





'''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    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]				):
			if isinstance(lowerCamelCase_		,       collections.abc.Iterable				):
						return x
			return (x, x)






@require_flax
class 							__lowercase       :







	'''simple docstring'''

	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				pass


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				pass


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







				'''simple docstring'''





				pass


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							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 )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase     ,_lowerCamelCase )

				__lowercase										=							FlaxVisionTextDualEncoderModel(_lowerCamelCase )

				__lowercase										=							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 )							->							Any:







				'''simple docstring'''





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

				__lowercase										=							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 )							->							str:







				'''simple docstring'''





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

				__lowercase										=							model(input_ids=_lowerCamelCase     ,pixel_values=_lowerCamelCase     ,attention_mask=_lowerCamelCase )
				__lowercase										=							output[0]

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(_lowerCamelCase )
							__lowercase										=							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )

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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase=None     ,**_lowerCamelCase )							->							Dict:







				'''simple docstring'''





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

				__lowercase										=							model(
				    input_ids=_lowerCamelCase     ,pixel_values=_lowerCamelCase     ,attention_mask=_lowerCamelCase     ,output_attentions=_lowerCamelCase )

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

				__lowercase										=							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 )							->							Optional[Any]:







				'''simple docstring'''





				pt_model.to(_lowerCamelCase )
				pt_model.eval()

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

				with torch.no_grad():
							__lowercase										=							pt_model(**_lowerCamelCase ).to_tuple()

				__lowercase										=							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 )
							__lowercase										=							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase     ,from_pt=_lowerCamelCase )

				__lowercase										=							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 )
							__lowercase										=							VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase     ,from_flax=_lowerCamelCase )

				pt_model_loaded.to(_lowerCamelCase )
				pt_model_loaded.eval()

				with torch.no_grad():
							__lowercase										=							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 )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase     ,_lowerCamelCase )

				__lowercase										=							VisionTextDualEncoderModel(_lowerCamelCase )
				__lowercase										=							FlaxVisionTextDualEncoderModel(_lowerCamelCase )

				__lowercase										=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,_lowerCamelCase )
				__lowercase										=							fx_state

				self.check_pt_flax_equivalence(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase     ,_lowerCamelCase )

				__lowercase										=							VisionTextDualEncoderModel(_lowerCamelCase )
				__lowercase										=							FlaxVisionTextDualEncoderModel(_lowerCamelCase )

				__lowercase										=							load_flax_weights_in_pytorch_model(_lowerCamelCase     ,fx_model.params )

				self.check_pt_flax_equivalence(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							self.prepare_config_and_inputs()
				self.check_model_from_pretrained_configs(**_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							self.prepare_config_and_inputs()
				self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase )


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







				'''simple docstring'''





				__lowercase										=							self.prepare_config_and_inputs()
				self.check_save_load(**_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.prepare_config_and_inputs()
				self.check_vision_text_output_attention(**_lowerCamelCase )


	@is_pt_flax_cross_test
	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





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

				__lowercase										=							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 )							->							str:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							self.get_pretrained_model_and_inputs()

				__lowercase										=							model_a(**_lowerCamelCase )
				__lowercase										=							outputs[0]

				with tempfile.TemporaryDirectory() as tmp_dirname:
							model_a.save_pretrained(_lowerCamelCase )
							__lowercase										=							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )

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






@require_flax
class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





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

				return model, inputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							FlaxViTModel(_lowerCamelCase )
				__lowercase										=							FlaxBertModel(_lowerCamelCase )
				return vision_model, text_model


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





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

				__lowercase						,		__lowercase										=							vision_config_and_inputs

				__lowercase						,		__lowercase						,		__lowercase						,		__lowercase										=							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 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





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

				return model, inputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							FlaxCLIPVisionModel(_lowerCamelCase )
				__lowercase										=							FlaxBertModel(_lowerCamelCase )
				return vision_model, text_model


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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

				__lowercase						,		__lowercase										=							vision_config_and_inputs

				__lowercase						,		__lowercase						,		__lowercase						,		__lowercase										=							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 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	@slow
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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

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

				__lowercase										=							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])     ,)

				__lowercase										=							np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )

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












'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE					=    {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''VanForImageClassification''',
	    '''VanModel''',
	    '''VanPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)





