






'''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     A__      (					__lowerCAmelCase		:      Any    ):
						if isinstance(__lowerCAmelCase ,     collections.abc.Iterable    ):
												return x
						return (x, x)




@require_flax
class 		UpperCamelCase__     :




		'''simple docstring'''





		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								pass






		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	np.abs((a - b)							).max()
								self.assertLessEqual(_lowerCAmelCase ,_lowerCAmelCase ,F'''Difference between torch and flax is {diff} (>= {tol}).'''							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__								=	VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel(_lowerCAmelCase							)

								lowerCamelCase__								=	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       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	{"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase							)

								lowerCamelCase__								=	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       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	{"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase							)

								lowerCamelCase__								=	model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase							)
								lowerCamelCase__								=	output[0]

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(_lowerCAmelCase							)
														lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase							)

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






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	{"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase							)

								lowerCamelCase__								=	model(
								    input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase ,output_attentions=_lowerCAmelCase							)

								lowerCamelCase__								=	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)
								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(_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       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								pt_model.to(_lowerCAmelCase							)
								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(**_lowerCAmelCase							).to_tuple()

								lowerCamelCase__								=	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							)
														lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_pt=_lowerCAmelCase							)

								lowerCamelCase__								=	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							)
														lowerCamelCase__								=	VisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_flax=_lowerCAmelCase							)

								pt_model_loaded.to(_lowerCAmelCase							)
								pt_model_loaded.eval()

								with torch.no_grad():
														lowerCamelCase__								=	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       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	VisionTextDualEncoderModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel(_lowerCAmelCase							)

								lowerCamelCase__								=	convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,_lowerCAmelCase							)
								lowerCamelCase__								=	fx_state

								self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	VisionTextDualEncoderModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel(_lowerCAmelCase							)

								lowerCamelCase__								=	load_flax_weights_in_pytorch_model(_lowerCAmelCase ,fx_model.params							)

								self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_save_load(**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**_lowerCAmelCase							)






		@is_pt_flax_cross_test
		def       UpperCamelCase_       (   self							):
								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(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)
								self.check_equivalence_flax_to_pt(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_pretrained_model_and_inputs()

								lowerCamelCase__								=	model_a(**_lowerCAmelCase							)
								lowerCamelCase__								=	outputs[0]

								with tempfile.TemporaryDirectory() as tmp_dirname:
														model_a.save_pretrained(_lowerCAmelCase							)
														lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase							)

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




@require_flax
class 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-vit""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
								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       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	FlaxViTModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxBertModel(_lowerCAmelCase							)
								return vision_model, text_model






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxViTModelTester(self							)
								lowerCamelCase__								=	FlaxBertModelTester(self							)
								lowerCamelCase__								=	vit_model_tester.prepare_config_and_inputs()
								lowerCamelCase__								=	bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase__							,      lowerCamelCase__								=	vision_config_and_inputs

								lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__							,      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 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-clip""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
								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       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	FlaxCLIPVisionModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxBertModel(_lowerCAmelCase							)
								return vision_model, text_model






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxCLIPVisionModelTester(self							)
								lowerCamelCase__								=	FlaxBertModelTester(self							)
								lowerCamelCase__								=	clip_model_tester.prepare_config_and_inputs()
								lowerCamelCase__								=	bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase__							,      lowerCamelCase__								=	vision_config_and_inputs

								lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__							,      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 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





		@slow
		def       UpperCamelCase_       (   self							):
								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=_lowerCAmelCase ,padding=_lowerCAmelCase ,return_tensors="""np"""							)

								lowerCamelCase__								=	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]) ,)

								lowerCamelCase__								=	np.array([[1.228_4727, 0.310_4122]]							)

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








def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   F'''Input value of [number={number}] must be an integer'''
      raise TypeError(SCREAMING_SNAKE_CASE_						)
   if number < 0:
      return False
   _UpperCAmelCase           =   number * number
   while number > 0:
      if number % 10 != number_square % 10:
         return False
      number //= 10
      number_square //= 10
   return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()