


'''simple docstring'''







import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
 from transformers import (
     FlaxBertModel,
     FlaxCLIPVisionModel,
     FlaxVisionTextDualEncoderModel,
     FlaxViTModel,
     VisionTextDualEncoderConfig,
     VisionTextDualEncoderProcessor,
 )
 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )


if is_torch_available():
 import torch

 from transformers import VisionTextDualEncoderModel

if is_vision_available():
 from PIL import Image




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




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


@require_flax
class  lowerCAmelCase_						:





  """simple docstring"""



  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





       '''simple docstring'''

       pass

  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       pass

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       pass

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							np.ndarray				,							SCREAMING_SNAKE_CASE__     :							np.ndarray				,							SCREAMING_SNAKE_CASE__     :							float						):





       '''simple docstring'''

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

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       __a       =   VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase				,							__UpperCamelCase						)

       __a       =   FlaxVisionTextDualEncoderModel(__UpperCamelCase						)

       __a       =   model(input_ids=__UpperCamelCase				,							pixel_values=__UpperCamelCase				,							attention_mask=__UpperCamelCase						)

       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     :							str				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[str]=None				,							**SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       __a ,						__a       =   self.get_vision_text_model(__UpperCamelCase				,							__UpperCamelCase						)
       __a       =   {"""vision_model""": vision_model, """text_model""": text_model}
       __a       =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase						)

       __a       =   model(input_ids=__UpperCamelCase				,							pixel_values=__UpperCamelCase				,							attention_mask=__UpperCamelCase						)

       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     :							str				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							List[str]=None				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __a ,						__a       =   self.get_vision_text_model(__UpperCamelCase				,							__UpperCamelCase						)
       __a       =   {"""vision_model""": vision_model, """text_model""": text_model}
       __a       =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase						)

       __a       =   model(input_ids=__UpperCamelCase				,							pixel_values=__UpperCamelCase				,							attention_mask=__UpperCamelCase						)
       __a       =   output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(__UpperCamelCase						)
            __a       =   FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase						)

            __a       =   model(input_ids=__UpperCamelCase				,							pixel_values=__UpperCamelCase				,							attention_mask=__UpperCamelCase						)
            __a       =   after_output[0]
            __a       =   np.amax(np.abs(out_a - out_a						)						)
            self.assertLessEqual(__UpperCamelCase				,							1E-3						)

  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							List[str]=None				,							**SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       __a ,						__a       =   self.get_vision_text_model(__UpperCamelCase				,							__UpperCamelCase						)
       __a       =   {"""vision_model""": vision_model, """text_model""": text_model}
       __a       =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase						)

       __a       =   model(
           input_ids=__UpperCamelCase				,							pixel_values=__UpperCamelCase				,							attention_mask=__UpperCamelCase				,							output_attentions=__UpperCamelCase						)

       __a       =   output.vision_model_output.attentions
       self.assertEqual(len(__UpperCamelCase						)				,							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(__UpperCamelCase						)				,							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     :							int				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       pt_model.to(__UpperCamelCase						)
       pt_model.eval()

       # prepare inputs
       __a       =   inputs_dict
       __a       =   {k: torch.tensor(v.tolist()						) for k, v in flax_inputs.items()}

       with torch.no_grad():
            __a       =   pt_model(**__UpperCamelCase						).to_tuple()

       __a       =   fx_model(**__UpperCamelCase						).to_tuple()
       self.assertEqual(len(__UpperCamelCase						)				,							len(__UpperCamelCase						)				,							"""Output lengths differ between Flax and PyTorch"""						)
       for fx_output, pt_output in zip(fx_outputs[:4]				,							pt_outputs[:4]						):
            self.assert_almost_equals(__UpperCamelCase				,							pt_output.numpy()				,							4E-2						)

       # PT -> Flax
       with tempfile.TemporaryDirectory() as tmpdirname:
            pt_model.save_pretrained(__UpperCamelCase						)
            __a       =   FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase				,							from_pt=__UpperCamelCase						)

       __a       =   fx_model_loaded(**__UpperCamelCase						).to_tuple()
       self.assertEqual(len(__UpperCamelCase						)				,							len(__UpperCamelCase						)				,							"""Output lengths differ between Flax and PyTorch"""						)
       for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]				,							pt_outputs[:4]						):
            self.assert_almost_equals(__UpperCamelCase				,							pt_output.numpy()				,							4E-2						)

