






"""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       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       if isinstance(lowerCAmelCase	,      collections.abc.Iterable		):
              return x
       return (x, x)





@require_tf
class   UpperCamelCase_ :



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Optional[int]:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None ,		**snake_case__				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__ ,		snake_case__				)

         UpperCAmelCase	    =			TFVisionTextDualEncoderModel(snake_case__				)

         UpperCAmelCase	    =			model(input_ids=snake_case__ ,		pixel_values=snake_case__ ,		attention_mask=snake_case__				)

         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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None ,		**snake_case__				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_vision_text_model(snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			TFVisionTextDualEncoderModel(vision_model=snake_case__ ,		text_model=snake_case__				)

         UpperCAmelCase	    =			model(input_ids=snake_case__ ,		pixel_values=snake_case__ ,		attention_mask=snake_case__				)

         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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None ,		**snake_case__				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_vision_text_model(snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			{"""vision_model""": vision_model, """text_model""": text_model}
         UpperCAmelCase	    =			TFVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__				)

         UpperCAmelCase	    =			model(input_ids=snake_case__ ,		pixel_values=snake_case__ ,		attention_mask=snake_case__				)

         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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None ,		**snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_vision_text_model(snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			TFVisionTextDualEncoderModel(vision_model=snake_case__ ,		text_model=snake_case__				)

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

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

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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None ,		**snake_case__				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_vision_text_model(snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			TFVisionTextDualEncoderModel(vision_model=snake_case__ ,		text_model=snake_case__				)

         UpperCAmelCase	    =			model(
             input_ids=snake_case__ ,		pixel_values=snake_case__ ,		attention_mask=snake_case__ ,		output_attentions=snake_case__				)

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

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

         UpperCAmelCase	    =			output.text_model_output.attentions
         self.assertEqual(len(snake_case__				) ,		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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			np.abs((a - b)				).max()
         self.assertLessEqual(snake_case__ ,		snake_case__ ,		f'''Difference between torch and flax is {diff} (>= {tol}).'''				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_model(**snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.prepare_config_and_inputs()
         self.check_save_load(**snake_case__				)



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



         """simple docstring"""





         UpperCAmelCase	    =			self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**snake_case__				)



  @slow
  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_pretrained_model_and_inputs()

         UpperCAmelCase	    =			model_a(**snake_case__				)
         UpperCAmelCase	    =			outputs[0].numpy()

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

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





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



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





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

         return model, inputs



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			TFViTModel(snake_case__ ,		name="""vision_model"""				)
         UpperCAmelCase	    =			TFBertModel(snake_case__ ,		name="""text_model"""				)
         return vision_model, text_model



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			TFViTModelTester(self				)
         UpperCAmelCase	    =			TFBertModelTester(self				)
         UpperCAmelCase	    =			vit_model_tester.prepare_config_and_inputs()
         UpperCAmelCase	    =			bert_model_tester.prepare_config_and_inputs()

         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			vision_config_and_inputs

         (
             (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,              
         )	    =			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   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             """Rocketknight1/tiny-random-deit-tf""" ,		"""hf-internal-testing/tiny-random-roberta"""				)
         UpperCAmelCase	    =			13
         UpperCAmelCase	    =			floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ]				)
         UpperCAmelCase	    =			ids_tensor([batch_size, 4] ,		model.text_model.config.vocab_size				)
         UpperCAmelCase	    =			random_attention_mask([batch_size, 4]				)
         UpperCAmelCase	    =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

         return model, inputs



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None ,		**snake_case__				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_vision_text_model(snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			TFVisionTextDualEncoderModel(vision_model=snake_case__ ,		text_model=snake_case__				)

         UpperCAmelCase	    =			model(
             input_ids=snake_case__ ,		pixel_values=snake_case__ ,		attention_mask=snake_case__ ,		output_attentions=snake_case__				)

         UpperCAmelCase	    =			output.vision_model_output.attentions
         self.assertEqual(len(snake_case__				) ,		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)
         UpperCAmelCase	    =			to_atuple(vision_model.config.image_size				)
         UpperCAmelCase	    =			to_atuple(vision_model.config.patch_size				)
         UpperCAmelCase	    =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         UpperCAmelCase	    =			num_patches + 2
         self.assertEqual(vision_attentions[0].shape[-3:] ,		(vision_config.num_attention_heads, seq_len, seq_len)				)

         UpperCAmelCase	    =			output.text_model_output.attentions
         self.assertEqual(len(snake_case__				) ,		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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			TFDeiTModel(snake_case__ ,		name="""vision_model"""				)
         UpperCAmelCase	    =			TFRobertaModel(snake_case__ ,		name="""text_model"""				)
         return vision_model, text_model



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			TFDeiTModelTester(self				)
         UpperCAmelCase	    =			TFRobertaModelTester(self				)
         UpperCAmelCase	    =			vit_model_tester.prepare_config_and_inputs()
         UpperCAmelCase	    =			bert_model_tester.prepare_config_and_inputs()

         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			vision_config_and_inputs

         (
             (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,              
         )	    =			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   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):



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



         """simple docstring"""





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

         return model, inputs



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			TFCLIPVisionModel(snake_case__ ,		name="""vision_model"""				)
         UpperCAmelCase	    =			TFBertModel(snake_case__ ,		name="""text_model"""				)
         return vision_model, text_model



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



         """simple docstring"""





         UpperCAmelCase	    =			TFCLIPVisionModelTester(self				)
         UpperCAmelCase	    =			TFBertModelTester(self				)
         UpperCAmelCase	    =			clip_model_tester.prepare_config_and_inputs()
         UpperCAmelCase	    =			bert_model_tester.prepare_config_and_inputs()

         UpperCAmelCase    ,       UpperCAmelCase	    =			vision_config_and_inputs

         (
             (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,       (
             UpperCAmelCase       
         )    ,              
         )	    =			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   UpperCamelCase_ (       unittest.TestCase  ):



  @slow
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			TFVisionTextDualEncoderModel.from_pretrained(
             """clip-italian/clip-italian""" ,		logit_scale_init_value=1.0 ,		from_pt=snake_case__				)
         UpperCAmelCase	    =			VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""				)

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

         UpperCAmelCase	    =			model(**snake_case__				)

         # 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]) ,		)

         UpperCAmelCase	    =			np.array([[1.2_284_727, 0.3_104_122]]				)

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








"""simple docstring"""







import random
from typing import Any






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       for _ in range(len(lowerCAmelCase		)		):
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase    ,       UpperCAmelCase	    =			data[b], data[a]
       return data


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      [0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase_	:    List[str]            =      ['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
