




"""simple docstring"""


import random



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      lowerCAmelCase__      :			List[Any]   =	a[left_index]
      lowerCAmelCase__      :			Union[str, Any]   =	left_index + 1
      for j in range(left_index + 1    ,		__UpperCAmelCase				):
            if a[j] < pivot:
                  lowerCAmelCase__ ,   lowerCAmelCase__      :			List[Any]   =	a[i], a[j]
                  i += 1
      lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[Any]   =	a[i - 1], a[left_index]
      return i - 1



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Any:
      if left < right:
            lowerCAmelCase__      :			Any   =	random.randint(__UpperCAmelCase    ,		right - 1				)
            lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[Any]   =	(
                a[left],
                a[pivot],
            )  # switches the pivot with the left most bound
            lowerCAmelCase__      :			Optional[Any]   =	partition(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
            quick_sort_random(
                __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)  # recursive quicksort to the left of the pivot point
            quick_sort_random(
                __UpperCAmelCase    ,		pivot_index + 1    ,		__UpperCAmelCase				)  # recursive quicksort to the right of the pivot point



def 	lowercase_				(			)						->    Dict:
      lowerCAmelCase__      :			List[str]   =	input("""Enter numbers separated by a comma:\n"""				).strip()
      lowerCAmelCase__      :			int   =	[int(__UpperCAmelCase				) for item in user_input.split(""","""				)]

      quick_sort_random(__UpperCAmelCase    ,		0    ,		len(__UpperCAmelCase				)				)

      print(__UpperCAmelCase				)


if __name__ == "__main__":
  main()











"""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 	lowercase_				(			__UpperCAmelCase				)						->    str:
      if isinstance(__UpperCAmelCase    ,		collections.abc.Iterable				):
            return x
      return (x, x)


@require_tf
class _lowerCamelCase    :






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		int   )			->						int:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[Any]:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		Any=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase   ,     UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	TFVisionTextDualEncoderModel(UpperCamelCase   )

         lowerCAmelCase__      :			List[Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )

         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 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		Any=None   ,     **UpperCamelCase       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )

         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 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		List[str]=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Dict   =	{"""vision_model""": vision_model, """text_model""": text_model}
         lowerCAmelCase__      :			Optional[int]   =	TFVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )

         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 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Tuple=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			List[Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	output[0].numpy()

         with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(UpperCamelCase   )
               lowerCAmelCase__      :			str   =	TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase   )

               lowerCAmelCase__      :			Optional[Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )
               lowerCAmelCase__      :			int   =	after_output[0].numpy()
               lowerCAmelCase__      :			Tuple   =	np.amax(np.abs(out_a - out_a   )   )
               self.assertLessEqual(UpperCamelCase   ,     1E-5   )






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		List[Any]=None   ,     **UpperCamelCase       :		Optional[int]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			int   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	model(
             input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   ,     output_attentions=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	output.vision_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     vision_config.num_hidden_layers   )

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

         lowerCAmelCase__      :			List[str]   =	output.text_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     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 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		float   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	np.abs((a - b)   ).max()
         self.assertLessEqual(UpperCamelCase   ,     UpperCamelCase   ,     f"""Difference between torch and flax is {diff} (>= {tol})."""   )






   def 	_lowerCAmelCase   (		self       :		str   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_model(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	self.prepare_config_and_inputs()
         self.check_save_load(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**UpperCamelCase   )






   @slow
   def 	_lowerCAmelCase   (		self       :		str   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			List[str]   =	self.get_pretrained_model_and_inputs()

         lowerCAmelCase__      :			Union[str, Any]   =	model_a(**UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	outputs[0].numpy()

         with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(UpperCamelCase   )
               lowerCAmelCase__      :			int   =	TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase   )

               lowerCAmelCase__      :			List[str]   =	model_a(**UpperCamelCase   )
               lowerCAmelCase__      :			Dict   =	after_outputs[0].numpy()
               lowerCAmelCase__      :			List[str]   =	np.amax(np.abs(out_a - out_a   )   )
               self.assertLessEqual(UpperCamelCase   ,     1E-5   )






@require_tf
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             """hf-internal-testing/tiny-random-vit"""   ,     """hf-internal-testing/tiny-random-bert"""   )
         lowerCAmelCase__      :			Optional[Any]   =	13
         lowerCAmelCase__      :			List[str]   =	floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ]   )
         lowerCAmelCase__      :			Dict   =	ids_tensor([batch_size, 4]   ,     model.text_model.config.vocab_size   )
         lowerCAmelCase__      :			Optional[Any]   =	random_attention_mask([batch_size, 4]   )
         lowerCAmelCase__      :			Dict   =	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

         return model, inputs






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		int   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	TFViTModel(UpperCamelCase   ,     name="""vision_model"""   )
         lowerCAmelCase__      :			Any   =	TFBertModel(UpperCamelCase   ,     name="""text_model"""   )
         return vision_model, text_model






   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	TFViTModelTester(self   )
         lowerCAmelCase__      :			str   =	TFBertModelTester(self   )
         lowerCAmelCase__      :			Optional[int]   =	vit_model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			List[Any]   =	bert_model_tester.prepare_config_and_inputs()

         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Dict   =	vision_config_and_inputs

         (
             (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   
         )      :			List[Any]   =	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    (					a_       ,	unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		int   )			->						str:

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

         return model, inputs






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		str=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			List[Any]   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[Any]   =	model(
             input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   ,     output_attentions=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	output.vision_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     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__      :			Dict   =	to_atuple(vision_model.config.image_size   )
         lowerCAmelCase__      :			Any   =	to_atuple(vision_model.config.patch_size   )
         lowerCAmelCase__      :			Dict   =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         lowerCAmelCase__      :			Optional[Any]   =	num_patches + 2
         self.assertEqual(vision_attentions[0].shape[-3:]   ,     (vision_config.num_attention_heads, seq_len, seq_len)   )

         lowerCAmelCase__      :			Union[str, Any]   =	output.text_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     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 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		str   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	TFDeiTModel(UpperCamelCase   ,     name="""vision_model"""   )
         lowerCAmelCase__      :			str   =	TFRobertaModel(UpperCamelCase   ,     name="""text_model"""   )
         return vision_model, text_model






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	TFDeiTModelTester(self   )
         lowerCAmelCase__      :			Union[str, Any]   =	TFRobertaModelTester(self   )
         lowerCAmelCase__      :			Optional[int]   =	vit_model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			Any   =	bert_model_tester.prepare_config_and_inputs()

         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[Any]   =	vision_config_and_inputs

         (
             (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   
         )      :			str   =	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    (					a_       ,	unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             """Rocketknight1/tiny-random-clip-tf"""   ,     """hf-internal-testing/tiny-random-bert"""   )
         lowerCAmelCase__      :			Any   =	13
         lowerCAmelCase__      :			List[str]   =	floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ]   )
         lowerCAmelCase__      :			Optional[Any]   =	ids_tensor([batch_size, 4]   ,     model.text_model.config.vocab_size   )
         lowerCAmelCase__      :			str   =	random_attention_mask([batch_size, 4]   )
         lowerCAmelCase__      :			List[str]   =	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

         return model, inputs






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	TFCLIPVisionModel(UpperCamelCase   ,     name="""vision_model"""   )
         lowerCAmelCase__      :			List[str]   =	TFBertModel(UpperCamelCase   ,     name="""text_model"""   )
         return vision_model, text_model






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	TFCLIPVisionModelTester(self   )
         lowerCAmelCase__      :			int   =	TFBertModelTester(self   )
         lowerCAmelCase__      :			str   =	clip_model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			Optional[int]   =	bert_model_tester.prepare_config_and_inputs()

         lowerCAmelCase__ ,   lowerCAmelCase__      :			Dict   =	vision_config_and_inputs

         (
             (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   
         )      :			str   =	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 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	TFVisionTextDualEncoderModel.from_pretrained(
             """clip-italian/clip-italian"""   ,     logit_scale_init_value=1.0   ,     from_pt=UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""   )

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

         lowerCAmelCase__      :			Tuple   =	model(**UpperCamelCase   )

         # 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__      :			List[Any]   =	np.array([[1.228_4727, 0.310_4122]]   )

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





