 snake_case__			,       snake_case__					:      str  			=						int(newh + 0.5 ), int(neww + 0.5 )
                 snake_case__			,       snake_case__					:      Any  			=						(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

           else:
                 snake_case__					:      int  			=						[]
                 for image in image_inputs:
                       snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.get_expected_values([image] )
                       expected_values.append((expected_height, expected_width) )
                 snake_case__					:      Any  			=						max(__lowerCamelCase    ,       key=lambda __lowerCamelCase : item[0] )[0]
                 snake_case__					:      List[Any]  			=						max(__lowerCamelCase    ,       key=lambda __lowerCamelCase : item[1] )[1]

           return expected_height, expected_width






@require_torch
@require_vision
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase       ):
     A_       							= ViltImageProcessor if is_vision_available() else None





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      int  			=						ViltImageProcessingTester(self )





     @property
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           return self.image_processor_tester.prepare_image_processor_dict()





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_mean' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_std' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_normalize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_resize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size_divisor' ) )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      Optional[Any]  			=						self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 30} )

           snake_case__					:      List[str]  			=						self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42 )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 42} )





     def  _lowerCAmelCase			(    self    :				str ):
           pass





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           # Initialize image_processing
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           snake_case__					:      str  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       Image.Image )

           # Test not batched input
           snake_case__					:      int  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           # Initialize image_processing
           snake_case__					:      List[Any]  			=						self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       numpify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       np.ndarray )

           # Test not batched input
           snake_case__					:      List[Any]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Tuple  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )





     def  _lowerCAmelCase			(    self    :				str ):
           # Initialize image_processing
           snake_case__					:      str  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       torchify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       torch.Tensor )

           # Test not batched input
           snake_case__					:      Optional[int]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Optional[int]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Optional[int]  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Tuple  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )
