






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      _lowerCamelCase	(						__A    :							Optional[Any] )      ->					Tuple:







    if isinstance(lowercase__		,		collections.abc.Iterable ):
        return x
    return (x, x)
@require_tf
class 							A_    :






 '''simple docstring'''




 def  snake_case__				(	self							,  _A							,  _A) ->		List[Any]:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     pass


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

     """simple docstring"""



     pass


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_							,  lowerCAmelCase_)

     _UpperCAmelCase				:      Optional[int]			  =				TFVisionTextDualEncoderModel(lowerCAmelCase_)

     _UpperCAmelCase				:      Union[str, 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  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      List[Any]			  =				self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
     _UpperCAmelCase				:      Union[str, Any]			  =				TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

     _UpperCAmelCase				:      Optional[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  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
     _UpperCAmelCase				:      Dict			  =				{'''vision_model''': vision_model, '''text_model''': text_model}
     _UpperCAmelCase				:      Optional[int]			  =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_)

     _UpperCAmelCase				:      Optional[Any]			  =				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  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Optional[int]			  =				self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
     _UpperCAmelCase				:      Tuple			  =				TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

     _UpperCAmelCase				:      str			  =				model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)
     _UpperCAmelCase				:      Optional[int]			  =				output[0].numpy()

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

         _UpperCAmelCase				:      Tuple			  =				model(input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_)
         _UpperCAmelCase				:      List[str]			  =				after_output[0].numpy()
         _UpperCAmelCase				:      Union[str, Any]			  =				np.amax(np.abs(out_a - out_a))
         self.assertLessEqual(lowerCAmelCase_							,  1e-5)


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
     _UpperCAmelCase				:      Any			  =				TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

     _UpperCAmelCase				:      Tuple			  =				model(
         input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  output_attentions=lowerCAmelCase_)

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

     _UpperCAmelCase				:      Dict			  =				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  snake_case__				(	self							,  _A							,  _A							,  _A) ->		Dict:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_model(**lowerCAmelCase_)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.prepare_config_and_inputs()
     self.check_model_from_pretrained_configs(**lowerCAmelCase_)


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

     """simple docstring"""



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


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

     """simple docstring"""



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


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.prepare_config_and_inputs()
     self.check_vision_text_output_attention(**lowerCAmelCase_)


 @slow
 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				self.get_pretrained_model_and_inputs()

     _UpperCAmelCase				:      Tuple			  =				model_a(**lowerCAmelCase_)
     _UpperCAmelCase				:      List[Any]			  =				outputs[0].numpy()

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

         _UpperCAmelCase				:      Any			  =				model_a(**lowerCAmelCase_)
         _UpperCAmelCase				:      List[Any]			  =				after_outputs[0].numpy()
         _UpperCAmelCase				:      Tuple			  =				np.amax(np.abs(out_a - out_a))
         self.assertLessEqual(lowerCAmelCase_							,  1e-5)



@require_tf
class 							A_    (						__a ,   unittest.TestCase			):






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         '''hf-internal-testing/tiny-random-vit'''							,  '''hf-internal-testing/tiny-random-bert''')
     _UpperCAmelCase				:      Optional[int]			  =				13
     _UpperCAmelCase				:      str			  =				floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ])
     _UpperCAmelCase				:      str			  =				ids_tensor([batch_size, 4]							,  model.text_model.config.vocab_size)
     _UpperCAmelCase				:      str			  =				random_attention_mask([batch_size, 4])
     _UpperCAmelCase				:      List[str]			  =				{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

     return model, inputs


 def  snake_case__				(	self							,  _A							,  _A) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				TFViTModel(lowerCAmelCase_							,  name='''vision_model''')
     _UpperCAmelCase				:      Union[str, Any]			  =				TFBertModel(lowerCAmelCase_							,  name='''text_model''')
     return vision_model, text_model


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

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				TFViTModelTester(self)
     _UpperCAmelCase				:      str			  =				TFBertModelTester(self)
     _UpperCAmelCase				:      Any			  =				vit_model_tester.prepare_config_and_inputs()
     _UpperCAmelCase				:      Optional[int]			  =				bert_model_tester.prepare_config_and_inputs()

     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Union[str, Any]			  =				vision_config_and_inputs

     (
         (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , 						
     )				:      str			  =				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_    (						__a ,   unittest.TestCase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         '''Rocketknight1/tiny-random-deit-tf'''							,  '''hf-internal-testing/tiny-random-roberta''')
     _UpperCAmelCase				:      List[Any]			  =				13
     _UpperCAmelCase				:      List[Any]			  =				floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ])
     _UpperCAmelCase				:      Any			  =				ids_tensor([batch_size, 4]							,  model.text_model.config.vocab_size)
     _UpperCAmelCase				:      Any			  =				random_attention_mask([batch_size, 4])
     _UpperCAmelCase				:      List[str]			  =				{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

     return model, inputs


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		int:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				self.get_vision_text_model(lowerCAmelCase_							,  lowerCAmelCase_)
     _UpperCAmelCase				:      Optional[Any]			  =				TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase_							,  text_model=lowerCAmelCase_)

     _UpperCAmelCase				:      Optional[int]			  =				model(
         input_ids=lowerCAmelCase_							,  pixel_values=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  output_attentions=lowerCAmelCase_)

     _UpperCAmelCase				:      Union[str, Any]			  =				output.vision_model_output.attentions
     self.assertEqual(len(lowerCAmelCase_)							,  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				:      int			  =				to_atuple(vision_model.config.image_size)
     _UpperCAmelCase				:      str			  =				to_atuple(vision_model.config.patch_size)
     _UpperCAmelCase				:      int			  =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     _UpperCAmelCase				:      Optional[Any]			  =				num_patches + 2
     self.assertEqual(vision_attentions[0].shape[-3:]							,  (vision_config.num_attention_heads, seq_len, seq_len))

     _UpperCAmelCase				:      Union[str, Any]			  =				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  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				TFDeiTModel(lowerCAmelCase_							,  name='''vision_model''')
     _UpperCAmelCase				:      str			  =				TFRobertaModel(lowerCAmelCase_							,  name='''text_model''')
     return vision_model, text_model


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				TFDeiTModelTester(self)
     _UpperCAmelCase				:      Tuple			  =				TFRobertaModelTester(self)
     _UpperCAmelCase				:      Dict			  =				vit_model_tester.prepare_config_and_inputs()
     _UpperCAmelCase				:      List[Any]			  =				bert_model_tester.prepare_config_and_inputs()

     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				vision_config_and_inputs

     (
         (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , 						
     )				:      List[str]			  =				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_    (						__a ,   unittest.TestCase			):






 '''simple docstring'''




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

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         '''Rocketknight1/tiny-random-clip-tf'''							,  '''hf-internal-testing/tiny-random-bert''')
     _UpperCAmelCase				:      Union[str, Any]			  =				13
     _UpperCAmelCase				:      Optional[int]			  =				floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ])
     _UpperCAmelCase				:      int			  =				ids_tensor([batch_size, 4]							,  model.text_model.config.vocab_size)
     _UpperCAmelCase				:      Optional[int]			  =				random_attention_mask([batch_size, 4])
     _UpperCAmelCase				:      Union[str, Any]			  =				{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

     return model, inputs


 def  snake_case__				(	self							,  _A							,  _A) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				TFCLIPVisionModel(lowerCAmelCase_							,  name='''vision_model''')
     _UpperCAmelCase				:      Optional[Any]			  =				TFBertModel(lowerCAmelCase_							,  name='''text_model''')
     return vision_model, text_model


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				TFCLIPVisionModelTester(self)
     _UpperCAmelCase				:      int			  =				TFBertModelTester(self)
     _UpperCAmelCase				:      int			  =				clip_model_tester.prepare_config_and_inputs()
     _UpperCAmelCase				:      Tuple			  =				bert_model_tester.prepare_config_and_inputs()

     _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				vision_config_and_inputs

     (
         (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , (
         _UpperCAmelCase						
     )  , 						
     )				:      Any			  =				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			):






 '''simple docstring'''




 @slow
 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				TFVisionTextDualEncoderModel.from_pretrained(
         '''clip-italian/clip-italian'''							,  logit_scale_init_value=1.0							,  from_pt=lowerCAmelCase_)
     _UpperCAmelCase				:      Union[str, Any]			  =				VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''')

     _UpperCAmelCase				:      Optional[Any]			  =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     _UpperCAmelCase				:      List[str]			  =				processor(
         text=['''una foto di un gatto''', '''una foto di un cane''']							,  images=lowerCAmelCase_							,  padding=lowerCAmelCase_							,  return_tensors='''np''')

     _UpperCAmelCase				:      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])							,  )

     _UpperCAmelCase				:      Optional[Any]			  =				np.array([[1.2284727, 0.3104122]])

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












from math import pow, sqrt

def      _lowerCamelCase	(						*__A    :							float )      ->					bool:
    _UpperCAmelCase				:      str			  =				len(__A ) > 0 and all(value > 0.0 for value in values )
    return result

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A )
        else ValueError('''Input Error: Molar mass values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,		2 )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )


def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(pow(effusion_rate_a / effusion_rate_a		,		2 ) / molar_mass		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )




