




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      (      _SCREAMING_SNAKE_CASE    )	-> List[Any]:
       if isinstance(_SCREAMING_SNAKE_CASE ,collections.abc.Iterable    ):
              return x
       return (x, x)




@require_flax
class 		UpperCamelCase__	:

    '''simple docstring'''



    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[str]:
           pass







    def     _lowercase     (    self     )     ->      Any:
           pass







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







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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      Tuple			 =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      int			 =					FlaxVisionTextDualEncoderModel(UpperCamelCase__     )

           lowerCamelCase   :      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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      Optional[int]			 =					self.get_vision_text_model(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					{"vision_model": vision_model, "text_model": text_model}
           lowerCamelCase   :      Tuple			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__     )

           lowerCamelCase   :      Any			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      str			 =					self.get_vision_text_model(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					{"vision_model": vision_model, "text_model": text_model}
           lowerCamelCase   :      Union[str, Any]			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__     )

           lowerCamelCase   :      Union[str, Any]			 =					model(input_ids=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     )
           lowerCamelCase   :      int			 =					output[0]

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

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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Union[str, Any]:
           lowerCamelCase ,      lowerCamelCase   :      Dict			 =					self.get_vision_text_model(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					{"vision_model": vision_model, "text_model": text_model}
           lowerCamelCase   :      Tuple			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__     )

           lowerCamelCase   :      Union[str, Any]			 =					model(
               input_ids=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     ,  output_attentions=UpperCamelCase__     )

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

           lowerCamelCase   :      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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           pt_model.to(UpperCamelCase__     )
           pt_model.eval()

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

           with torch.no_grad():
                  lowerCamelCase   :      int			 =					pt_model(**UpperCamelCase__     ).to_tuple()

           lowerCamelCase   :      Any			 =					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__     )
                  lowerCamelCase   :      List[str]			 =					FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__     ,  from_pt=UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					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__     )
                  lowerCamelCase   :      Any			 =					VisionTextDualEncoderModel.from_pretrained(UpperCamelCase__     ,  from_flax=UpperCamelCase__     )

           pt_model_loaded.to(UpperCamelCase__     )
           pt_model_loaded.eval()

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

           lowerCamelCase   :      str			 =					VisionTextDualEncoderModel(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					FlaxVisionTextDualEncoderModel(UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,  UpperCamelCase__     )
           lowerCamelCase   :      List[Any]			 =					fx_state

           self.check_pt_flax_equivalence(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      Any			 =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      int			 =					VisionTextDualEncoderModel(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					FlaxVisionTextDualEncoderModel(UpperCamelCase__     )

           lowerCamelCase   :      Any			 =					load_flax_weights_in_pytorch_model(UpperCamelCase__     ,  fx_model.params     )

           self.check_pt_flax_equivalence(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[Any]			 =					self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      Any			 =					self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      List[str]			 =					self.prepare_config_and_inputs()
           self.check_save_load(**UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Any			 =					self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**UpperCamelCase__     )







    @is_pt_flax_cross_test
    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      int			 =					self.prepare_config_and_inputs()
           lowerCamelCase   :      Union[str, Any]			 =					config_inputs_dict.pop("vision_config"     )
           lowerCamelCase   :      Tuple			 =					config_inputs_dict.pop("text_config"     )

           lowerCamelCase   :      Dict			 =					config_inputs_dict

           self.check_equivalence_pt_to_flax(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )
           self.check_equivalence_flax_to_pt(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    @slow
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase ,      lowerCamelCase   :      List[Any]			 =					self.get_pretrained_model_and_inputs()

           lowerCamelCase   :      Any			 =					model_a(**UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					outputs[0]

           with tempfile.TemporaryDirectory() as tmp_dirname:
                  model_a.save_pretrained(UpperCamelCase__     )
                  lowerCamelCase   :      Optional[int]			 =					FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__     )

                  lowerCamelCase   :      int			 =					model_a(**UpperCamelCase__     )
                  lowerCamelCase   :      List[str]			 =					after_outputs[0]
                  lowerCamelCase   :      Tuple			 =					np.amax(np.abs(out_a - out_a     )     )
                  self.assertLessEqual(UpperCamelCase__     ,  1e-5     )




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

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      List[Any]			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               "hf-internal-testing/tiny-random-vit"     ,  "hf-internal-testing/tiny-bert"     ,  vision_from_pt=UpperCamelCase__     ,  text_from_pt=UpperCamelCase__     ,  )
           lowerCamelCase   :      str			 =					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   :      Dict			 =					ids_tensor([batch_size, 4]     ,  model.config.text_config.vocab_size     )
           lowerCamelCase   :      Dict			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           lowerCamelCase   :      Dict			 =					FlaxViTModel(UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					FlaxBertModel(UpperCamelCase__     )
           return vision_model, text_model







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

           lowerCamelCase ,      lowerCamelCase   :      Optional[int]			 =					vision_config_and_inputs

           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      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_torch
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      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__     ,  )
           lowerCamelCase   :      Optional[int]			 =					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   :      Tuple			 =					ids_tensor([batch_size, 4]     ,  model.config.text_config.vocab_size     )
           lowerCamelCase   :      Union[str, Any]			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      str:
           lowerCamelCase   :      List[Any]			 =					FlaxCLIPVisionModel(UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					FlaxBertModel(UpperCamelCase__     )
           return vision_model, text_model







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

           lowerCamelCase ,      lowerCamelCase   :      Any			 =					vision_config_and_inputs

           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      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 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @slow
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Tuple			 =					FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"     ,  logit_scale_init_value=1.0     )
           lowerCamelCase   :      Optional[int]			 =					VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"     )

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

           lowerCamelCase   :      Dict			 =					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])     ,  )

           lowerCamelCase   :      Dict			 =					np.array([[1.2284727, 0.3104122]]     )

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












def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       lowerCamelCase   :      str			 =					(boundary[1] - boundary[0]) / steps
       lowerCamelCase   :      List[str]			 =					boundary[0]
       lowerCamelCase   :      Union[str, Any]			 =					boundary[1]
       lowerCamelCase   :      int			 =					make_points(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					0.0
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       for i in x_i:
              # print(i)
              y += h * f(_SCREAMING_SNAKE_CASE    )
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       return y

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       lowerCamelCase   :      int			 =					a + h
       while x < (b - h):
              yield x
              lowerCamelCase   :      List[str]			 =					x + h

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:  # enter your function here
       lowerCamelCase   :      str			 =					(x - 0) * (x - 0)
       return y

def      A      (      )	-> int:
       lowerCamelCase   :      int			 =					0.0  # Lower bound of integration
       lowerCamelCase   :      int			 =					1.0  # Upper bound of integration
       lowerCamelCase   :      Dict			 =					10.0  # define number of steps or resolution
       lowerCamelCase   :      int			 =					[a, b]  # define boundary of integration
       lowerCamelCase   :      str			 =					method_a(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       print(f'''y = {y}'''    )


if __name__ == "__main__":
       main()






