



'''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  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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







@require_tf
class 	UpperCAmelCase	:


 '''simple docstring'''




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      pass




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      pass




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



      """simple docstring"""







      pass




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      a_       		=VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=TFVisionTextDualEncoderModel(lowerCAmelCase_)

      a_       		=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							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

      a_       		=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							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



      """simple docstring"""







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

      a_       		=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							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

      a_       		=model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)
      a_       		=output[0].numpy()

      with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(lowerCAmelCase_)
           a_       		=TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_)

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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

      a_       		=model(
          input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  output_attentions=lowerCAmelCase_)

      a_       		=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)
      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(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							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Dict:



      """simple docstring"""







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




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      a_       		=self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_model(**lowerCAmelCase_)




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**lowerCAmelCase_)




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



      """simple docstring"""







      a_       		=self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase_)




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



      """simple docstring"""







      a_       		=self.prepare_config_and_inputs()
      self.check_save_load(**lowerCAmelCase_)




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		=self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**lowerCAmelCase_)




 @slow
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_		,						a_       		=self.get_pretrained_model_and_inputs()

      a_       		=model_a(**lowerCAmelCase_)
      a_       		=outputs[0].numpy()

      with tempfile.TemporaryDirectory() as tmp_dirname:
           model_a.save_pretrained(lowerCAmelCase_)
           a_       		=TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_)

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







@require_tf
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




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



      """simple docstring"""







      a_       		=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          "hf-internal-testing/tiny-random-vit"							,  "hf-internal-testing/tiny-random-bert")
      a_       		=1_3
      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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      a_       		=TFViTModel(lowerCAmelCase_							,  name="vision_model")
      a_       		=TFBertModel(lowerCAmelCase_							,  name="text_model")
      return vision_model, text_model




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



      """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 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      a_       		=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          "Rocketknight1/tiny-random-deit-tf"							,  "hf-internal-testing/tiny-random-roberta")
      a_       		=1_3
      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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      int:



      """simple docstring"""







      a_		,						a_       		=self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		=TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

      a_       		=model(
          input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  output_attentions=lowerCAmelCase_)

      a_       		=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)
      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(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							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







      a_       		=TFDeiTModel(lowerCAmelCase_							,  name="vision_model")
      a_       		=TFRobertaModel(lowerCAmelCase_							,  name="text_model")
      return vision_model, text_model




 def       lowercase_  (   self) ->      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 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




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



      """simple docstring"""







      a_       		=TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          "Rocketknight1/tiny-random-clip-tf"							,  "hf-internal-testing/tiny-random-bert")
      a_       		=1_3
      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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







      a_       		=TFCLIPVisionModel(lowerCAmelCase_							,  name="vision_model")
      a_       		=TFBertModel(lowerCAmelCase_							,  name="text_model")
      return vision_model, text_model




 def       lowercase_  (   self) ->      int:



      """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 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




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



      """simple docstring"""







      a_       		=TFVisionTextDualEncoderModel.from_pretrained(
          "clip-italian/clip-italian"							,  logit_scale_init_value=1.0							,  from_pt=lowerCAmelCase_)
      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=lowerCAmelCase_							,  padding=lowerCAmelCase_							,  return_tensors="np")

      a_       		=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])							,  )

      a_       		=np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]])

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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase							       =			{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


