





'''simple docstring'''


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_SCREAMING_SNAKE_CASE	 =							{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}

class    lowerCAmelCase_		(		nn.Module		):




	def __init__(	self			,     _lowerCAmelCase		)    ->							str:
					super().__init__()
					_lowerCAmelCase				=      torchvision.models.resnetaaa(pretrained=lowercase__		)
					_lowerCAmelCase				=      list(model.children()		)[:-2]
					_lowerCAmelCase				=      nn.Sequential(*lowercase__		)
					_lowerCAmelCase				=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)





	def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
					_lowerCAmelCase				=      self.pool(self.model(lowercase__		)		)
					_lowerCAmelCase				=      torch.flatten(lowercase__			,     start_dim=2		)
					_lowerCAmelCase				=      out.transpose(1			,     2		).contiguous()
					return out  # BxNx2048







class    lowerCAmelCase_		(		a__		):




	def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							int:
					_lowerCAmelCase				=      [json.loads(lowercase__		) for l in open(lowercase__		)]
					_lowerCAmelCase				=      os.path.dirname(lowercase__		)
					_lowerCAmelCase				=      tokenizer
					_lowerCAmelCase				=      labels
					_lowerCAmelCase				=      len(lowercase__		)
					_lowerCAmelCase				=      max_seq_length

					_lowerCAmelCase				=      transforms




	def __len__(	self		)    ->							Optional[int]:
					return len(self.data		)




	def __getitem__(	self			,     _lowerCAmelCase		)    ->							Optional[int]:
					_lowerCAmelCase				=      torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]			,     add_special_tokens=lowercase__		)		)
					_lowerCAmelCase				=      sentence[0], sentence[1:-1], sentence[-1]
					_lowerCAmelCase				=      sentence[: self.max_seq_length]

					_lowerCAmelCase				=      torch.zeros(self.n_classes		)
					_lowerCAmelCase				=      1

					_lowerCAmelCase				=      Image.open(os.path.join(self.data_dir			,     self.data[index]["img"]		)		).convert("RGB"		)
					_lowerCAmelCase				=      self.transforms(lowercase__		)

					return {
					    "image_start_token": start_token,
					    "image_end_token": end_token,
					    "sentence": sentence,
					    "image": image,
					    "label": label,
					}





	def 							_snake_case   (	self		)    ->							Optional[int]:
					_lowerCAmelCase				=      Counter()
					for row in self.data:
									label_freqs.update(row["label"]		)
					return label_freqs







def 	__a(SCREAMING_SNAKE_CASE_  :					List[str]    ):



				'''simple docstring'''





				_lowerCAmelCase				=      [len(row["sentence"]    ) for row in batch]
				_lowerCAmelCase				=      len(lowerCAmelCase_    ), max(lowerCAmelCase_    )

				_lowerCAmelCase				=      torch.zeros(lowerCAmelCase_		,       lowerCAmelCase_		,       dtype=torch.long    )
				_lowerCAmelCase				=      torch.zeros(lowerCAmelCase_		,       lowerCAmelCase_		,       dtype=torch.long    )

				for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_		,       lowerCAmelCase_    )    ):
								_lowerCAmelCase				=      input_row['''sentence''']
								_lowerCAmelCase				=      1

				_lowerCAmelCase				=      torch.stack([row["image"] for row in batch]    )
				_lowerCAmelCase				=      torch.stack([row["label"] for row in batch]    )
				_lowerCAmelCase				=      torch.stack([row["image_start_token"] for row in batch]    )
				_lowerCAmelCase				=      torch.stack([row["image_end_token"] for row in batch]    )

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor


def 	__a():



				'''simple docstring'''





				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]





def 	__a():



				'''simple docstring'''





				return transforms.Compose(
				    [
				        transforms.Resize(256    ),
				        transforms.CenterCrop(224    ),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]		,       std=[0.1222_1994, 0.1214_5835, 0.1438_0469]		,       ),
				    ]    )









'''simple docstring'''


from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
       from transformers import (
           TFBertModel,
           TFCLIPVisionModel,
           TFDeiTModel,
           TFRobertaModel,
           TFVisionTextDualEncoderModel,
           TFViTModel,
           VisionTextDualEncoderConfig,
       )

if is_vision_available():
       from PIL import Image

       from transformers import VisionTextDualEncoderProcessor


def 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





    if isinstance(SCREAMING_SNAKE_CASE_		,       collections.abc.Iterable    ):
        return x
    return (x, x)

@require_tf
class    lowerCAmelCase_		:




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     pass




 def 							_snake_case   (	self		)    ->							Dict:
     pass




 def 							_snake_case   (	self		)    ->							int:
     pass




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(_lowerCAmelCase		)

     _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)

     self.assertEqual(output["text_embeds"].shape			,     (input_ids.shape[0], config.projection_dim)		)
     self.assertEqual(output["image_embeds"].shape			,     (pixel_values.shape[0], config.projection_dim)		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

     _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)

     self.assertEqual(output["text_embeds"].shape			,     (input_ids.shape[0], model.config.projection_dim)		)
     self.assertEqual(output["image_embeds"].shape			,     (pixel_values.shape[0], model.config.projection_dim)		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      {"vision_model": vision_model, "text_model": text_model}
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase		)

     _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)

     self.assertEqual(output["text_embeds"].shape			,     (input_ids.shape[0], model.config.projection_dim)		)
     self.assertEqual(output["image_embeds"].shape			,     (pixel_values.shape[0], model.config.projection_dim)		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Optional[int]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

     _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)
     _lowerCAmelCase				=      output[0].numpy()

     with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase		)

         _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)
         _lowerCAmelCase				=      after_output[0].numpy()
         _lowerCAmelCase				=      np.amax(np.abs(out_a - out_a		)		)
         self.assertLessEqual(_lowerCAmelCase			,     1E-5		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Dict:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

     _lowerCAmelCase				=      model(
         input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase			,     output_attentions=_lowerCAmelCase		)

     _lowerCAmelCase				=      output.vision_model_output.attentions
     self.assertEqual(len(_lowerCAmelCase		)			,     vision_config.num_hidden_layers		)

     # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
     _lowerCAmelCase				=      to_atuple(vision_model.config.image_size		)
     _lowerCAmelCase				=      to_atuple(vision_model.config.patch_size		)
     _lowerCAmelCase				=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     _lowerCAmelCase				=      num_patches + 1
     self.assertEqual(vision_attentions[0].shape[-3:]			,     (vision_config.num_attention_heads, seq_len, seq_len)		)

     _lowerCAmelCase				=      output.text_model_output.attentions
     self.assertEqual(len(_lowerCAmelCase		)			,     text_config.num_hidden_layers		)

     self.assertEqual(
         text_attentions[0].shape[-3:]			,     (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[int]:
     _lowerCAmelCase				=      np.abs((a - b)		).max()
     self.assertLessEqual(_lowerCAmelCase			,     _lowerCAmelCase			,     f'''Difference between torch and flax is {diff} (>= {tol}).'''		)




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_model(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_model_from_pretrained_configs(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_save_load(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_vision_text_output_attention(**_lowerCAmelCase		)





 @slow
 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_pretrained_model_and_inputs()

     _lowerCAmelCase				=      model_a(**_lowerCAmelCase		)
     _lowerCAmelCase				=      outputs[0].numpy()

     with tempfile.TemporaryDirectory() as tmp_dirname:
         model_a.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase		)

         _lowerCAmelCase				=      model_a(**_lowerCAmelCase		)
         _lowerCAmelCase				=      after_outputs[0].numpy()
         _lowerCAmelCase				=      np.amax(np.abs(out_a - out_a		)		)
         self.assertLessEqual(_lowerCAmelCase			,     1E-5		)







@require_tf
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         "hf-internal-testing/tiny-random-vit"			,     "hf-internal-testing/tiny-random-bert"		)
     _lowerCAmelCase				=      13
     _lowerCAmelCase				=      floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]		)
     _lowerCAmelCase				=      ids_tensor([batch_size, 4]			,     model.text_model.config.vocab_size		)
     _lowerCAmelCase				=      random_attention_mask([batch_size, 4]		)
     _lowerCAmelCase				=      {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

     return model, inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      TFViTModel(_lowerCAmelCase			,     name="vision_model"		)
     _lowerCAmelCase				=      TFBertModel(_lowerCAmelCase			,     name="text_model"		)
     return vision_model, text_model





 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      TFViTModelTester(self		)
     _lowerCAmelCase				=      TFBertModelTester(self		)
     _lowerCAmelCase				=      vit_model_tester.prepare_config_and_inputs()
     _lowerCAmelCase				=      bert_model_tester.prepare_config_and_inputs()

     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      vision_config_and_inputs

     (
         (
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			
     )				=      text_config_and_inputs

     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": input_mask,
         "input_ids": input_ids,
         "text_token_type_ids": token_type_ids,
         "text_sequence_labels": sequence_labels,
         "text_token_labels": token_labels,
         "text_choice_labels": choice_labels,
     }







