






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   snake_case (UpperCAmelCase__  )	->    str:
  if isinstance(UpperCAmelCase__     ,   collections.abc.Iterable  ):
    return x
  return (x, x)




@require_flax
class     _lowerCAmelCase:





       """simple docstring"""







       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    np.abs((a - b)							).max()
         self.assertLessEqual(_lowerCamelCase  ,      _lowerCamelCase  ,      f'''Difference between torch and flax is {diff} (>= {tol}).'''							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=None  ,      **_lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: Union[str, Any]   				=    FlaxVisionTextDualEncoderModel(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=None  ,      **_lowerCamelCase							):
         UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_vision_text_model(_lowerCamelCase  ,      _lowerCamelCase							)
         UpperCamelCase_: Dict   				=    {'vision_model': vision_model, 'text_model': text_model}
         UpperCamelCase_: List[Any]   				=    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase							)

         UpperCamelCase_: Optional[Any]   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=None  ,      **_lowerCamelCase							):
         UpperCamelCase_				,UpperCamelCase_: Dict   				=    self.get_vision_text_model(_lowerCamelCase  ,      _lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    {'vision_model': vision_model, 'text_model': text_model}
         UpperCamelCase_: Optional[int]   				=    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    model(input_ids=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      attention_mask=_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    output[0]

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(_lowerCamelCase							)
           UpperCamelCase_: Tuple   				=    FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase							)

           UpperCamelCase_: Tuple   				=    model(input_ids=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      attention_mask=_lowerCamelCase							)
           UpperCamelCase_: List[str]   				=    after_output[0]
           UpperCamelCase_: int   				=    np.amax(np.abs(out_a - out_a							)							)
           self.assertLessEqual(_lowerCamelCase  ,      1e-3							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=None  ,      **_lowerCamelCase							):
         UpperCamelCase_				,UpperCamelCase_: Dict   				=    self.get_vision_text_model(_lowerCamelCase  ,      _lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    {'vision_model': vision_model, 'text_model': text_model}
         UpperCamelCase_: List[str]   				=    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase							)

         UpperCamelCase_: Any   				=    model(
             input_ids=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      output_attentions=_lowerCamelCase							)

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

         UpperCamelCase_: Tuple   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         pt_model.to(_lowerCamelCase							)
         pt_model.eval()

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

         with torch.no_grad():
           UpperCamelCase_: List[str]   				=    pt_model(**_lowerCamelCase							).to_tuple()

         UpperCamelCase_: Dict   				=    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							)
           UpperCamelCase_: int   				=    FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase  ,      from_pt=_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    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							)
           UpperCamelCase_: int   				=    VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase  ,      from_flax=_lowerCamelCase							)

         pt_model_loaded.to(_lowerCamelCase							)
         pt_model_loaded.eval()

         with torch.no_grad():
           UpperCamelCase_: Dict   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: Any   				=    VisionTextDualEncoderModel(_lowerCamelCase							)
         UpperCamelCase_: int   				=    FlaxVisionTextDualEncoderModel(_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,      _lowerCamelCase							)
         UpperCamelCase_: Dict   				=    fx_state

         self.check_pt_flax_equivalence(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Any   				=    VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: int   				=    VisionTextDualEncoderModel(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    FlaxVisionTextDualEncoderModel(_lowerCamelCase							)

         UpperCamelCase_: Union[str, Any]   				=    load_flax_weights_in_pytorch_model(_lowerCamelCase  ,      fx_model.params							)

         self.check_pt_flax_equivalence(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    self.prepare_config_and_inputs()
         self.check_save_load(**_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**_lowerCamelCase							)





       @is_pt_flax_cross_test
       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    self.prepare_config_and_inputs()
         UpperCamelCase_: Tuple   				=    config_inputs_dict.pop('vision_config'							)
         UpperCamelCase_: List[str]   				=    config_inputs_dict.pop('text_config'							)

         UpperCamelCase_: Dict   				=    config_inputs_dict

         self.check_equivalence_pt_to_flax(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)
         self.check_equivalence_flax_to_pt(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       @slow
       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: int   				=    self.get_pretrained_model_and_inputs()

         UpperCamelCase_: Union[str, Any]   				=    model_a(**_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    outputs[0]

         with tempfile.TemporaryDirectory() as tmp_dirname:
           model_a.save_pretrained(_lowerCamelCase							)
           UpperCamelCase_: Tuple   				=    FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase							)

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







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





       """simple docstring"""







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

         return model, inputs





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    FlaxViTModel(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    FlaxBertModel(_lowerCamelCase							)
         return vision_model, text_model





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    FlaxViTModelTester(self							)
         UpperCamelCase_: List[Any]   				=    FlaxBertModelTester(self							)
         UpperCamelCase_: List[Any]   				=    vit_model_tester.prepare_config_and_inputs()
         UpperCamelCase_: List[str]   				=    bert_model_tester.prepare_config_and_inputs()

         UpperCamelCase_				,UpperCamelCase_: int   				=    vision_config_and_inputs

         UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: 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_torch
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
             'hf-internal-testing/tiny-random-clip'  ,      'hf-internal-testing/tiny-bert'  ,      vision_from_pt=_lowerCamelCase  ,      text_from_pt=_lowerCamelCase  ,      )
         UpperCamelCase_: int   				=    1_3
         UpperCamelCase_: List[str]   				=    floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ]							)
         UpperCamelCase_: str   				=    ids_tensor([batch_size, 4]  ,      model.config.text_config.vocab_size							)
         UpperCamelCase_: Dict   				=    random_attention_mask([batch_size, 4]							)
         UpperCamelCase_: Tuple   				=    {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

         return model, inputs





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[str]   				=    FlaxCLIPVisionModel(_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    FlaxBertModel(_lowerCamelCase							)
         return vision_model, text_model





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    FlaxCLIPVisionModelTester(self							)
         UpperCamelCase_: List[Any]   				=    FlaxBertModelTester(self							)
         UpperCamelCase_: Optional[int]   				=    clip_model_tester.prepare_config_and_inputs()
         UpperCamelCase_: List[str]   				=    bert_model_tester.prepare_config_and_inputs()

         UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    vision_config_and_inputs

         UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Union[str, 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     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: str   				=    FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'  ,      logit_scale_init_value=1.0							)
         UpperCamelCase_: int   				=    VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'							)

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

         UpperCamelCase_: str   				=    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])  ,      )

         UpperCamelCase_: List[Any]   				=    np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]							)

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







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=RoFormerTokenizer
       a    :		int									=RoFormerTokenizerFast
       a    :		int									=True
       a    :		Optional[int]									=True





       def       _a   ( self							):
         super().setUp()





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    '永和服装饰品有限公司,今天天气非常好'
         UpperCamelCase_: Any   				=    '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.get_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: int   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Tuple   				=    tokenizer.tokenize(_lowerCamelCase							)

         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Dict   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.get_rust_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Optional[Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)
         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass