



"""simple docstring"""






from __future__ import annotations

from cmath import sqrt






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int			):
			"""simple docstring"""

			if a == 0:
						raise ValueError('''Coefficient \'a\' must not be zero.'''			)
			lowerCamelCase__	:	Optional[int]				=b * b - 4 * a * c

			lowerCamelCase__	:	List[str]				=(-b + sqrt(__lowerCamelCase			)) / (2 * a)
			lowerCamelCase__	:	int				=(-b - sqrt(__lowerCamelCase			)) / (2 * a)

			return (
			    root_a.real if not root_a.imag else root_a,
			    root_a.real if not root_a.imag else root_a,
			)






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	int				=quadratic_roots(a=5							,	b=6							,	c=1			)
			print(f'''The solutions are: {solutiona} and {solutiona}'''			)


if __name__ == "__main__":
		main()









"""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   snake_case__     (	__lowerCamelCase       :					List[Any]			):
			"""simple docstring"""
			if isinstance(__lowerCamelCase							,	collections.abc.Iterable			):
						return x
			return (x, x)

@require_flax
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						List[str],						lowerCamelCase							:						Any  )->      Union[str, Any]:
								pass



					def 	snake_case ( self							:						List[str]  )->      List[str]:
								pass



					def 	snake_case ( self							:						Optional[Any]  )->      str:
								pass



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						float  )->      Dict:
								lowerCamelCase__	:	Union[str, Any]				=np.abs((a - b)  ).max()
								self.assertLessEqual(lowerCamelCase,						lowerCamelCase,						F'''Difference between torch and flax is {diff} (>= {tol}).'''  )



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Any,						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict,						lowerCamelCase							:						Any=None,						**lowerCamelCase							:						str  )->      int:
								lowerCamelCase__	:	List[str]				=VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase,						lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=FlaxVisionTextDualEncoderModel(lowerCamelCase  )

								lowerCamelCase__	:	Dict				=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							:						Any,						lowerCamelCase							:						int,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						str=None,						**lowerCamelCase							:						List[Any]  )->      int:
								lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=self.get_vision_text_model(lowerCamelCase,						lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				={'''vision_model''': vision_model, '''text_model''': text_model}
								lowerCamelCase__	:	Tuple				=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase  )

								lowerCamelCase__	:	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							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Dict=None,						**lowerCamelCase							:						int  )->      List[str]:
								lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=self.get_vision_text_model(lowerCamelCase,						lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				={'''vision_model''': vision_model, '''text_model''': text_model}
								lowerCamelCase__	:	int				=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=model(input_ids=lowerCamelCase,						pixel_values=lowerCamelCase,						attention_mask=lowerCamelCase  )
								lowerCamelCase__	:	int				=output[0]

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

											lowerCamelCase__	:	Optional[int]				=model(input_ids=lowerCamelCase,						pixel_values=lowerCamelCase,						attention_mask=lowerCamelCase  )
											lowerCamelCase__	:	List[str]				=after_output[0]
											lowerCamelCase__	:	Any				=np.amax(np.abs(out_a - out_a  )  )
											self.assertLessEqual(lowerCamelCase,						1E-3  )



					def 	snake_case ( self							:						Optional[Any],						lowerCamelCase							:						Dict,						lowerCamelCase							:						str,						lowerCamelCase							:						str,						lowerCamelCase							:						str,						lowerCamelCase							:						List[Any]=None,						**lowerCamelCase							:						List[Any]  )->      Tuple:
								lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=self.get_vision_text_model(lowerCamelCase,						lowerCamelCase  )
								lowerCamelCase__	:	Any				={'''vision_model''': vision_model, '''text_model''': text_model}
								lowerCamelCase__	:	int				=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=model(
								    input_ids=lowerCamelCase,						pixel_values=lowerCamelCase,						attention_mask=lowerCamelCase,						output_attentions=lowerCamelCase  )

								lowerCamelCase__	:	int				=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__	:	Tuple				=to_atuple(vision_model.config.image_size  )
								lowerCamelCase__	:	Optional[Any]				=to_atuple(vision_model.config.patch_size  )
								lowerCamelCase__	:	Union[str, Any]				=(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__	:	List[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							:						Tuple,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Any,						lowerCamelCase							:						Union[str, Any]  )->      Any:
								pt_model.to(lowerCamelCase  )
								pt_model.eval()

