




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      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[str]	):


      '''simple docstring'''



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







@require_flax
class     A:



 '''simple docstring'''


 def 							a__					(							self   :    List[str]		,				A_   :    List[str]		,				A_   :    List[str]    )  ->      Union[str, Any]:

       """simple docstring"""






       pass

 def 							a__					(							self   :    Dict    )  ->      List[Any]:

       """simple docstring"""






       pass

 def 							a__					(							self   :    int    )  ->      int:

       """simple docstring"""






       pass

 def 							a__					(							self   :    List[Any]		,				A_   :    List[str]		,				A_   :    Optional[int]		,				A_   :    int    )  ->      str:

       """simple docstring"""






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

 def 							a__					(							self   :    Dict		,				A_   :    Dict		,				A_   :    Any		,				A_   :    List[Any]		,				A_   :    List[Any]		,				A_   :    Dict=None		,				**A_   :    Any    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__		,				snake_case__    )

       lowerCamelCase_							=       FlaxVisionTextDualEncoderModel(snake_case__    )

       lowerCamelCase_							=       model(input_ids=snake_case__		,				pixel_values=snake_case__		,				attention_mask=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   :    Dict		,				A_   :    List[str]		,				A_   :    str		,				A_   :    str		,				A_   :    Optional[int]		,				A_   :    List[Any]=None		,				**A_   :    Dict    )  ->      Dict:

       """simple docstring"""






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

       lowerCamelCase_							=       model(input_ids=snake_case__		,				pixel_values=snake_case__		,				attention_mask=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   :    List[Any]		,				A_   :    Any		,				A_   :    Any		,				A_   :    List[str]		,				A_   :    List[str]		,				A_   :    Optional[int]=None		,				**A_   :    Optional[Any]    )  ->      List[str]:

       """simple docstring"""






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

       lowerCamelCase_							=       model(input_ids=snake_case__		,				pixel_values=snake_case__		,				attention_mask=snake_case__    )
       lowerCamelCase_							=       output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(snake_case__    )
             lowerCamelCase_							=       FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__    )

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

 def 							a__					(							self   :    Optional[int]		,				A_   :    Any		,				A_   :    Optional[int]		,				A_   :    List[Any]		,				A_   :    str		,				A_   :    str=None		,				**A_   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






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

       lowerCamelCase_							=       model(
           input_ids=snake_case__		,				pixel_values=snake_case__		,				attention_mask=snake_case__		,				output_attentions=snake_case__    )

       lowerCamelCase_							=       output.vision_model_output.attentions
       self.assertEqual(len(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)
       lowerCamelCase_							=       to_atuple(vision_model.config.image_size    )
       lowerCamelCase_							=       to_atuple(vision_model.config.patch_size    )
       lowerCamelCase_							=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       lowerCamelCase_							=       num_patches + 1
       self.assertEqual(vision_attentions[0].shape[-3:]		,				(vision_config.num_attention_heads, seq_len, seq_len)    )

       lowerCamelCase_							=       output.text_model_output.attentions
       self.assertEqual(len(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   :    int		,				A_   :    List[str]		,				A_   :    Dict		,				A_   :    List[Any]    )  ->      List[Any]:

       """simple docstring"""






       pt_model.to(snake_case__    )
       pt_model.eval()

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

       with torch.no_grad():
             lowerCamelCase_							=       pt_model(**snake_case__    ).to_tuple()

       lowerCamelCase_							=       fx_model(**snake_case__    ).to_tuple()
       self.assertEqual(len(snake_case__    )		,				len(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(snake_case__		,				pt_output.numpy()		,				4E-2    )

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

       lowerCamelCase_							=       fx_model_loaded(**snake_case__    ).to_tuple()
       self.assertEqual(len(snake_case__    )		,				len(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(snake_case__		,				pt_output.numpy()		,				4E-2    )

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

       pt_model_loaded.to(snake_case__    )
       pt_model_loaded.eval()

       with torch.no_grad():
             lowerCamelCase_							=       pt_model_loaded(**snake_case__    ).to_tuple()

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

 def 							a__					(							self   :    Tuple		,				A_   :    Union[str, Any]		,				A_   :    Optional[Any]		,				A_   :    Optional[Any]    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__		,				snake_case__    )

       lowerCamelCase_							=       VisionTextDualEncoderModel(snake_case__    )
       lowerCamelCase_							=       FlaxVisionTextDualEncoderModel(snake_case__    )

       lowerCamelCase_							=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,				snake_case__    )
       lowerCamelCase_							=       fx_state

       self.check_pt_flax_equivalence(snake_case__		,				snake_case__		,				snake_case__    )

 def 							a__					(							self   :    Any		,				A_   :    List[Any]		,				A_   :    int		,				A_   :    str    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__		,				snake_case__    )

       lowerCamelCase_							=       VisionTextDualEncoderModel(snake_case__    )
       lowerCamelCase_							=       FlaxVisionTextDualEncoderModel(snake_case__    )

       lowerCamelCase_							=       load_flax_weights_in_pytorch_model(snake_case__		,				fx_model.params    )

       self.check_pt_flax_equivalence(snake_case__		,				snake_case__		,				snake_case__    )

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

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**snake_case__    )

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

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**snake_case__    )

 def 							a__					(							self   :    List[str]    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       self.check_save_load(**snake_case__    )

 def 							a__					(							self   :    List[str]    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**snake_case__    )

 @is_pt_flax_cross_test
 def 							a__					(							self   :    Union[str, Any]    )  ->      Optional[int]:

       """simple docstring"""






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

       lowerCamelCase_							=       config_inputs_dict

       self.check_equivalence_pt_to_flax(snake_case__		,				snake_case__		,				snake_case__    )
       self.check_equivalence_flax_to_pt(snake_case__		,				snake_case__		,				snake_case__    )





 @slow
 def 							a__					(							self   :    Tuple    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.get_pretrained_model_and_inputs()

       lowerCamelCase_							=       model_a(**snake_case__    )
       lowerCamelCase_							=       outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
             model_a.save_pretrained(snake_case__    )
             lowerCamelCase_							=       FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__    )

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







@require_flax
class     A(		lowerCamelCase_				, unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    Union[str, Any]    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           'hf-internal-testing/tiny-random-vit'		,				'hf-internal-testing/tiny-bert'		,				vision_from_pt=snake_case__		,				text_from_pt=snake_case__		,				)
       lowerCamelCase_							=       13
       lowerCamelCase_							=       floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]    )
       lowerCamelCase_							=       ids_tensor([batch_size, 4]		,				model.config.text_config.vocab_size    )
       lowerCamelCase_							=       random_attention_mask([batch_size, 4]    )
       lowerCamelCase_							=       {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

       return model, inputs

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    int		,				A_   :    Optional[int]    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       FlaxViTModel(snake_case__    )
       lowerCamelCase_							=       FlaxBertModel(snake_case__    )
       return vision_model, text_model





 def 							a__					(							self   :    str    )  ->      str:

       """simple docstring"""






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

       lowerCamelCase_							=       vision_config_and_inputs

       lowerCamelCase_							=       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(		lowerCamelCase_				, unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    Optional[int]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           'hf-internal-testing/tiny-random-clip'		,				'hf-internal-testing/tiny-bert'		,				vision_from_pt=snake_case__		,				text_from_pt=snake_case__		,				)
       lowerCamelCase_							=       13
       lowerCamelCase_							=       floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]    )
       lowerCamelCase_							=       ids_tensor([batch_size, 4]		,				model.config.text_config.vocab_size    )
       lowerCamelCase_							=       random_attention_mask([batch_size, 4]    )
       lowerCamelCase_							=       {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

       return model, inputs

 def 							a__					(							self   :    Any		,				A_   :    Union[str, Any]		,				A_   :    Any    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       FlaxCLIPVisionModel(snake_case__    )
       lowerCamelCase_							=       FlaxBertModel(snake_case__    )
       return vision_model, text_model





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

       """simple docstring"""






       lowerCamelCase_							=       FlaxCLIPVisionModelTester(self    )
       lowerCamelCase_							=       FlaxBertModelTester(self    )
       lowerCamelCase_							=       clip_model_tester.prepare_config_and_inputs()
       lowerCamelCase_							=       bert_model_tester.prepare_config_and_inputs()

       lowerCamelCase_							=       vision_config_and_inputs

       lowerCamelCase_							=       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  ):



 '''simple docstring'''


 @slow
 def 							a__					(							self   :    Tuple    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'		,				logit_scale_init_value=1.0    )
       lowerCamelCase_							=       VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'    )

       lowerCamelCase_							=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
       lowerCamelCase_							=       processor(
           text=['una foto di un gatto', 'una foto di un cane']		,				images=snake_case__		,				padding=snake_case__		,				return_tensors='np'    )

       lowerCamelCase_							=       model(**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])		,				)

       lowerCamelCase_							=       np.array([[1.2284727, 0.3104122]]    )

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









from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase__ :   Any						=							logging.get_logger(__name__)

UpperCamelCase__ :   Any						=							{
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[Any]						=     "van"






				def __init__(			self      ,snake_case__=224      ,snake_case__=3      ,snake_case__=[7, 3, 3, 3]      ,snake_case__=[4, 2, 2, 2]      ,snake_case__=[64, 128, 320, 512]      ,snake_case__=[3, 3, 12, 3]      ,snake_case__=[8, 8, 4, 4]      ,snake_case__="gelu"      ,snake_case__=0.02      ,snake_case__=1E-6      ,snake_case__=1E-2      ,snake_case__=0.0      ,snake_case__=0.0      ,**snake_case__      ,):
							super().__init__(**snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      image_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      num_channels
							SCREAMING_SNAKE_CASE_   : Dict					  =      patch_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      strides
							SCREAMING_SNAKE_CASE_   : str					  =      hidden_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      depths
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      mlp_ratios
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      layer_norm_eps
							SCREAMING_SNAKE_CASE_   : int					  =      layer_scale_init_value
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      drop_path_rate
							SCREAMING_SNAKE_CASE_   : List[str]					  =      dropout_rate



