



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						):





							'''simple docstring'''




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



@require_flax
class 						UpperCAmelCase_					:







							def      snake_case__   (     self,    __a,    __a):

														'''simple docstring'''

														pass







							def      snake_case__   (     self):

														'''simple docstring'''

														pass







							def      snake_case__   (     self):

														'''simple docstring'''

														pass







							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					np.abs((a - b)).max()
														self.assertLessEqual(UpperCamelCase_,    UpperCamelCase_,    f"Difference between torch and flax is {diff} (>= {tol}).")







							def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a=None,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase_,    UpperCamelCase_)

														_lowerCAmelCase  :							Optional[int]				     =					FlaxVisionTextDualEncoderModel(UpperCamelCase_)

														_lowerCAmelCase  :							int				     =					model(input_ids=UpperCamelCase_,    pixel_values=UpperCamelCase_,    attention_mask=UpperCamelCase_)

														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):

														'''simple docstring'''

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

														_lowerCAmelCase  :							Tuple				     =					model(input_ids=UpperCamelCase_,    pixel_values=UpperCamelCase_,    attention_mask=UpperCamelCase_)

														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):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.get_vision_text_model(UpperCamelCase_,    UpperCamelCase_)
														_lowerCAmelCase  :							List[Any]				     =					{'vision_model': vision_model, 'text_model': text_model}
														_lowerCAmelCase  :							str				     =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase_)

														_lowerCAmelCase  :							Any				     =					model(input_ids=UpperCamelCase_,    pixel_values=UpperCamelCase_,    attention_mask=UpperCamelCase_)
														_lowerCAmelCase  :							List[Any]				     =					output[0]

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

																					_lowerCAmelCase  :							Union[str, Any]				     =					model(input_ids=UpperCamelCase_,    pixel_values=UpperCamelCase_,    attention_mask=UpperCamelCase_)
																					_lowerCAmelCase  :							Union[str, Any]				     =					after_output[0]
																					_lowerCAmelCase  :							List[str]				     =					np.amax(np.abs(out_a - out_a))
																					self.assertLessEqual(UpperCamelCase_,    1E-3)







							def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a=None,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					self.get_vision_text_model(UpperCamelCase_,    UpperCamelCase_)
														_lowerCAmelCase  :							Optional[int]				     =					{'vision_model': vision_model, 'text_model': text_model}
														_lowerCAmelCase  :							Optional[int]				     =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase_)

														_lowerCAmelCase  :							Tuple				     =					model(
														    input_ids=UpperCamelCase_,    pixel_values=UpperCamelCase_,    attention_mask=UpperCamelCase_,    output_attentions=UpperCamelCase_)

														_lowerCAmelCase  :							Any				     =					output.vision_model_output.attentions
														self.assertEqual(len(UpperCamelCase_),    vision_config.num_hidden_layers)

														# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
														_lowerCAmelCase  :							str				     =					to_atuple(vision_model.config.image_size)
														_lowerCAmelCase  :							Dict				     =					to_atuple(vision_model.config.patch_size)
														_lowerCAmelCase  :							Tuple				     =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
														_lowerCAmelCase  :							int				     =					num_patches + 1
														self.assertEqual(vision_attentions[0].shape[-3:],    (vision_config.num_attention_heads, seq_len, seq_len))

														_lowerCAmelCase  :							int				     =					output.text_model_output.attentions
														self.assertEqual(len(UpperCamelCase_),    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):

														'''simple docstring'''

														pt_model.to(UpperCamelCase_)
														pt_model.eval()

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

														with torch.no_grad():
																					_lowerCAmelCase  :							Tuple				     =					pt_model(**UpperCamelCase_).to_tuple()

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

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

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

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

														pt_model_loaded.to(UpperCamelCase_)
														pt_model_loaded.eval()

														with torch.no_grad():
																					_lowerCAmelCase  :							List[str]				     =					pt_model_loaded(**UpperCamelCase_).to_tuple()

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







							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase_,    UpperCamelCase_)

														_lowerCAmelCase  :							Union[str, Any]				     =					VisionTextDualEncoderModel(UpperCamelCase_)
														_lowerCAmelCase  :							Optional[int]				     =					FlaxVisionTextDualEncoderModel(UpperCamelCase_)

