




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 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Dict:
   if isinstance(__SCREAMING_SNAKE_CASE						,collections.abc.Iterable     ):
      return x
   return (x, x)




@require_flax
class 						__lowercase		:




      """simple docstring"""





      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				Tuple:
         pass

      def 	UpperCAmelCase      (      self	)		->				int:
         pass

      def 	UpperCAmelCase      (      self	)		->				Tuple:
         pass

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

      def 	UpperCAmelCase      (      self				,      A				,      A				,      A				,      A				,      A=None				,      **A	)		->				str:
         snake_case      :				List[Any]				=       VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__				,      UpperCamelCase__	)

         snake_case      :				Tuple				=       FlaxVisionTextDualEncoderModel(UpperCamelCase__	)

         snake_case      :				List[Any]				=       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 	UpperCAmelCase      (      self				,      A				,      A				,      A				,      A				,      A=None				,      **A	)		->				Union[str, Any]:
         snake_case      :				Dict				=       self.get_vision_text_model(UpperCamelCase__				,      UpperCamelCase__	)
         snake_case      :				Optional[Any]				=       {"vision_model": vision_model, "text_model": text_model}
         snake_case      :				Optional[Any]				=       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__	)

         snake_case      :				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 	UpperCAmelCase      (      self				,      A				,      A				,      A				,      A				,      A=None				,      **A	)		->				Tuple:
         snake_case      :				List[Any]				=       self.get_vision_text_model(UpperCamelCase__				,      UpperCamelCase__	)
         snake_case      :				Dict				=       {"vision_model": vision_model, "text_model": text_model}
         snake_case      :				Optional[Any]				=       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__	)

         snake_case      :				int				=       model(input_ids=UpperCamelCase__				,      pixel_values=UpperCamelCase__				,      attention_mask=UpperCamelCase__	)
         snake_case      :				Optional[Any]				=       output[0]

         with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(UpperCamelCase__	)
            snake_case      :				List[Any]				=       FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__	)

            snake_case      :				Any				=       model(input_ids=UpperCamelCase__				,      pixel_values=UpperCamelCase__				,      attention_mask=UpperCamelCase__	)
            snake_case      :				Dict				=       after_output[0]
            snake_case      :				List[str]				=       np.amax(np.abs(out_a - out_a	)	)
            self.assertLessEqual(UpperCamelCase__				,      1e-3	)

      def 	UpperCAmelCase      (      self				,      A				,      A				,      A				,      A				,      A=None				,      **A	)		->				int:
         snake_case      :				Tuple				=       self.get_vision_text_model(UpperCamelCase__				,      UpperCamelCase__	)
         snake_case      :				List[str]				=       {"vision_model": vision_model, "text_model": text_model}
         snake_case      :				Tuple				=       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__	)

         snake_case      :				Optional[int]				=       model(
             input_ids=UpperCamelCase__				,      pixel_values=UpperCamelCase__				,      attention_mask=UpperCamelCase__				,      output_attentions=UpperCamelCase__	)

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

         snake_case      :				Optional[Any]				=       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 	UpperCAmelCase      (      self				,      A				,      A				,      A	)		->				str:
         pt_model.to(UpperCamelCase__	)
         pt_model.eval()

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

         with torch.no_grad():
            snake_case      :				List[str]				=       pt_model(**UpperCamelCase__	).to_tuple()

         snake_case      :				List[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__	)
            snake_case      :				Optional[int]				=       FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__				,      from_pt=UpperCamelCase__	)

         snake_case      :				List[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__	)
            snake_case      :				Union[str, Any]				=       VisionTextDualEncoderModel.from_pretrained(UpperCamelCase__				,      from_flax=UpperCamelCase__	)

         pt_model_loaded.to(UpperCamelCase__	)
         pt_model_loaded.eval()

         with torch.no_grad():
            snake_case      :				Union[str, Any]				=       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 	UpperCAmelCase      (      self				,      A				,      A				,      A	)		->				int:
         snake_case      :				Tuple				=       VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__				,      UpperCamelCase__	)

         snake_case      :				Any				=       VisionTextDualEncoderModel(UpperCamelCase__	)
         snake_case      :				List[Any]				=       FlaxVisionTextDualEncoderModel(UpperCamelCase__	)

         snake_case      :				str				=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,      UpperCamelCase__	)
         snake_case      :				int				=       fx_state

         self.check_pt_flax_equivalence(UpperCamelCase__				,      UpperCamelCase__				,      UpperCamelCase__	)

      def 	UpperCAmelCase      (      self				,      A				,      A				,      A	)		->				str:
         snake_case      :				List[Any]				=       VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__				,      UpperCamelCase__	)

         snake_case      :				Any				=       VisionTextDualEncoderModel(UpperCamelCase__	)
         snake_case      :				Optional[int]				=       FlaxVisionTextDualEncoderModel(UpperCamelCase__	)

         snake_case      :				Dict				=       load_flax_weights_in_pytorch_model(UpperCamelCase__				,      fx_model.params	)

         self.check_pt_flax_equivalence(UpperCamelCase__				,      UpperCamelCase__				,      UpperCamelCase__	)

      def 	UpperCAmelCase      (      self	)		->				int:
         snake_case      :				str				=       self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**UpperCamelCase__	)

      def 	UpperCAmelCase      (      self	)		->				str:
         snake_case      :				Dict				=       self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase__	)

