


"""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__	:							Union[str, Any]  ):
 if isinstance(lowerCamelCase__       ,		collections.abc.Iterable  ):
  return x
 return (x, x)

@require_flax
class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							int:
    pass


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


   def 					UpperCAmelCase__(						self						)      ->							int:
    pass


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Any:
    lowercase__       :       Optional[Any]				=						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__						)      ->							Dict:
    lowercase__       :       Any				=						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__ ,     lowerCamelCase__						)

    lowercase__       :       Any				=						FlaxVisionTextDualEncoderModel(lowerCamelCase__						)

    lowercase__       :       Optional[int]				=						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__						)      ->							Tuple:
    lowercase__	,       lowercase__       :       Optional[int]				=						self.get_vision_text_model(lowerCamelCase__ ,     lowerCamelCase__						)
    lowercase__       :       int				=						{"""vision_model""": vision_model, """text_model""": text_model}
    lowercase__       :       Optional[Any]				=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__						)

    lowercase__       :       int				=						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:
    lowercase__	,       lowercase__       :       Dict				=						self.get_vision_text_model(lowerCamelCase__ ,     lowerCamelCase__						)
    lowercase__       :       str				=						{"""vision_model""": vision_model, """text_model""": text_model}
    lowercase__       :       Union[str, Any]				=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__						)

    lowercase__       :       str				=						model(input_ids=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     attention_mask=lowerCamelCase__						)
    lowercase__       :       Tuple				=						output[0]

    with tempfile.TemporaryDirectory() as tmpdirname:
     model.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Optional[int]				=						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__						)

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__=None ,     **lowerCamelCase__						)      ->							Tuple:
    lowercase__	,       lowercase__       :       Optional[Any]				=						self.get_vision_text_model(lowerCamelCase__ ,     lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						{"""vision_model""": vision_model, """text_model""": text_model}
    lowercase__       :       List[str]				=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__						)

    lowercase__       :       Dict				=						model(
        input_ids=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     output_attentions=lowerCamelCase__						)

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

    lowercase__       :       str				=						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__						)      ->							Dict:
    pt_model.to(lowerCamelCase__						)
    pt_model.eval()

    # prepare inputs
    lowercase__       :       List[str]				=						inputs_dict
    lowercase__       :       List[str]				=						{k: torch.tensor(v.tolist()						) for k, v in flax_inputs.items()}

    with torch.no_grad():
     lowercase__       :       Optional[Any]				=						pt_model(**lowerCamelCase__						).to_tuple()

    lowercase__       :       Any				=						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__       :       Optional[Any]				=						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__ ,     from_pt=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						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__       :       str				=						VisionTextDualEncoderModel.from_pretrained(lowerCamelCase__ ,     from_flax=lowerCamelCase__						)

    pt_model_loaded.to(lowerCamelCase__						)
    pt_model_loaded.eval()

    with torch.no_grad():
     lowercase__       :       Optional[int]				=						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__						)      ->							Union[str, Any]:
    lowercase__       :       Optional[int]				=						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__ ,     lowerCamelCase__						)

    lowercase__       :       Dict				=						VisionTextDualEncoderModel(lowerCamelCase__						)
    lowercase__       :       Any				=						FlaxVisionTextDualEncoderModel(lowerCamelCase__						)

    lowercase__       :       Optional[Any]				=						convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,     lowerCamelCase__						)
    lowercase__       :       Tuple				=						fx_state

    self.check_pt_flax_equivalence(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							List[str]:
    lowercase__       :       int				=						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__ ,     lowerCamelCase__						)

    lowercase__       :       str				=						VisionTextDualEncoderModel(lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						FlaxVisionTextDualEncoderModel(lowerCamelCase__						)

    lowercase__       :       Any				=						load_flax_weights_in_pytorch_model(lowerCamelCase__ ,     fx_model.params						)

    self.check_pt_flax_equivalence(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       Any				=						self.prepare_config_and_inputs()
    self.check_model_from_pretrained_configs(**lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       Union[str, Any]				=						self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       Optional[int]				=						self.prepare_config_and_inputs()
    self.check_save_load(**lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       Union[str, Any]				=						self.prepare_config_and_inputs()
    self.check_vision_text_output_attention(**lowerCamelCase__						)


   @is_pt_flax_cross_test
   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       str				=						self.prepare_config_and_inputs()
    lowercase__       :       List[Any]				=						config_inputs_dict.pop("""vision_config"""						)
    lowercase__       :       str				=						config_inputs_dict.pop("""text_config"""						)

    lowercase__       :       str				=						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:
    lowercase__	,       lowercase__       :       Tuple				=						self.get_pretrained_model_and_inputs()

    lowercase__       :       str				=						model_a(**lowerCamelCase__						)
    lowercase__       :       List[str]				=						outputs[0]

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

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

@require_flax
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       List[str]				=						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__       :       List[str]				=						13
    lowercase__       :       Tuple				=						floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]						)
    lowercase__       :       Dict				=						ids_tensor([batch_size, 4] ,     model.config.text_config.vocab_size						)
    lowercase__       :       int				=						random_attention_mask([batch_size, 4]						)
    lowercase__       :       Tuple				=						{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

    return model, inputs


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       int				=						FlaxViTModel(lowerCamelCase__						)
    lowercase__       :       int				=						FlaxBertModel(lowerCamelCase__						)
    return vision_model, text_model







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       List[str]				=						FlaxViTModelTester(self						)
    lowercase__       :       str				=						FlaxBertModelTester(self						)
    lowercase__       :       Dict				=						vit_model_tester.prepare_config_and_inputs()
    lowercase__       :       Dict				=						bert_model_tester.prepare_config_and_inputs()

    lowercase__	,       lowercase__       :       str				=						vision_config_and_inputs

    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       Optional[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_torch
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       str				=						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__       :       Dict				=						13
    lowercase__       :       str				=						floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]						)
    lowercase__       :       List[str]				=						ids_tensor([batch_size, 4] ,     model.config.text_config.vocab_size						)
    lowercase__       :       str				=						random_attention_mask([batch_size, 4]						)
    lowercase__       :       Optional[Any]				=						{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

    return model, inputs


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Union[str, Any]:
    lowercase__       :       Any				=						FlaxCLIPVisionModel(lowerCamelCase__						)
    lowercase__       :       Tuple				=						FlaxBertModel(lowerCamelCase__						)
    return vision_model, text_model







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Any				=						FlaxCLIPVisionModelTester(self						)
    lowercase__       :       Optional[Any]				=						FlaxBertModelTester(self						)
    lowercase__       :       Tuple				=						clip_model_tester.prepare_config_and_inputs()
    lowercase__       :       Any				=						bert_model_tester.prepare_config_and_inputs()

    lowercase__	,       lowercase__       :       Optional[int]				=						vision_config_and_inputs

    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       Optional[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 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



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

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

    lowercase__       :       Tuple				=						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__       :       List[str]				=						np.array([[1.228_4727, 0.310_4122]]						)

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



"""simple docstring"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InformerForPrediction',
       'InformerModel',
       'InformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)