



'''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 						__snake_case(				_lowerCAmelCase		)		-> Union[str, Any]:
      if isinstance(_lowerCAmelCase     ,   collections.abc.Iterable		):
            return x
      return (x, x)







@require_tf
class     UpperCAmelCase_       :
    """simple docstring"""







    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     str ,     snake_case_					:     List[Any]						):
          pass




    def lowerCamelCase       (    self					:     Optional[int]						):
          pass




    def lowerCamelCase       (    self					:     Optional[Any]						):
          pass




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     Tuple ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     List[str] ,     snake_case_					:     Optional[Any]=None ,     **snake_case_					:     List[Any]						):
          snake_case__  :    Tuple    						=      VisionTextDualEncoderConfig.from_vision_text_configs(snake_case_ ,     snake_case_						)

          snake_case__  :    Optional[Any]    						=      TFVisionTextDualEncoderModel(snake_case_						)

          snake_case__  :    Tuple    						=      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 lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     Dict ,     snake_case_					:     Any ,     snake_case_					:     Optional[int] ,     snake_case_					:     Tuple ,     snake_case_					:     List[Any]=None ,     **snake_case_					:     Union[str, Any]						):
          snake_case__						,				snake_case__  :    List[str]    						=      self.get_vision_text_model(snake_case_ ,     snake_case_						)
          snake_case__  :    Union[str, Any]    						=      TFVisionTextDualEncoderModel(vision_model=snake_case_ ,     text_model=snake_case_						)

          snake_case__  :    int    						=      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 lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Dict ,     snake_case_					:     str=None ,     **snake_case_					:     Union[str, Any]						):
          snake_case__						,				snake_case__  :    Dict    						=      self.get_vision_text_model(snake_case_ ,     snake_case_						)
          snake_case__  :    Dict    						=      {"""vision_model""": vision_model, """text_model""": text_model}
          snake_case__  :    Optional[int]    						=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case_						)

          snake_case__  :    Dict    						=      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 lowerCamelCase       (    self					:     Any ,     snake_case_					:     Optional[int] ,     snake_case_					:     Dict ,     snake_case_					:     Any ,     snake_case_					:     Optional[int] ,     snake_case_					:     int=None ,     **snake_case_					:     str						):
          snake_case__						,				snake_case__  :    Union[str, Any]    						=      self.get_vision_text_model(snake_case_ ,     snake_case_						)
          snake_case__  :    Any    						=      TFVisionTextDualEncoderModel(vision_model=snake_case_ ,     text_model=snake_case_						)

          snake_case__  :    int    						=      model(input_ids=snake_case_ ,     pixel_values=snake_case_ ,     attention_mask=snake_case_						)
          snake_case__  :    int    						=      output[0].numpy()

          with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(snake_case_						)
                snake_case__  :    List[str]    						=      TFVisionTextDualEncoderModel.from_pretrained(snake_case_						)

                snake_case__  :    Dict    						=      model(input_ids=snake_case_ ,     pixel_values=snake_case_ ,     attention_mask=snake_case_						)
                snake_case__  :    Tuple    						=      after_output[0].numpy()
                snake_case__  :    int    						=      np.amax(np.abs(out_a - out_a						)						)
                self.assertLessEqual(snake_case_ ,     1E-5						)




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     List[Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Tuple ,     snake_case_					:     Optional[Any] ,     snake_case_					:     List[str]=None ,     **snake_case_					:     List[str]						):
          snake_case__						,				snake_case__  :    Optional[int]    						=      self.get_vision_text_model(snake_case_ ,     snake_case_						)
          snake_case__  :    Optional[int]    						=      TFVisionTextDualEncoderModel(vision_model=snake_case_ ,     text_model=snake_case_						)

          snake_case__  :    int    						=      model(
              input_ids=snake_case_ ,     pixel_values=snake_case_ ,     attention_mask=snake_case_ ,     output_attentions=snake_case_						)

          snake_case__  :    List[Any]    						=      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)
          snake_case__  :    Optional[Any]    						=      to_atuple(vision_model.config.image_size						)
          snake_case__  :    str    						=      to_atuple(vision_model.config.patch_size						)
          snake_case__  :    Any    						=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          snake_case__  :    Union[str, Any]    						=      num_patches + 1
          self.assertEqual(vision_attentions[0].shape[-3:] ,     (vision_config.num_attention_heads, seq_len, seq_len)						)

          snake_case__  :    Any    						=      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 lowerCamelCase       (    self					:     str ,     snake_case_					:     np.ndarray ,     snake_case_					:     np.ndarray ,     snake_case_					:     float						):
          snake_case__  :    List[Any]    						=      np.abs((a - b)						).max()
          self.assertLessEqual(snake_case_ ,     snake_case_ ,     f"Difference between torch and flax is {diff} (>= {tol})."						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    int    						=      self.prepare_config_and_inputs()
          self.check_vision_text_dual_encoder_model(**snake_case_						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Union[str, Any]    						=      self.prepare_config_and_inputs()
          self.check_model_from_pretrained_configs(**snake_case_						)




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    int    						=      self.prepare_config_and_inputs()
          self.check_vision_text_dual_encoder_from_pretrained(**snake_case_						)




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Optional[int]    						=      self.prepare_config_and_inputs()
          self.check_save_load(**snake_case_						)