@require_tf
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							List[Any]:
     # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
     # just reinitialize it.
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         "Rocketknight1/tiny-random-deit-tf"			,     "hf-internal-testing/tiny-random-roberta"		)
     _lowerCAmelCase				=      13
     _lowerCAmelCase				=      floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]		)
     _lowerCAmelCase				=      ids_tensor([batch_size, 4]			,     model.text_model.config.vocab_size		)
     _lowerCAmelCase				=      random_attention_mask([batch_size, 4]		)
     _lowerCAmelCase				=      {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

     return model, inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							int:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

     _lowerCAmelCase				=      model(
         input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase			,     output_attentions=_lowerCAmelCase		)

     _lowerCAmelCase				=      output.vision_model_output.attentions
     self.assertEqual(len(_lowerCAmelCase		)			,     vision_config.num_hidden_layers		)

     # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
     _lowerCAmelCase				=      to_atuple(vision_model.config.image_size		)
     _lowerCAmelCase				=      to_atuple(vision_model.config.patch_size		)
     _lowerCAmelCase				=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     _lowerCAmelCase				=      num_patches + 2
     self.assertEqual(vision_attentions[0].shape[-3:]			,     (vision_config.num_attention_heads, seq_len, seq_len)		)

     _lowerCAmelCase				=      output.text_model_output.attentions
     self.assertEqual(len(_lowerCAmelCase		)			,     text_config.num_hidden_layers		)

     self.assertEqual(
         text_attentions[0].shape[-3:]			,     (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      TFDeiTModel(_lowerCAmelCase			,     name="vision_model"		)
     _lowerCAmelCase				=      TFRobertaModel(_lowerCAmelCase			,     name="text_model"		)
     return vision_model, text_model





 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      TFDeiTModelTester(self		)
     _lowerCAmelCase				=      TFRobertaModelTester(self		)
     _lowerCAmelCase				=      vit_model_tester.prepare_config_and_inputs()
     _lowerCAmelCase				=      bert_model_tester.prepare_config_and_inputs()

     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      vision_config_and_inputs

     (
         (
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			
     )				=      text_config_and_inputs

     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": input_mask,
         "input_ids": input_ids,
         "text_token_type_ids": token_type_ids,
         "text_sequence_labels": sequence_labels,
         "text_token_labels": token_labels,
         "text_choice_labels": choice_labels,
     }







@require_tf
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         "Rocketknight1/tiny-random-clip-tf"			,     "hf-internal-testing/tiny-random-bert"		)
     _lowerCAmelCase				=      13
     _lowerCAmelCase				=      floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]		)
     _lowerCAmelCase				=      ids_tensor([batch_size, 4]			,     model.text_model.config.vocab_size		)
     _lowerCAmelCase				=      random_attention_mask([batch_size, 4]		)
     _lowerCAmelCase				=      {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

     return model, inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							int:
     _lowerCAmelCase				=      TFCLIPVisionModel(_lowerCAmelCase			,     name="vision_model"		)
     _lowerCAmelCase				=      TFBertModel(_lowerCAmelCase			,     name="text_model"		)
     return vision_model, text_model





 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      TFCLIPVisionModelTester(self		)
     _lowerCAmelCase				=      TFBertModelTester(self		)
     _lowerCAmelCase				=      clip_model_tester.prepare_config_and_inputs()
     _lowerCAmelCase				=      bert_model_tester.prepare_config_and_inputs()

     _lowerCAmelCase			,			_lowerCAmelCase				=      vision_config_and_inputs

     (
         (
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			
     )				=      text_config_and_inputs

     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": input_mask,
         "input_ids": input_ids,
         "text_token_type_ids": token_type_ids,
         "text_sequence_labels": sequence_labels,
         "text_token_labels": token_labels,
         "text_choice_labels": choice_labels,
     }







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




 @slow
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_pretrained(
         "clip-italian/clip-italian"			,     logit_scale_init_value=1.0			,     from_pt=_lowerCAmelCase		)
     _lowerCAmelCase				=      VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"		)

     _lowerCAmelCase				=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
     _lowerCAmelCase				=      processor(
         text=["una foto di un gatto", "una foto di un cane"]			,     images=_lowerCAmelCase			,     padding=_lowerCAmelCase			,     return_tensors="np"		)

     _lowerCAmelCase				=      model(**_lowerCAmelCase		)

     # verify the logits
     self.assertEqual(outputs.logits_per_image.shape			,     (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
     self.assertEqual(
         outputs.logits_per_text.shape			,     (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])			,     )

     _lowerCAmelCase				=      np.array([[1.2284727, 0.3104122]]		)

     self.assertTrue(np.allclose(outputs.logits_per_image.numpy()			,     _lowerCAmelCase			,     atol=1E-3		)		)


