
'''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__    (    lowercase :					Union[str, Any]				)		->		Any:





     """simple docstring"""

     if isinstance(SCREAMING_SNAKE_CASE_, collections.abc.Iterable				):
          return x
     return (x, x)
@require_flax
class     __lowerCAmelCase					:






       """simple docstring"""





       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Dict					)   ->					List[str]:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            pass





       def snake_case__						(							self       :		Any					)   ->					Dict:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            _UpperCamelCase       =	np.abs((a - b)					).max()
            self.assertLessEqual(_lowercase	,			_lowercase	,			f"""Difference between torch and flax is {diff} (>= {tol})."""					)





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Optional[Any]=None	,			**lowerCAmelCase__       :		Tuple					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	VisionTextDualEncoderConfig.from_vision_text_configs(_lowercase	,			_lowercase					)

            _UpperCamelCase       =	FlaxVisionTextDualEncoderModel(_lowercase					)

            _UpperCamelCase       =	model(input_ids=_lowercase	,			pixel_values=_lowercase	,			attention_mask=_lowercase					)

            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       :		Optional[int]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Tuple=None	,			**lowerCAmelCase__       :		List[str]					)   ->					List[Any]:

            '''simple docstring'''




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

            _UpperCamelCase       =	model(input_ids=_lowercase	,			pixel_values=_lowercase	,			attention_mask=_lowercase					)

            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       :		Dict	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Any=None	,			**lowerCAmelCase__       :		str					)   ->					int:

            '''simple docstring'''




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

            _UpperCamelCase       =	model(input_ids=_lowercase	,			pixel_values=_lowercase	,			attention_mask=_lowercase					)
            _UpperCamelCase       =	output[0]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(_lowercase					)
                 _UpperCamelCase       =	FlaxVisionTextDualEncoderModel.from_pretrained(_lowercase					)

                 _UpperCamelCase       =	model(input_ids=_lowercase	,			pixel_values=_lowercase	,			attention_mask=_lowercase					)
                 _UpperCamelCase       =	after_output[0]
                 _UpperCamelCase       =	np.amax(np.abs(out_a - out_a					)					)
                 self.assertLessEqual(_lowercase	,			1e-3					)





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		str=None	,			**lowerCAmelCase__       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




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

            _UpperCamelCase       =	model(
                input_ids=_lowercase	,			pixel_values=_lowercase	,			attention_mask=_lowercase	,			output_attentions=_lowercase					)

            _UpperCamelCase       =	output.vision_model_output.attentions
            self.assertEqual(len(_lowercase					)	,			vision_config.num_hidden_layers					)

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

            _UpperCamelCase       =	output.text_model_output.attentions
            self.assertEqual(len(_lowercase					)	,			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       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		List[str]					)   ->					Optional[Any]:

            '''simple docstring'''




            pt_model.to(_lowercase					)
            pt_model.eval()

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

            with torch.no_grad():
                 _UpperCamelCase       =	pt_model(**_lowercase					).to_tuple()

            _UpperCamelCase       =	fx_model(**_lowercase					).to_tuple()
            self.assertEqual(len(_lowercase					)	,			len(_lowercase					)	,			'''Output lengths differ between Flax and PyTorch'''					)
            for fx_output, pt_output in zip(fx_outputs[:4]	,			pt_outputs[:4]					):
                 self.assert_almost_equals(_lowercase	,			pt_output.numpy()	,			4e-2					)

            # PT -> Flax
            with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(_lowercase					)
                 _UpperCamelCase       =	FlaxVisionTextDualEncoderModel.from_pretrained(_lowercase	,			from_pt=_lowercase					)

            _UpperCamelCase       =	fx_model_loaded(**_lowercase					).to_tuple()
            self.assertEqual(len(_lowercase					)	,			len(_lowercase					)	,			'''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(_lowercase	,			pt_output.numpy()	,			4e-2					)

            # Flax -> PT
            with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(_lowercase					)
                 _UpperCamelCase       =	VisionTextDualEncoderModel.from_pretrained(_lowercase	,			from_flax=_lowercase					)

            pt_model_loaded.to(_lowercase					)
            pt_model_loaded.eval()

            with torch.no_grad():
                 _UpperCamelCase       =	pt_model_loaded(**_lowercase					).to_tuple()

            self.assertEqual(len(_lowercase					)	,			len(_lowercase					)	,			'''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(_lowercase	,			pt_output_loaded.numpy()	,			4e-2					)





