from collections.abc import Sequence







def  __UpperCamelCase    (						A ,							A = False							):
     if not arr:
          return 0

     UpperCamelCase__										=      0 if allow_empty_subarrays else float('''-inf'''							)
     UpperCamelCase__										=      0.0
     for num in arr:
          UpperCamelCase__										=      max(0 if allow_empty_subarrays else num ,							curr_sum + num							)
          UpperCamelCase__										=      max(A ,							A							)

     return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     __magic_name__								=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(f"""{max_subarray_sum(nums) = }""")




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



@require_flax
class     _A   :






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       pass






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_	)

       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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_vision_text_model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {'''vision_model''': vision_model, '''text_model''': text_model}
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_	)

       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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_vision_text_model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {'''vision_model''': vision_model, '''text_model''': text_model}
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_	)

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_vision_text_model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {'''vision_model''': vision_model, '''text_model''': text_model}
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(
           input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				output_attentions=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      output.vision_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				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(SCREAMING_SNAKE_CASE_	)   ,				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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       pt_model.to(SCREAMING_SNAKE_CASE_	)
       pt_model.eval()

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

       with torch.no_grad():
            UpperCamelCase__										=      pt_model(**SCREAMING_SNAKE_CASE_	).to_tuple()

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

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

       UpperCamelCase__										=      fx_model_loaded(**SCREAMING_SNAKE_CASE_	).to_tuple()
       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				len(SCREAMING_SNAKE_CASE_	)   ,				'''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(SCREAMING_SNAKE_CASE_   ,				pt_output.numpy()   ,				4E-2	)

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

       pt_model_loaded.to(SCREAMING_SNAKE_CASE_	)
       pt_model_loaded.eval()

       with torch.no_grad():
            UpperCamelCase__										=      pt_model_loaded(**SCREAMING_SNAKE_CASE_	).to_tuple()

       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				len(SCREAMING_SNAKE_CASE_	)   ,				'''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(SCREAMING_SNAKE_CASE_   ,				pt_output_loaded.numpy()   ,				4E-2	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      fx_state

       self.check_pt_flax_equivalence(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_   ,				fx_model.params	)

       self.check_pt_flax_equivalence(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_save_load(**SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**SCREAMING_SNAKE_CASE_	)






  @is_pt_flax_cross_test
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__										=      config_inputs_dict.pop('''vision_config'''	)
       UpperCamelCase__										=      config_inputs_dict.pop('''text_config'''	)

       UpperCamelCase__										=      config_inputs_dict

       self.check_equivalence_pt_to_flax(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       self.check_equivalence_flax_to_pt(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  @slow
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_pretrained_model_and_inputs()

       UpperCamelCase__										=      model_a(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_	)

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



@require_flax
class     _A   (    __UpperCamelCase	, unittest.TestCase							):






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           '''hf-internal-testing/tiny-random-vit'''   ,				'''hf-internal-testing/tiny-bert'''   ,				vision_from_pt=SCREAMING_SNAKE_CASE_   ,				text_from_pt=SCREAMING_SNAKE_CASE_   ,				)
       UpperCamelCase__										=      13
       UpperCamelCase__										=      floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]	)
       UpperCamelCase__										=      ids_tensor([batch_size, 4]   ,				model.config.text_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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxViTModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxBertModel(SCREAMING_SNAKE_CASE_	)
       return vision_model, text_model






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





       '''simple docstring'''





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

       UpperCamelCase__						,       UpperCamelCase__										=      vision_config_and_inputs

       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      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     _A   (    __UpperCamelCase	, unittest.TestCase							):






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           '''hf-internal-testing/tiny-random-clip'''   ,				'''hf-internal-testing/tiny-bert'''   ,				vision_from_pt=SCREAMING_SNAKE_CASE_   ,				text_from_pt=SCREAMING_SNAKE_CASE_   ,				)
       UpperCamelCase__										=      13
       UpperCamelCase__										=      floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]	)
       UpperCamelCase__										=      ids_tensor([batch_size, 4]   ,				model.config.text_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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxCLIPVisionModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxBertModel(SCREAMING_SNAKE_CASE_	)
       return vision_model, text_model






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





       '''simple docstring'''





       UpperCamelCase__										=      FlaxCLIPVisionModelTester(self	)
       UpperCamelCase__										=      FlaxBertModelTester(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__										=      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     _A   (    unittest.TestCase							):






  @slow
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''   ,				logit_scale_init_value=1.0	)
       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=SCREAMING_SNAKE_CASE_   ,				padding=SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)

       UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

       # 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([[1.2284727, 0.3104122]]	)

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



