
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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Any:



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

@require_flax
class __snake_case		:
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				np.abs((a - b)				).max()
           self.assertLessEqual(_lowerCAmelCase		,_lowerCAmelCase		,f"Difference between torch and flax is {diff} (>= {tol})."				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase		,_lowerCAmelCase				)

           lowercase							:					List[str]	     =				FlaxVisionTextDualEncoderModel(_lowerCAmelCase				)

           lowercase							:					Optional[Any]	     =				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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					List[Any]	     =				self.get_vision_text_model(_lowerCAmelCase		,_lowerCAmelCase				)
           lowercase							:					Dict	     =				{"""vision_model""": vision_model, """text_model""": text_model}
           lowercase							:					List[str]	     =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase				)

           lowercase							:					int	     =				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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					Any	     =				self.get_vision_text_model(_lowerCAmelCase		,_lowerCAmelCase				)
           lowercase							:					List[Any]	     =				{"""vision_model""": vision_model, """text_model""": text_model}
           lowercase							:					Optional[Any]	     =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase				)

           lowercase							:					Any	     =				model(input_ids=_lowerCAmelCase		,pixel_values=_lowerCAmelCase		,attention_mask=_lowerCAmelCase				)
           lowercase							:					Union[str, Any]	     =				output[0]

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(_lowerCAmelCase				)
                  lowercase							:					Optional[int]	     =				FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase				)

                  lowercase							:					List[str]	     =				model(input_ids=_lowerCAmelCase		,pixel_values=_lowerCAmelCase		,attention_mask=_lowerCAmelCase				)
                  lowercase							:					Optional[int]	     =				after_output[0]
                  lowercase							:					int	     =				np.amax(np.abs(out_a - out_a				)				)
                  self.assertLessEqual(_lowerCAmelCase		,1e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					List[str]	     =				self.get_vision_text_model(_lowerCAmelCase		,_lowerCAmelCase				)
           lowercase							:					Optional[Any]	     =				{"""vision_model""": vision_model, """text_model""": text_model}
           lowercase							:					Tuple	     =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase				)

           lowercase							:					Optional[int]	     =				model(
               input_ids=_lowerCAmelCase		,pixel_values=_lowerCAmelCase		,attention_mask=_lowerCAmelCase		,output_attentions=_lowerCAmelCase				)

           lowercase							:					Tuple	     =				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)
           lowercase							:					str	     =				to_atuple(vision_model.config.image_size				)
           lowercase							:					Union[str, Any]	     =				to_atuple(vision_model.config.patch_size				)
           lowercase							:					str	     =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
           lowercase							:					str	     =				num_patches + 1
           self.assertEqual(vision_attentions[0].shape[-3:]		,(vision_config.num_attention_heads, seq_len, seq_len)				)

           lowercase							:					List[str]	     =				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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           pt_model.to(_lowerCAmelCase				)
           pt_model.eval()

           # prepare inputs
           lowercase							:					str	     =				inputs_dict
           lowercase							:					Optional[int]	     =				{k: torch.tensor(v.tolist()				) for k, v in flax_inputs.items()}

           with torch.no_grad():
                  lowercase							:					int	     =				pt_model(**_lowerCAmelCase				).to_tuple()

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

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

           lowercase							:					Union[str, Any]	     =				fx_model_loaded(**_lowerCAmelCase				).to_tuple()
           self.assertEqual(len(_lowerCAmelCase				)		,len(_lowerCAmelCase				)		,"""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(_lowerCAmelCase		,pt_output.numpy()		,4e-2				)

           # Flax -> PT
           with tempfile.TemporaryDirectory() as tmpdirname:
                  fx_model.save_pretrained(_lowerCAmelCase				)
                  lowercase							:					List[Any]	     =				VisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase		,from_flax=_lowerCAmelCase				)

           pt_model_loaded.to(_lowerCAmelCase				)
           pt_model_loaded.eval()

           with torch.no_grad():
                  lowercase							:					str	     =				pt_model_loaded(**_lowerCAmelCase				).to_tuple()

           self.assertEqual(len(_lowerCAmelCase				)		,len(_lowerCAmelCase				)		,"""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(_lowerCAmelCase		,pt_output_loaded.numpy()		,4e-2				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase		,_lowerCAmelCase				)

           lowercase							:					List[Any]	     =				VisionTextDualEncoderModel(_lowerCAmelCase				)
           lowercase							:					Union[str, Any]	     =				FlaxVisionTextDualEncoderModel(_lowerCAmelCase				)

           lowercase							:					Optional[int]	     =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,_lowerCAmelCase				)
           lowercase							:					int	     =				fx_state

           self.check_pt_flax_equivalence(_lowerCAmelCase		,_lowerCAmelCase		,_lowerCAmelCase				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase		,_lowerCAmelCase				)

           lowercase							:					Tuple	     =				VisionTextDualEncoderModel(_lowerCAmelCase				)
           lowercase							:					List[str]	     =				FlaxVisionTextDualEncoderModel(_lowerCAmelCase				)

           lowercase							:					Optional[Any]	     =				load_flax_weights_in_pytorch_model(_lowerCAmelCase		,fx_model.params				)