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

            '''simple docstring'''




            _UpperCamelCase       =	VisionTextDualEncoderConfig.from_vision_text_configs(_lowercase	,			_lowercase					)

            _UpperCamelCase       =	VisionTextDualEncoderModel(_lowercase					)
            _UpperCamelCase       =	FlaxVisionTextDualEncoderModel(_lowercase					)

            _UpperCamelCase       =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,			_lowercase					)
            _UpperCamelCase       =	fx_state

            self.check_pt_flax_equivalence(_lowercase	,			_lowercase	,			_lowercase					)





       def snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Tuple					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	VisionTextDualEncoderConfig.from_vision_text_configs(_lowercase	,			_lowercase					)

            _UpperCamelCase       =	VisionTextDualEncoderModel(_lowercase					)
            _UpperCamelCase       =	FlaxVisionTextDualEncoderModel(_lowercase					)

            _UpperCamelCase       =	load_flax_weights_in_pytorch_model(_lowercase	,			fx_model.params					)

            self.check_pt_flax_equivalence(_lowercase	,			_lowercase	,			_lowercase					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            self.check_model_from_pretrained_configs(**_lowercase					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            self.check_vision_text_dual_encoder_from_pretrained(**_lowercase					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            self.check_save_load(**_lowercase					)





       def snake_case__						(							self       :		Dict					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            self.check_vision_text_output_attention(**_lowercase					)





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

            '''simple docstring'''




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

            _UpperCamelCase       =	config_inputs_dict

            self.check_equivalence_pt_to_flax(_lowercase	,			_lowercase	,			_lowercase					)
            self.check_equivalence_flax_to_pt(_lowercase	,			_lowercase	,			_lowercase					)







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

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.get_pretrained_model_and_inputs()

            _UpperCamelCase       =	model_a(**_lowercase					)
            _UpperCamelCase       =	outputs[0]

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 model_a.save_pretrained(_lowercase					)
                 _UpperCamelCase       =	FlaxVisionTextDualEncoderModel.from_pretrained(_lowercase					)

                 _UpperCamelCase       =	model_a(**_lowercase					)
                 _UpperCamelCase       =	after_outputs[0]
                 _UpperCamelCase       =	np.amax(np.abs(out_a - out_a					)					)
                 self.assertLessEqual(_lowercase	,			1e-5					)

@require_flax
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Tuple					)   ->					Any:

            '''simple docstring'''




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

            return model, inputs





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

            '''simple docstring'''




            _UpperCamelCase       =	FlaxViTModel(_lowercase					)
            _UpperCamelCase       =	FlaxBertModel(_lowercase					)
            return vision_model, text_model







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

            '''simple docstring'''




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

            _UpperCamelCase   ,    _UpperCamelCase       =	vision_config_and_inputs

            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	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     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""





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

            '''simple docstring'''




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

            return model, inputs





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

            '''simple docstring'''




            _UpperCamelCase       =	FlaxCLIPVisionModel(_lowercase					)
            _UpperCamelCase       =	FlaxBertModel(_lowercase					)
            return vision_model, text_model







       def snake_case__						(							self       :		Tuple					)   ->					Any:

            '''simple docstring'''




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

            _UpperCamelCase   ,    _UpperCamelCase       =	vision_config_and_inputs

            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	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     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       @slow
       def snake_case__						(							self       :		int					)   ->					str:

            '''simple docstring'''




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

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

            _UpperCamelCase       =	model(**_lowercase					)

            # 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       =	np.array([[1.2284727, 0.3104122]]					)

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










'''simple docstring'''

import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


lowercase__						:      List[Any]	=    parse(importlib.metadata.version('torch'))







def  a__    (    lowercase :					Union[str, Version], lowercase :					str, lowercase :					str				)		->		List[str]:





     """simple docstring"""

     if operation not in STR_OPERATION_TO_FUNC.keys():
          raise ValueError(F"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()				)}, received {operation}"""				)
     _UpperCamelCase       =	STR_OPERATION_TO_FUNC[operation]
     if isinstance(lowercase, lowercase				):
          _UpperCamelCase       =	parse(importlib.metadata.version(lowercase				)				)
     return operation(lowercase, parse(lowercase				)				)







def  a__    (    lowercase :					str, lowercase :					str				)		->		List[Any]:





     """simple docstring"""

     return compare_versions(lowercase, lowercase, lowercase				)



