





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_       (					lowerCAmelCase_ :     Union[str, Any]      ):



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



@require_tf
class 							_UpperCAmelCase       :

  """simple docstring"""


  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						List[Any]	)				->		Dict:
     pass






  def 							lowercase						(							self					:						Optional[Any]	)				->		List[str]:
     pass






  def 							lowercase						(							self					:						Tuple	)				->		int:
     pass






  def 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Optional[Any]=None   ,    **lowerCAmelCase_					:						Optional[int]	)				->		Any:
     __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 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						Optional[Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Optional[Any]   ,    lowerCAmelCase_					:						Tuple=None   ,    **lowerCAmelCase_					:						str	)				->		List[Any]:
     __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 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						Dict=None   ,    **lowerCAmelCase_					:						int	)				->		List[Any]:
     __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 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						Tuple   ,    lowerCAmelCase_					:						Tuple   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						Optional[Any]=None   ,    **lowerCAmelCase_					:						List[str]	)				->		Optional[Any]:
     __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 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						str=None   ,    **lowerCAmelCase_					:						Dict	)				->		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_   ,    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 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						List[str]	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      np.abs((a - b)	).max()
     self.assertLessEqual(lowerCAmelCase_   ,    lowerCAmelCase_   ,    f"""Difference between torch and flax is {diff} (>= {tol})."""	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		List[str]:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_model(**lowerCAmelCase_	)






  def 							lowercase						(							self					:						Optional[int]	)				->		List[Any]:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     self.check_model_from_pretrained_configs(**lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[Any]:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase_	)






  def 							lowercase						(							self					:						Tuple	)				->		List[Any]:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     self.check_save_load(**lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[Any]	)				->		Any:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     self.check_vision_text_output_attention(**lowerCAmelCase_	)






  @slow
  def 							lowercase						(							self					:						Any	)				->		int:
     __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 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  def 							lowercase						(							self					:						List[Any]	)				->		Optional[int]:
     __lowerCAmelCase				      =      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         'hf-internal-testing/tiny-random-vit'   ,    'hf-internal-testing/tiny-random-bert'	)
     __lowerCAmelCase				      =      1_3
     __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 							lowercase						(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[str]	)				->		str:
     __lowerCAmelCase				      =      TFViTModel(lowerCAmelCase_   ,    name='vision_model'	)
     __lowerCAmelCase				      =      TFBertModel(lowerCAmelCase_   ,    name='text_model'	)
     return vision_model, text_model






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[Any]:
     __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 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  def 							lowercase						(							self					:						str	)				->		int:
     __lowerCAmelCase				      =      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         'Rocketknight1/tiny-random-deit-tf'   ,    'hf-internal-testing/tiny-random-roberta'	)
     __lowerCAmelCase				      =      1_3
     __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 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[str]=None   ,    **lowerCAmelCase_					:						int	)				->		List[str]:
     __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 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						Union[str, Any]	)				->		Dict:
     __lowerCAmelCase				      =      TFDeiTModel(lowerCAmelCase_   ,    name='vision_model'	)
     __lowerCAmelCase				      =      TFRobertaModel(lowerCAmelCase_   ,    name='text_model'	)
     return vision_model, text_model






  def 							lowercase						(							self					:						Tuple	)				->		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 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  def 							lowercase						(							self					:						List[Any]	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         'Rocketknight1/tiny-random-clip-tf'   ,    'hf-internal-testing/tiny-random-bert'	)
     __lowerCAmelCase				      =      1_3
     __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 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						int	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      TFCLIPVisionModel(lowerCAmelCase_   ,    name='vision_model'	)
     __lowerCAmelCase				      =      TFBertModel(lowerCAmelCase_   ,    name='text_model'	)
     return vision_model, text_model






  def 							lowercase						(							self					:						Optional[Any]	)				->		List[str]:
     __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 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  @slow
  def 							lowercase						(							self					:						List[Any]	)				->		List[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([[1.2_28_47_27, 0.3_10_41_22]]	)

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






'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    1
          for i in range(0    ,      len(__A)):
               total *= numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps




def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    0
          for i in range(0    ,      len(__A)):
               total += numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