           self.check_pt_flax_equivalence(_lowerCAmelCase		,_lowerCAmelCase		,_lowerCAmelCase				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**_lowerCAmelCase				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.prepare_config_and_inputs()
           self.check_save_load(**_lowerCAmelCase				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**_lowerCAmelCase				)
    @is_pt_flax_cross_test
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.prepare_config_and_inputs()
           lowercase							:					Dict	     =				config_inputs_dict.pop("""vision_config"""				)
           lowercase							:					int	     =				config_inputs_dict.pop("""text_config"""				)

           lowercase							:					Optional[Any]	     =				config_inputs_dict

           self.check_equivalence_pt_to_flax(_lowerCAmelCase		,_lowerCAmelCase		,_lowerCAmelCase				)
           self.check_equivalence_flax_to_pt(_lowerCAmelCase		,_lowerCAmelCase		,_lowerCAmelCase				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					int	     =				self.get_pretrained_model_and_inputs()

           lowercase							:					Tuple	     =				model_a(**_lowerCAmelCase				)
           lowercase							:					List[str]	     =				outputs[0]

           with tempfile.TemporaryDirectory() as tmp_dirname:
                  model_a.save_pretrained(_lowerCAmelCase				)
                  lowercase							:					Optional[int]	     =				FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase				)

                  lowercase							:					List[str]	     =				model_a(**_lowerCAmelCase				)
                  lowercase							:					List[Any]	     =				after_outputs[0]
                  lowercase							:					List[Any]	     =				np.amax(np.abs(out_a - out_a				)				)
                  self.assertLessEqual(_lowerCAmelCase		,1e-5				)






@require_flax
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               """hf-internal-testing/tiny-random-vit"""		,"""hf-internal-testing/tiny-bert"""		,vision_from_pt=_lowerCAmelCase		,text_from_pt=_lowerCAmelCase		,)
           lowercase							:					Dict	     =				13
           lowercase							:					Union[str, Any]	     =				floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ]				)
           lowercase							:					int	     =				ids_tensor([batch_size, 4]		,model.config.text_config.vocab_size				)
           lowercase							:					Dict	     =				random_attention_mask([batch_size, 4]				)
           lowercase							:					List[Any]	     =				{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

           return model, inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				FlaxViTModel(_lowerCAmelCase				)
           lowercase							:					List[str]	     =				FlaxBertModel(_lowerCAmelCase				)
           return vision_model, text_model
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				FlaxViTModelTester(self				)
           lowercase							:					int	     =				FlaxBertModelTester(self				)
           lowercase							:					Union[str, Any]	     =				vit_model_tester.prepare_config_and_inputs()
           lowercase							:					Union[str, Any]	     =				bert_model_tester.prepare_config_and_inputs()

           lowercase     ,      lowercase							:					Any	     =				vision_config_and_inputs

           lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					Tuple	     =				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 __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               """hf-internal-testing/tiny-random-clip"""		,"""hf-internal-testing/tiny-bert"""		,vision_from_pt=_lowerCAmelCase		,text_from_pt=_lowerCAmelCase		,)
           lowercase							:					List[str]	     =				13
           lowercase							:					List[Any]	     =				floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ]				)
           lowercase							:					List[Any]	     =				ids_tensor([batch_size, 4]		,model.config.text_config.vocab_size				)
           lowercase							:					str	     =				random_attention_mask([batch_size, 4]				)
           lowercase							:					List[str]	     =				{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

           return model, inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				FlaxCLIPVisionModel(_lowerCAmelCase				)
           lowercase							:					int	     =				FlaxBertModel(_lowerCAmelCase				)
           return vision_model, text_model
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				FlaxCLIPVisionModelTester(self				)
           lowercase							:					Optional[Any]	     =				FlaxBertModelTester(self				)
           lowercase							:					int	     =				clip_model_tester.prepare_config_and_inputs()
           lowercase							:					Union[str, Any]	     =				bert_model_tester.prepare_config_and_inputs()

           lowercase     ,      lowercase							:					Dict	     =				vision_config_and_inputs

           lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					Dict	     =				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 __snake_case		(				unittest.TestCase					):
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""		,logit_scale_init_value=1.0				)
           lowercase							:					Dict	     =				VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""				)

           lowercase							:					Any	     =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
           lowercase							:					int	     =				processor(
               text=["""una foto di un gatto""", """una foto di un cane"""]		,images=_lowerCAmelCase		,padding=_lowerCAmelCase		,return_tensors="""np"""				)

           lowercase							:					Optional[Any]	     =				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])		,)

           lowercase							:					List[str]	     =				np.array([[1.2_284_727, 0.3_104_122]]				)

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




from bisect import bisect
from itertools import accumulate







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					Dict	     =				sorted(zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     key=lambda SCREAMING_SNAKE_CASE__    : x[0] / x[1] ,     reverse=SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					Optional[Any]	     =				[i[0] for i in r], [i[1] for i in r]
       lowercase							:					Any	     =				list(accumulate(SCREAMING_SNAKE_CASE__    )    )
       lowercase							:					int	     =				bisect(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       return (
           0
           if k == 0
           else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]    )
       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
