



'''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  (__A):



     """simple docstring"""



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






@require_tf
class __A			:



 '''simple docstring'''







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


      """simple docstring"""



      pass



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


      """simple docstring"""



      pass



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


      """simple docstring"""



      pass



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A=None ,   **A       )    ->     int:


      """simple docstring"""



      _a      =  VisionTextDualEncoderConfig.from_vision_text_configs(A ,   A       )

      _a      =  TFVisionTextDualEncoderModel(A       )

      _a      =  model(input_ids=A ,   pixel_values=A ,   attention_mask=A       )

      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 					a__  (self ,   A ,   A ,   A ,   A ,   A=None ,   **A       )    ->     Dict:


      """simple docstring"""



      _a  ,    _a      =  self.get_vision_text_model(A ,   A       )
      _a      =  TFVisionTextDualEncoderModel(vision_model=A ,   text_model=A       )

      _a      =  model(input_ids=A ,   pixel_values=A ,   attention_mask=A       )

      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 					a__  (self ,   A ,   A ,   A ,   A ,   A=None ,   **A       )    ->     int:


      """simple docstring"""



      _a  ,    _a      =  self.get_vision_text_model(A ,   A       )
      _a      =  {'''vision_model''': vision_model, '''text_model''': text_model}
      _a      =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**A       )

      _a      =  model(input_ids=A ,   pixel_values=A ,   attention_mask=A       )

      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 					a__  (self ,   A ,   A ,   A ,   A ,   A=None ,   **A       )    ->     Tuple:


      """simple docstring"""



      _a  ,    _a      =  self.get_vision_text_model(A ,   A       )
      _a      =  TFVisionTextDualEncoderModel(vision_model=A ,   text_model=A       )

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

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

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



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A=None ,   **A       )    ->     int:


      """simple docstring"""



      _a  ,    _a      =  self.get_vision_text_model(A ,   A       )
      _a      =  TFVisionTextDualEncoderModel(vision_model=A ,   text_model=A       )

      _a      =  model(
          input_ids=A ,   pixel_values=A ,   attention_mask=A ,   output_attentions=A       )

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

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

      _a      =  output.text_model_output.attentions
      self.assertEqual(len(A       ) ,   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 					a__  (self ,   A ,   A ,   A       )    ->     Any:


      """simple docstring"""



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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_model(**A       )



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


      """simple docstring"""



      _a      =  self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**A       )



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**A       )



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  self.prepare_config_and_inputs()
      self.check_save_load(**A       )



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


      """simple docstring"""



      _a      =  self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**A       )



 @slow
 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a  ,    _a      =  self.get_pretrained_model_and_inputs()

      _a      =  model_a(**A       )
      _a      =  outputs[0].numpy()

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

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






@require_tf
class __A			(  A   ,   unittest.TestCase						):



 '''simple docstring'''







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


      """simple docstring"""



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

      return model, inputs



 def 					a__  (self ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  TFViTModel(A ,   name='''vision_model'''       )
      _a      =  TFBertModel(A ,   name='''text_model'''       )
      return vision_model, text_model



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


      """simple docstring"""



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

      _a  ,    _a  ,    _a      =  vision_config_and_inputs

      (
          (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    
      )      =  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 __A			(  A   ,   unittest.TestCase						):



 '''simple docstring'''







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


      """simple docstring"""



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

      return model, inputs



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A=None ,   **A       )    ->     Optional[int]:


      """simple docstring"""



      _a  ,    _a      =  self.get_vision_text_model(A ,   A       )
      _a      =  TFVisionTextDualEncoderModel(vision_model=A ,   text_model=A       )

      _a      =  model(
          input_ids=A ,   pixel_values=A ,   attention_mask=A ,   output_attentions=A       )

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

      _a      =  output.text_model_output.attentions
      self.assertEqual(len(A       ) ,   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 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      _a      =  TFDeiTModel(A ,   name='''vision_model'''       )
      _a      =  TFRobertaModel(A ,   name='''text_model'''       )
      return vision_model, text_model



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


      """simple docstring"""



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

      _a  ,    _a  ,    _a      =  vision_config_and_inputs

      (
          (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    
      )      =  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 __A			(  A   ,   unittest.TestCase						):



 '''simple docstring'''







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


      """simple docstring"""



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

      return model, inputs



 def 					a__  (self ,   A ,   A       )    ->     List[str]:


      """simple docstring"""



      _a      =  TFCLIPVisionModel(A ,   name='''vision_model'''       )
      _a      =  TFBertModel(A ,   name='''text_model'''       )
      return vision_model, text_model



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


      """simple docstring"""



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

      _a  ,    _a      =  vision_config_and_inputs

      (
          (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    
      )      =  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 __A			(  unittest.TestCase						):



 '''simple docstring'''







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


      """simple docstring"""



      _a      =  TFVisionTextDualEncoderModel.from_pretrained(
          '''clip-italian/clip-italian''' ,   logit_scale_init_value=1.0 ,   from_pt=A       )
      _a      =  VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''       )

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

      _a      =  model(**A       )

      # 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]) ,   )

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

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








'''simple docstring'''





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_		       =  {"configuration_mmbt": ["MMBTConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_		       =  ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
  from .configuration_mmbt import MMBTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
  import sys

  lowercase_		       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



