




"""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(__snake_case   ,collections.abc.Iterable		):
    return x
  return (x, x)


@require_tf
class        a				:


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Dict					):
      pass


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      pass


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


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  str=None   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      VisionTextDualEncoderConfig.from_vision_text_configs(_A   ,				_A					)

      _UpperCAmelCase						 =      TFVisionTextDualEncoderModel(_A					)

      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple=None   ,				**__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.get_vision_text_model(_A   ,				_A					)
      _UpperCAmelCase						 =      TFVisionTextDualEncoderModel(vision_model=_A   ,				text_model=_A					)

      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.get_vision_text_model(_A   ,				_A					)
      _UpperCAmelCase						 =      {"""vision_model""": vision_model, """text_model""": text_model}
      _UpperCAmelCase						 =      TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_A					)

      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  str=None   ,				**__lowerCAmelCase	:  Any					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.get_vision_text_model(_A   ,				_A					)
      _UpperCAmelCase						 =      TFVisionTextDualEncoderModel(vision_model=_A   ,				text_model=_A					)

      _UpperCAmelCase						 =      model(input_ids=_A   ,				pixel_values=_A   ,				attention_mask=_A					)
      _UpperCAmelCase						 =      output[0].numpy()

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(_A					)
        _UpperCAmelCase						 =      TFVisionTextDualEncoderModel.from_pretrained(_A					)

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


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Dict=None   ,				**__lowerCAmelCase	:  str					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.get_vision_text_model(_A   ,				_A					)
      _UpperCAmelCase						 =      TFVisionTextDualEncoderModel(vision_model=_A   ,				text_model=_A					)

      _UpperCAmelCase						 =      model(
          input_ids=_A   ,				pixel_values=_A   ,				attention_mask=_A   ,				output_attentions=_A					)

      _UpperCAmelCase						 =      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)
      _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(_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      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  np.ndarray   ,				__lowerCAmelCase	:  float					):
      _UpperCAmelCase						 =      np.abs((a - b)					).max()
      self.assertLessEqual(_A   ,				_A   ,				f'''Difference between torch and flax is {diff} (>= {tol}).'''					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_model(**_A					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**_A					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**_A					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      self.check_save_load(**_A					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**_A					)


    @slow
    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.get_pretrained_model_and_inputs()

      _UpperCAmelCase						 =      model_a(**_A					)
      _UpperCAmelCase						 =      outputs[0].numpy()

      with tempfile.TemporaryDirectory() as tmp_dirname:
        model_a.save_pretrained(_A					)
        _UpperCAmelCase						 =      TFVisionTextDualEncoderModel.from_pretrained(_A					)

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



@require_tf
class        a				(		__lowercase	,							unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  str					):
      _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      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      TFViTModel(_A   ,				name="""vision_model"""					)
      _UpperCAmelCase						 =      TFBertModel(_A   ,				name="""text_model"""					)
      return vision_model, text_model


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _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        a				(		__lowercase	,							unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
      # just reinitialize it.
      _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      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  str=None   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.get_vision_text_model(_A   ,				_A					)
      _UpperCAmelCase						 =      TFVisionTextDualEncoderModel(vision_model=_A   ,				text_model=_A					)

      _UpperCAmelCase						 =      model(
          input_ids=_A   ,				pixel_values=_A   ,				attention_mask=_A   ,				output_attentions=_A					)

      _UpperCAmelCase						 =      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)
      _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(_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      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      TFDeiTModel(_A   ,				name="""vision_model"""					)
      _UpperCAmelCase						 =      TFRobertaModel(_A   ,				name="""text_model"""					)
      return vision_model, text_model


    def      lowerCAmelCase_  (					self	:  Dict					):
      _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        a				(		__lowercase	,							unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  str					):
      _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      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      TFCLIPVisionModel(_A   ,				name="""vision_model"""					)
      _UpperCAmelCase						 =      TFBertModel(_A   ,				name="""text_model"""					)
      return vision_model, text_model


    def      lowerCAmelCase_  (					self	:  int					):
      _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        a				(		unittest.TestCase   ):


    @slow
    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      TFVisionTextDualEncoderModel.from_pretrained(
          """clip-italian/clip-italian"""   ,				logit_scale_init_value=1.0   ,				from_pt=_A					)
      _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=_A   ,				padding=_A   ,				return_tensors="""np"""					)

      _UpperCAmelCase						 =      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])   ,				)

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

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

"""simple docstring"""


from __future__ import annotations






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return [ord(lowercase		) - 96 for elem in plain]






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return "".join(chr(elem + 96		) for elem in encoded		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      encode(input("""-> """		).strip().lower()		)
  print("""Encoded: """   ,lowercase		)
  print("""Decoded:"""   ,decode(lowercase		)		)


if __name__ == "__main__":
    main()