      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         snake_case      :				Tuple				=       self.prepare_config_and_inputs()
         self.check_save_load(**UpperCamelCase__	)

      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         snake_case      :				Optional[Any]				=       self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**UpperCamelCase__	)

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

         snake_case      :				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 	UpperCAmelCase      (      self	)		->				List[Any]:
         snake_case      :				List[str]				=       self.get_pretrained_model_and_inputs()

         snake_case      :				Tuple				=       model_a(**UpperCamelCase__	)
         snake_case      :				Dict				=       outputs[0]

         with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(UpperCamelCase__	)
            snake_case      :				List[Any]				=       FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__	)

            snake_case      :				Tuple				=       model_a(**UpperCamelCase__	)
            snake_case      :				Optional[Any]				=       after_outputs[0]
            snake_case      :				Tuple				=       np.amax(np.abs(out_a - out_a	)	)
            self.assertLessEqual(UpperCamelCase__				,      1e-5	)




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




      """simple docstring"""





      def 	UpperCAmelCase      (      self	)		->				Dict:
         snake_case      :				int				=       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
             """hf-internal-testing/tiny-random-vit"""				,      """hf-internal-testing/tiny-bert"""				,      vision_from_pt=UpperCamelCase__				,      text_from_pt=UpperCamelCase__				,      )
         snake_case      :				str				=       1_3
         snake_case      :				Union[str, Any]				=       floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ]	)
         snake_case      :				Dict				=       ids_tensor([batch_size, 4]				,      model.config.text_config.vocab_size	)
         snake_case      :				str				=       random_attention_mask([batch_size, 4]	)
         snake_case      :				List[str]				=       {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

         return model, inputs

      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				Any:
         snake_case      :				str				=       FlaxViTModel(UpperCamelCase__	)
         snake_case      :				int				=       FlaxBertModel(UpperCamelCase__	)
         return vision_model, text_model

      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         snake_case      :				List[str]				=       FlaxViTModelTester(self	)
         snake_case      :				Any				=       FlaxBertModelTester(self	)
         snake_case      :				Optional[Any]				=       vit_model_tester.prepare_config_and_inputs()
         snake_case      :				str				=       bert_model_tester.prepare_config_and_inputs()

         snake_case      :				Optional[int]				=       vision_config_and_inputs

         snake_case      :				str				=       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		(__snake_case     ,		unittest.TestCase							):




      """simple docstring"""





      def 	UpperCAmelCase      (      self	)		->				Tuple:
         snake_case      :				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__				,      )
         snake_case      :				int				=       1_3
         snake_case      :				Dict				=       floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ]	)
         snake_case      :				Optional[int]				=       ids_tensor([batch_size, 4]				,      model.config.text_config.vocab_size	)
         snake_case      :				str				=       random_attention_mask([batch_size, 4]	)
         snake_case      :				str				=       {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

         return model, inputs

      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				Union[str, Any]:
         snake_case      :				Union[str, Any]				=       FlaxCLIPVisionModel(UpperCamelCase__	)
         snake_case      :				Tuple				=       FlaxBertModel(UpperCamelCase__	)
         return vision_model, text_model

      def 	UpperCAmelCase      (      self	)		->				Dict:
         snake_case      :				Optional[int]				=       FlaxCLIPVisionModelTester(self	)
         snake_case      :				Union[str, Any]				=       FlaxBertModelTester(self	)
         snake_case      :				Optional[Any]				=       clip_model_tester.prepare_config_and_inputs()
         snake_case      :				Dict				=       bert_model_tester.prepare_config_and_inputs()

         snake_case      :				Optional[Any]				=       vision_config_and_inputs

         snake_case      :				Optional[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_flax
@require_vision
class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





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

         snake_case      :				List[str]				=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
         snake_case      :				str				=       processor(
             text=["""una foto di un gatto""", """una foto di un cane"""]				,      images=UpperCamelCase__				,      padding=UpperCamelCase__				,      return_tensors="""np"""	)

         snake_case      :				List[str]				=       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])				,      )

         snake_case      :				str				=       np.array([[1.2_28_47_27, 0.3_10_41_22]]	)

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








"""simple docstring"""


from __future__ import annotations






def     a__    (    __SCREAMING_SNAKE_CASE )	->	bool:
		__lowerCAmelCase:       Tuple          =			str(__SCREAMING_SNAKE_CASE )
		return len(__SCREAMING_SNAKE_CASE ) == 9 and set(__SCREAMING_SNAKE_CASE ) == set("123456789" )






def     a__    (    )	->	int | None:
		for base_num in range(9_9_9_9					, 4_9_9_9					, -1 ):
				__lowerCAmelCase:       Tuple          =			1_0_0_0_0_2 * base_num
				if is_9_pandigital(__SCREAMING_SNAKE_CASE ):
						return candidate

		for base_num in range(3_3_3					, 9_9					, -1 ):
				__lowerCAmelCase:       int          =			1_0_0_2_0_0_3 * base_num
				if is_9_pandigital(__SCREAMING_SNAKE_CASE ):
						return candidate

		return None


if __name__ == "__main__":
			print(F'''{solution() = }''')


