



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



@require_flax
class        _SCREAMING_SNAKE_CASE		:





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       pass





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







       pass





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







       pass





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







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





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase=None    ,  **UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__)

       __UpperCAmelCase			 =FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE__)

       __UpperCAmelCase			 =model(input_ids=SCREAMING_SNAKE_CASE__    ,  pixel_values=SCREAMING_SNAKE_CASE__    ,  attention_mask=SCREAMING_SNAKE_CASE__)

       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 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase=None    ,  **UpperCAmelCase):
       '''simple docstring'''







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

       __UpperCAmelCase			 =model(input_ids=SCREAMING_SNAKE_CASE__    ,  pixel_values=SCREAMING_SNAKE_CASE__    ,  attention_mask=SCREAMING_SNAKE_CASE__)

       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 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase=None    ,  **UpperCAmelCase):
       '''simple docstring'''







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

       __UpperCAmelCase			 =model(input_ids=SCREAMING_SNAKE_CASE__    ,  pixel_values=SCREAMING_SNAKE_CASE__    ,  attention_mask=SCREAMING_SNAKE_CASE__)
       __UpperCAmelCase			 =output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(SCREAMING_SNAKE_CASE__)
           __UpperCAmelCase			 =FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE__)

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





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase=None    ,  **UpperCAmelCase):
       '''simple docstring'''







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

       __UpperCAmelCase			 =model(
           input_ids=SCREAMING_SNAKE_CASE__    ,  pixel_values=SCREAMING_SNAKE_CASE__    ,  attention_mask=SCREAMING_SNAKE_CASE__    ,  output_attentions=SCREAMING_SNAKE_CASE__)

       __UpperCAmelCase			 =output.vision_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE__)    ,  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(SCREAMING_SNAKE_CASE__)    ,  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 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       pt_model.to(SCREAMING_SNAKE_CASE__)
       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(**SCREAMING_SNAKE_CASE__).to_tuple()

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

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

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

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

       pt_model_loaded.to(SCREAMING_SNAKE_CASE__)
       pt_model_loaded.eval()

       with torch.no_grad():
           __UpperCAmelCase			 =pt_model_loaded(**SCREAMING_SNAKE_CASE__).to_tuple()

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





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__)

       __UpperCAmelCase			 =VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE__)
       __UpperCAmelCase			 =FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE__)

       __UpperCAmelCase			 =convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,  SCREAMING_SNAKE_CASE__)
       __UpperCAmelCase			 =fx_state

       self.check_pt_flax_equivalence(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__)





   def 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__)

       __UpperCAmelCase			 =VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE__)
       __UpperCAmelCase			 =FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE__)

       __UpperCAmelCase			 =load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__    ,  fx_model.params)

       self.check_pt_flax_equivalence(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__)





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







       __UpperCAmelCase			 =self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**SCREAMING_SNAKE_CASE__)





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







       __UpperCAmelCase			 =self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**SCREAMING_SNAKE_CASE__)





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







       __UpperCAmelCase			 =self.prepare_config_and_inputs()
       self.check_save_load(**SCREAMING_SNAKE_CASE__)





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







       __UpperCAmelCase			 =self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**SCREAMING_SNAKE_CASE__)





   @is_pt_flax_cross_test
   def 	A__      (self):
       '''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(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__)
       self.check_equivalence_flax_to_pt(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__)






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







       __UpperCAmelCase , __UpperCAmelCase			 =self.get_pretrained_model_and_inputs()

       __UpperCAmelCase			 =model_a(**SCREAMING_SNAKE_CASE__)
       __UpperCAmelCase			 =outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
           model_a.save_pretrained(SCREAMING_SNAKE_CASE__)
           __UpperCAmelCase			 =FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE__)

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







@require_flax
class        _SCREAMING_SNAKE_CASE		(       _lowerCAmelCase			,			unittest.TestCase ):





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







       __UpperCAmelCase			 =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           '''hf-internal-testing/tiny-random-vit'''    ,  '''hf-internal-testing/tiny-bert'''    ,  vision_from_pt=SCREAMING_SNAKE_CASE__    ,  text_from_pt=SCREAMING_SNAKE_CASE__    ,  )
       __UpperCAmelCase			 =1_3
       __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 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =FlaxViTModel(SCREAMING_SNAKE_CASE__)
       __UpperCAmelCase			 =FlaxBertModel(SCREAMING_SNAKE_CASE__)
       return vision_model, text_model






   def 	A__      (self):
       '''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        _SCREAMING_SNAKE_CASE		(       _lowerCAmelCase			,			unittest.TestCase ):





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







       __UpperCAmelCase			 =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           '''hf-internal-testing/tiny-random-clip'''    ,  '''hf-internal-testing/tiny-bert'''    ,  vision_from_pt=SCREAMING_SNAKE_CASE__    ,  text_from_pt=SCREAMING_SNAKE_CASE__    ,  )
       __UpperCAmelCase			 =1_3
       __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 	A__      (self    ,  UpperCAmelCase    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =FlaxCLIPVisionModel(SCREAMING_SNAKE_CASE__)
       __UpperCAmelCase			 =FlaxBertModel(SCREAMING_SNAKE_CASE__)
       return vision_model, text_model






   def 	A__      (self):
       '''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        _SCREAMING_SNAKE_CASE		(       unittest.TestCase ):





   @slow
   def 	A__      (self):
       '''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=SCREAMING_SNAKE_CASE__    ,  padding=SCREAMING_SNAKE_CASE__    ,  return_tensors='''np''')

       __UpperCAmelCase			 =model(**SCREAMING_SNAKE_CASE__)

       # 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([[1.228_4727, 0.310_4122]])

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





from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