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Optional[int]    						=      self.prepare_config_and_inputs()
          self.check_vision_text_output_attention(**snake_case_						)




    @slow
    def lowerCamelCase       (    self					:     str						):
          snake_case__						,				snake_case__  :    Any    						=      self.get_pretrained_model_and_inputs()

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

          with tempfile.TemporaryDirectory() as tmp_dirname:
                model_a.save_pretrained(snake_case_						)
                snake_case__  :    Optional[Any]    						=      TFVisionTextDualEncoderModel.from_pretrained(snake_case_						)

                snake_case__  :    int    						=      model_a(**snake_case_						)
                snake_case__  :    Dict    						=      after_outputs[0].numpy()
                snake_case__  :    Optional[Any]    						=      np.amax(np.abs(out_a - out_a						)						)
                self.assertLessEqual(snake_case_ ,     1E-5						)







@require_tf
class     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







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

          return model, inputs




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Tuple ,     snake_case_					:     Optional[int]						):
          snake_case__  :    Union[str, Any]    						=      TFViTModel(snake_case_ ,     name="""vision_model"""						)
          snake_case__  :    Any    						=      TFBertModel(snake_case_ ,     name="""text_model"""						)
          return vision_model, text_model




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    Union[str, Any]    						=      TFViTModelTester(self						)
          snake_case__  :    str    						=      TFBertModelTester(self						)
          snake_case__  :    Union[str, Any]    						=      vit_model_tester.prepare_config_and_inputs()
          snake_case__  :    Any    						=      bert_model_tester.prepare_config_and_inputs()

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

          (
              (
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				
          )  :    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     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







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

          return model, inputs




    def lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     Dict ,     snake_case_					:     List[Any] ,     snake_case_					:     int ,     snake_case_					:     Dict ,     snake_case_					:     Optional[int]=None ,     **snake_case_					:     Optional[Any]						):
          snake_case__						,				snake_case__  :    Any    						=      self.get_vision_text_model(snake_case_ ,     snake_case_						)
          snake_case__  :    Tuple    						=      TFVisionTextDualEncoderModel(vision_model=snake_case_ ,     text_model=snake_case_						)

          snake_case__  :    Union[str, Any]    						=      model(
              input_ids=snake_case_ ,     pixel_values=snake_case_ ,     attention_mask=snake_case_ ,     output_attentions=snake_case_						)

          snake_case__  :    str    						=      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)
          snake_case__  :    Tuple    						=      to_atuple(vision_model.config.image_size						)
          snake_case__  :    List[Any]    						=      to_atuple(vision_model.config.patch_size						)
          snake_case__  :    Tuple    						=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          snake_case__  :    Dict    						=      num_patches + 2
          self.assertEqual(vision_attentions[0].shape[-3:] ,     (vision_config.num_attention_heads, seq_len, seq_len)						)

          snake_case__  :    Optional[Any]    						=      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 lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     Optional[int] ,     snake_case_					:     Optional[int]						):
          snake_case__  :    Union[str, Any]    						=      TFDeiTModel(snake_case_ ,     name="""vision_model"""						)
          snake_case__  :    Tuple    						=      TFRobertaModel(snake_case_ ,     name="""text_model"""						)
          return vision_model, text_model




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      TFDeiTModelTester(self						)
          snake_case__  :    Union[str, Any]    						=      TFRobertaModelTester(self						)
          snake_case__  :    Optional[Any]    						=      vit_model_tester.prepare_config_and_inputs()
          snake_case__  :    str    						=      bert_model_tester.prepare_config_and_inputs()

          snake_case__						,				snake_case__						,				snake_case__  :    Any    						=      vision_config_and_inputs

          (
              (
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				
          )  :    Dict    						=      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	):
    """simple docstring"""







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

          return model, inputs




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Tuple ,     snake_case_					:     int						):
          snake_case__  :    List[str]    						=      TFCLIPVisionModel(snake_case_ ,     name="""vision_model"""						)
          snake_case__  :    Optional[Any]    						=      TFBertModel(snake_case_ ,     name="""text_model"""						)
          return vision_model, text_model




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    int    						=      TFCLIPVisionModelTester(self						)
          snake_case__  :    Optional[int]    						=      TFBertModelTester(self						)
          snake_case__  :    str    						=      clip_model_tester.prepare_config_and_inputs()
          snake_case__  :    Optional[Any]    						=      bert_model_tester.prepare_config_and_inputs()

          snake_case__						,				snake_case__  :    List[Any]    						=      vision_config_and_inputs

          (
              (
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				
          )  :    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     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @slow
    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Optional[Any]    						=      TFVisionTextDualEncoderModel.from_pretrained(
              """clip-italian/clip-italian""" ,     logit_scale_init_value=1.0 ,     from_pt=snake_case_						)
          snake_case__  :    int    						=      VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""						)

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

          snake_case__  :    int    						=      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]) ,     )

          snake_case__  :    Optional[int]    						=      np.array([[1.2284727, 0.3104122]]						)

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





'''simple docstring'''





def 						__snake_case(				_lowerCAmelCase = 1_000		)		-> int:
      return sum(e for e in range(3     ,   _lowerCAmelCase		) if e % 3 == 0 or e % 5 == 0		)


if __name__ == "__main__":
       print(F"{solution() = }")