														_lowerCAmelCase  :							str				     =					convert_pytorch_state_dict_to_flax(pt_model.state_dict(),    UpperCamelCase_)
														_lowerCAmelCase  :							List[Any]				     =					fx_state

														self.check_pt_flax_equivalence(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)







							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase_,    UpperCamelCase_)

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

														_lowerCAmelCase  :							Tuple				     =					load_flax_weights_in_pytorch_model(UpperCamelCase_,    fx_model.params)

														self.check_pt_flax_equivalence(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.prepare_config_and_inputs()
														self.check_model_from_pretrained_configs(**UpperCamelCase_)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					self.prepare_config_and_inputs()
														self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase_)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					self.prepare_config_and_inputs()
														self.check_save_load(**UpperCamelCase_)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.prepare_config_and_inputs()
														self.check_vision_text_output_attention(**UpperCamelCase_)







							@is_pt_flax_cross_test
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.prepare_config_and_inputs()
														_lowerCAmelCase  :							List[Any]				     =					config_inputs_dict.pop("vision_config")
														_lowerCAmelCase  :							List[str]				     =					config_inputs_dict.pop("text_config")

														_lowerCAmelCase  :							List[Any]				     =					config_inputs_dict

														self.check_equivalence_pt_to_flax(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)
														self.check_equivalence_flax_to_pt(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					self.get_pretrained_model_and_inputs()

														_lowerCAmelCase  :							int				     =					model_a(**UpperCamelCase_)
														_lowerCAmelCase  :							int				     =					outputs[0]

														with tempfile.TemporaryDirectory() as tmp_dirname:
																					model_a.save_pretrained(UpperCamelCase_)
																					_lowerCAmelCase  :							Union[str, Any]				     =					FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase_)

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



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







							def      snake_case__   (     self):

														'''simple docstring'''

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

														return model, inputs







							def      snake_case__   (     self,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					FlaxViTModel(UpperCamelCase_)
														_lowerCAmelCase  :							Optional[Any]				     =					FlaxBertModel(UpperCamelCase_)
														return vision_model, text_model







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					FlaxViTModelTester(self)
														_lowerCAmelCase  :							Optional[int]				     =					FlaxBertModelTester(self)
														_lowerCAmelCase  :							List[Any]				     =					vit_model_tester.prepare_config_and_inputs()
														_lowerCAmelCase  :							Any				     =					bert_model_tester.prepare_config_and_inputs()

														_lowerCAmelCase  :							Optional[Any]				     =					vision_config_and_inputs

														_lowerCAmelCase  :							Any				     =					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):







							def      snake_case__   (     self):

														'''simple docstring'''

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

														return model, inputs







							def      snake_case__   (     self,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					FlaxCLIPVisionModel(UpperCamelCase_)
														_lowerCAmelCase  :							str				     =					FlaxBertModel(UpperCamelCase_)
														return vision_model, text_model







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					FlaxCLIPVisionModelTester(self)
														_lowerCAmelCase  :							Optional[Any]				     =					FlaxBertModelTester(self)
														_lowerCAmelCase  :							Any				     =					clip_model_tester.prepare_config_and_inputs()
														_lowerCAmelCase  :							str				     =					bert_model_tester.prepare_config_and_inputs()

														_lowerCAmelCase  :							Tuple				     =					vision_config_and_inputs

														_lowerCAmelCase  :							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_flax
@require_vision
class 						UpperCAmelCase_					( unittest.TestCase):







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

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

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

														_lowerCAmelCase  :							Dict				     =					model(**UpperCamelCase_)

														# 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  :							Union[str, Any]				     =					np.array([[1.2_284_727, 0.3_104_122]])

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











import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					BeautifulSoup(requests.get(_lowerCamelCase       ,				params=_lowerCamelCase						).content       ,				"html.parser"						)
							_lowerCAmelCase  :							Any				     =					soup.find("div"       ,				attrs={"class": "gs_ri"}						)
							_lowerCAmelCase  :							str				     =					div.find("div"       ,				attrs={"class": "gs_fl"}						).find_all("a"						)
							return anchors[2].get_text()


if __name__ == "__main__":
						_snake_case	=						{
						    "title": (
						        "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
						        "capacitance, volumetric capacitance, and energy density"
						    ),
						    "journal": "Chem. Mater.",
						    "volume": 30,
						    "pages": "3979-3990",
						    "year": 2018,
						    "hl": "en",
						}
						print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
