"""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   (		A      ):






     '''simple docstring'''



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






@require_flax
class    a__    :


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							np.abs((a - b)  ).max()
        self.assertLessEqual(_UpperCAmelCase,				_UpperCAmelCase,				F'''Difference between torch and flax is {diff} (>= {tol}).'''  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							FlaxVisionTextDualEncoderModel(_UpperCAmelCase  )

        lowercase__				 =							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,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.get_vision_text_model(_UpperCAmelCase,				_UpperCAmelCase  )
        lowercase__				 =							{"vision_model": vision_model, "text_model": text_model}
        lowercase__				 =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCAmelCase  )

        lowercase__				 =							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,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.get_vision_text_model(_UpperCAmelCase,				_UpperCAmelCase  )
        lowercase__				 =							{"vision_model": vision_model, "text_model": text_model}
        lowercase__				 =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCAmelCase  )

        lowercase__				 =							model(input_ids=_UpperCAmelCase,				pixel_values=_UpperCAmelCase,				attention_mask=_UpperCAmelCase  )
        lowercase__				 =							output[0]

        with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(_UpperCAmelCase  )
             lowercase__				 =							FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCAmelCase  )

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.get_vision_text_model(_UpperCAmelCase,				_UpperCAmelCase  )
        lowercase__				 =							{"vision_model": vision_model, "text_model": text_model}
        lowercase__				 =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCAmelCase  )

        lowercase__				 =							model(
            input_ids=_UpperCAmelCase,				pixel_values=_UpperCAmelCase,				attention_mask=_UpperCAmelCase,				output_attentions=_UpperCAmelCase  )

        lowercase__				 =							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)
        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(_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,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        pt_model.to(_UpperCAmelCase  )
        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(**_UpperCAmelCase  ).to_tuple()

        lowercase__				 =							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  )
             lowercase__				 =							FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCAmelCase,				from_pt=_UpperCAmelCase  )

        lowercase__				 =							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  )
             lowercase__				 =							VisionTextDualEncoderModel.from_pretrained(_UpperCAmelCase,				from_flax=_UpperCAmelCase  )

        pt_model_loaded.to(_UpperCAmelCase  )
        pt_model_loaded.eval()

        with torch.no_grad():
             lowercase__				 =							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,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							VisionTextDualEncoderModel(_UpperCAmelCase  )
        lowercase__				 =							FlaxVisionTextDualEncoderModel(_UpperCAmelCase  )

        lowercase__				 =							convert_pytorch_state_dict_to_flax(pt_model.state_dict(),				_UpperCAmelCase  )
        lowercase__				 =							fx_state

        self.check_pt_flax_equivalence(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCAmelCase,				_UpperCAmelCase  )

        lowercase__				 =							VisionTextDualEncoderModel(_UpperCAmelCase  )
        lowercase__				 =							FlaxVisionTextDualEncoderModel(_UpperCAmelCase  )

        lowercase__				 =							load_flax_weights_in_pytorch_model(_UpperCAmelCase,				fx_model.params  )

        self.check_pt_flax_equivalence(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.prepare_config_and_inputs()
        self.check_model_from_pretrained_configs(**_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.prepare_config_and_inputs()
        self.check_vision_text_dual_encoder_from_pretrained(**_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.prepare_config_and_inputs()
        self.check_save_load(**_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.prepare_config_and_inputs()
        self.check_vision_text_output_attention(**_UpperCAmelCase  )


   @is_pt_flax_cross_test
   def 	snake_case__	(     self  ):
        '''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(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )
        self.check_equivalence_flax_to_pt(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.get_pretrained_model_and_inputs()

        lowercase__				 =							model_a(**_UpperCAmelCase  )
        lowercase__				 =							outputs[0]

        with tempfile.TemporaryDirectory() as tmp_dirname:
             model_a.save_pretrained(_UpperCAmelCase  )
             lowercase__				 =							FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCAmelCase  )

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






@require_flax
class    a__    (   _a					,					unittest.TestCase	):


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
            "hf-internal-testing/tiny-random-vit",				"hf-internal-testing/tiny-bert",				vision_from_pt=_UpperCAmelCase,				text_from_pt=_UpperCAmelCase,				)
        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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							FlaxViTModel(_UpperCAmelCase  )
        lowercase__				 =							FlaxBertModel(_UpperCAmelCase  )
        return vision_model, text_model


   def 	snake_case__	(     self  ):
        '''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    a__    (   _a					,					unittest.TestCase	):


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
            "hf-internal-testing/tiny-random-clip",				"hf-internal-testing/tiny-bert",				vision_from_pt=_UpperCAmelCase,				text_from_pt=_UpperCAmelCase,				)
        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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							FlaxCLIPVisionModel(_UpperCAmelCase  )
        lowercase__				 =							FlaxBertModel(_UpperCAmelCase  )
        return vision_model, text_model


   def 	snake_case__	(     self  ):
        '''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    a__    (   unittest.TestCase	):


   @slow
   def 	snake_case__	(     self  ):
        '''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=_UpperCAmelCase,				padding=_UpperCAmelCase,				return_tensors="np"  )

        lowercase__				 =							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]),				)

        lowercase__				 =							np.array([[1.2_284_727, 0.3_104_122]]  )

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





"""simple docstring"""


import os
import sys

lowerCAmelCase_:					Any     =					os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase_:					Union[str, Any]     =					[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]







@add_start_docstrings(AutoConfig.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoConfig.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoTokenizer.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoTokenizer.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModel.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModel.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForCausalLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForMaskedLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForSequenceClassification.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForQuestionAnswering.from_pretrained(*A    ,				**A      )