       # Flax -> PT
       with tempfile.TemporaryDirectory() as tmpdirname:
            fx_model.save_pretrained(__UpperCamelCase						)
            __a       =   VisionTextDualEncoderModel.from_pretrained(__UpperCamelCase				,							from_flax=__UpperCamelCase						)

       pt_model_loaded.to(__UpperCamelCase						)
       pt_model_loaded.eval()

       with torch.no_grad():
            __a       =   pt_model_loaded(**__UpperCamelCase						).to_tuple()

       self.assertEqual(len(__UpperCamelCase						)				,							len(__UpperCamelCase						)				,							"""Output lengths differ between Flax and PyTorch"""						)
       for fx_output, pt_output_loaded in zip(fx_outputs[:4]				,							pt_outputs_loaded[:4]						):
            self.assert_almost_equals(__UpperCamelCase				,							pt_output_loaded.numpy()				,							4E-2						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Any						):





       '''simple docstring'''

       __a       =   VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase				,							__UpperCamelCase						)

       __a       =   VisionTextDualEncoderModel(__UpperCamelCase						)
       __a       =   FlaxVisionTextDualEncoderModel(__UpperCamelCase						)

       __a       =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,							__UpperCamelCase						)
       __a       =   fx_state

       self.check_pt_flax_equivalence(__UpperCamelCase				,							__UpperCamelCase				,							__UpperCamelCase						)

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       __a       =   VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase				,							__UpperCamelCase						)

       __a       =   VisionTextDualEncoderModel(__UpperCamelCase						)
       __a       =   FlaxVisionTextDualEncoderModel(__UpperCamelCase						)

       __a       =   load_flax_weights_in_pytorch_model(__UpperCamelCase				,							fx_model.params						)

       self.check_pt_flax_equivalence(__UpperCamelCase				,							__UpperCamelCase				,							__UpperCamelCase						)

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**__UpperCamelCase						)

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**__UpperCamelCase						)

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       self.check_save_load(**__UpperCamelCase						)

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**__UpperCamelCase						)

  @is_pt_flax_cross_test
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       __a       =   config_inputs_dict.pop("""vision_config"""						)
       __a       =   config_inputs_dict.pop("""text_config"""						)

       __a       =   config_inputs_dict

       self.check_equivalence_pt_to_flax(__UpperCamelCase				,							__UpperCamelCase				,							__UpperCamelCase						)
       self.check_equivalence_flax_to_pt(__UpperCamelCase				,							__UpperCamelCase				,							__UpperCamelCase						)


  @slow
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a ,						__a       =   self.get_pretrained_model_and_inputs()

       __a       =   model_a(**__UpperCamelCase						)
       __a       =   outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(__UpperCamelCase						)
            __a       =   FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase						)

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


@require_flax
class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           """hf-internal-testing/tiny-random-vit"""				,							"""hf-internal-testing/tiny-bert"""				,							vision_from_pt=__UpperCamelCase				,							text_from_pt=__UpperCamelCase				,							)
       __a       =   1_3
       __a       =   floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]						)
       __a       =   ids_tensor([batch_size, 4]				,							model.config.text_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     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Any						):





       '''simple docstring'''

       __a       =   FlaxViTModel(__UpperCamelCase						)
       __a       =   FlaxBertModel(__UpperCamelCase						)
       return vision_model, text_model


  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   FlaxViTModelTester(self						)
       __a       =   FlaxBertModelTester(self						)
       __a       =   vit_model_tester.prepare_config_and_inputs()
       __a       =   bert_model_tester.prepare_config_and_inputs()

       __a ,						__a       =   vision_config_and_inputs

       __a ,						__a ,						__a ,						__a       =   text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }


@require_torch
class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           """hf-internal-testing/tiny-random-clip"""				,							"""hf-internal-testing/tiny-bert"""				,							vision_from_pt=__UpperCamelCase				,							text_from_pt=__UpperCamelCase				,							)
       __a       =   1_3
       __a       =   floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]						)
       __a       =   ids_tensor([batch_size, 4]				,							model.config.text_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     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __a       =   FlaxCLIPVisionModel(__UpperCamelCase						)
       __a       =   FlaxBertModel(__UpperCamelCase						)
       return vision_model, text_model


  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   FlaxCLIPVisionModelTester(self						)
       __a       =   FlaxBertModelTester(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       =   text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }


@require_flax
@require_vision
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""				,							logit_scale_init_value=1.0						)
       __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=__UpperCamelCase				,							padding=__UpperCamelCase				,							return_tensors="""np"""						)

       __a       =   model(**__UpperCamelCase						)

       # 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				,							__UpperCamelCase				,							atol=1E-3						)						)










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