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

								with torch.no_grad():
											lowerCamelCase__	:	List[str]				=pt_model(**lowerCamelCase  ).to_tuple()

								lowerCamelCase__	:	Optional[Any]				=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__	:	Optional[int]				=FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase,						from_pt=lowerCamelCase  )

								lowerCamelCase__	:	List[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  )
											lowerCamelCase__	:	str				=VisionTextDualEncoderModel.from_pretrained(lowerCamelCase,						from_flax=lowerCamelCase  )

								pt_model_loaded.to(lowerCamelCase  )
								pt_model_loaded.eval()

								with torch.no_grad():
											lowerCamelCase__	:	List[Any]				=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 	snake_case ( self							:						str,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						str  )->      List[Any]:
								lowerCamelCase__	:	Any				=VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase,						lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=VisionTextDualEncoderModel(lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=FlaxVisionTextDualEncoderModel(lowerCamelCase  )

								lowerCamelCase__	:	str				=convert_pytorch_state_dict_to_flax(pt_model.state_dict(),						lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=fx_state

								self.check_pt_flax_equivalence(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Union[str, Any]  )->      Optional[int]:
								lowerCamelCase__	:	Dict				=VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase,						lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=VisionTextDualEncoderModel(lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=FlaxVisionTextDualEncoderModel(lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=load_flax_weights_in_pytorch_model(lowerCamelCase,						fx_model.params  )

								self.check_pt_flax_equivalence(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[int]  )->      Union[str, Any]:
								lowerCamelCase__	:	Any				=self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**lowerCamelCase  )



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	int				=self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase  )



					def 	snake_case ( self							:						Tuple  )->      Any:
								lowerCamelCase__	:	Tuple				=self.prepare_config_and_inputs()
								self.check_save_load(**lowerCamelCase  )



					def 	snake_case ( self							:						str  )->      Any:
								lowerCamelCase__	:	str				=self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**lowerCamelCase  )



					@is_pt_flax_cross_test
					def 	snake_case ( self							:						Tuple  )->      List[Any]:
								lowerCamelCase__	:	Union[str, Any]				=self.prepare_config_and_inputs()
								lowerCamelCase__	:	Union[str, Any]				=config_inputs_dict.pop('''vision_config'''  )
								lowerCamelCase__	:	Optional[Any]				=config_inputs_dict.pop('''text_config'''  )

								lowerCamelCase__	:	Tuple				=config_inputs_dict

								self.check_equivalence_pt_to_flax(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								self.check_equivalence_flax_to_pt(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )




					@slow
					def 	snake_case ( self							:						Optional[Any]  )->      Tuple:
								lowerCamelCase__			,     lowerCamelCase__	:	Dict				=self.get_pretrained_model_and_inputs()

								lowerCamelCase__	:	Optional[int]				=model_a(**lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=outputs[0]

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

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





@require_flax
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[int]  )->      Optional[Any]:
								lowerCamelCase__	:	str				=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__	:	Union[str, Any]				=13
								lowerCamelCase__	:	List[str]				=floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]  )
								lowerCamelCase__	:	List[str]				=ids_tensor([batch_size, 4],						model.config.text_config.vocab_size  )
								lowerCamelCase__	:	Optional[int]				=random_attention_mask([batch_size, 4]  )
								lowerCamelCase__	:	Any				={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

								return model, inputs



					def 	snake_case ( self							:						str,						lowerCamelCase							:						str,						lowerCamelCase							:						int  )->      int:
								lowerCamelCase__	:	str				=FlaxViTModel(lowerCamelCase  )
								lowerCamelCase__	:	Any				=FlaxBertModel(lowerCamelCase  )
								return vision_model, text_model




					def 	snake_case ( self							:						int  )->      Optional[int]:
								lowerCamelCase__	:	Any				=FlaxViTModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=FlaxBertModelTester(self  )
								lowerCamelCase__	:	Any				=vit_model_tester.prepare_config_and_inputs()
								lowerCamelCase__	:	Optional[Any]				=bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase__			,     lowerCamelCase__	:	Any				=vision_config_and_inputs

								lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__			,     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_torch
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[int]  )->      Optional[int]:
								lowerCamelCase__	:	Union[str, Any]				=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__	:	Union[str, Any]				=13
								lowerCamelCase__	:	Optional[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__	:	str				=random_attention_mask([batch_size, 4]  )
								lowerCamelCase__	:	Optional[int]				={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

								return model, inputs



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Any,						lowerCamelCase							:						Dict  )->      Dict:
								lowerCamelCase__	:	str				=FlaxCLIPVisionModel(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=FlaxBertModel(lowerCamelCase  )
								return vision_model, text_model




					def 	snake_case ( self							:						Optional[int]  )->      Optional[Any]:
								lowerCamelCase__	:	List[Any]				=FlaxCLIPVisionModelTester(self  )
								lowerCamelCase__	:	List[Any]				=FlaxBertModelTester(self  )
								lowerCamelCase__	:	Any				=clip_model_tester.prepare_config_and_inputs()
								lowerCamelCase__	:	Optional[int]				=bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=vision_config_and_inputs

								lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=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  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					@slow
					def 	snake_case ( self							:						Tuple  )->      Optional[Any]:
								lowerCamelCase__	:	Any				=FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian''',						logit_scale_init_value=1.0  )
								lowerCamelCase__	:	List[Any]				=VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''  )

								lowerCamelCase__	:	Optional[int]				=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								lowerCamelCase__	:	Dict				=processor(
								    text=['''una foto di un gatto''', '''una foto di un cane'''],						images=lowerCamelCase,						padding=lowerCamelCase,						return_tensors='''np'''  )

								lowerCamelCase__	:	List[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]),						)

								lowerCamelCase__	:	Any				=np.array([[1.2_284_727, 0.3_104_122]]  )

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




