



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 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					str:
   if isinstance(snake_case__	,					collections.abc.Iterable     ):
      return x
   return (x, x)



@require_flax
class     lowerCAmelCase_		:
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Dict:
          pass
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		List[Any]:
          pass
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Dict						     =     np.abs((a - b)	).max()
          self.assertLessEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      F"""Difference between torch and flax is {diff} (>= {tol})."""	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Any						     =     VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     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  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     self.get_vision_text_model(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     {'vision_model': vision_model, 'text_model': text_model}
          UpperCamelCase   :  List[str]						     =     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     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  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase     ,					UpperCamelCase   :  List[Any]						     =     self.get_vision_text_model(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     {'vision_model': vision_model, 'text_model': text_model}
          UpperCamelCase   :  int						     =     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

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

          with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  int						     =     FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_	)

             UpperCamelCase   :  Any						     =     model(input_ids=SCREAMING_SNAKE_CASE_,      pixel_values=SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Dict						     =     after_output[0]
             UpperCamelCase   :  str						     =     np.amax(np.abs(out_a - out_a	)	)
             self.assertLessEqual(SCREAMING_SNAKE_CASE_,      1e-3	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase     ,					UpperCamelCase   :  Union[str, Any]						     =     self.get_vision_text_model(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     {'vision_model': vision_model, 'text_model': text_model}
          UpperCamelCase   :  Union[str, Any]						     =     FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Tuple						     =     model(
              input_ids=SCREAMING_SNAKE_CASE_,      pixel_values=SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_,      output_attentions=SCREAMING_SNAKE_CASE_	)

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

          UpperCamelCase   :  Union[str, Any]						     =     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  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Any:
          pt_model.to(SCREAMING_SNAKE_CASE_	)
          pt_model.eval()

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

          with torch.no_grad():
             UpperCamelCase   :  Tuple						     =     pt_model(**SCREAMING_SNAKE_CASE_	).to_tuple()

          UpperCamelCase   :  List[str]						     =     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   :  List[Any]						     =     FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_,      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Dict						     =     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   :  int						     =     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   :  List[str]						     =     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  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  List[Any]						     =     VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Dict						     =     VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     convert_pytorch_state_dict_to_flax(pt_model.state_dict(),      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     fx_state

          self.check_pt_flax_equivalence(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  str						     =     VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Union[str, Any]						     =     VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     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  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  List[Any]						     =     self.prepare_config_and_inputs()
          self.check_model_from_pretrained_configs(**SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.prepare_config_and_inputs()
          self.check_vision_text_dual_encoder_from_pretrained(**SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  str						     =     self.prepare_config_and_inputs()
          self.check_save_load(**SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  List[Any]						     =     self.prepare_config_and_inputs()
          self.check_vision_text_output_attention(**SCREAMING_SNAKE_CASE_	)
       @is_pt_flax_cross_test
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Any						     =     self.prepare_config_and_inputs()
          UpperCamelCase   :  Union[str, Any]						     =     config_inputs_dict.pop('vision_config'	)
          UpperCamelCase   :  Union[str, Any]						     =     config_inputs_dict.pop('text_config'	)

          UpperCamelCase   :  Dict						     =     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  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase     ,					UpperCamelCase   :  str						     =     self.get_pretrained_model_and_inputs()

          UpperCamelCase   :  str						     =     model_a(**SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     outputs[0]

          with tempfile.TemporaryDirectory() as tmp_dirname:
             model_a.save_pretrained(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  int						     =     FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_	)

             UpperCamelCase   :  Any						     =     model_a(**SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Dict						     =     after_outputs[0]
             UpperCamelCase   :  Any						     =     np.amax(np.abs(out_a - out_a	)	)
             self.assertLessEqual(SCREAMING_SNAKE_CASE_,      1e-5	)






@require_flax
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Optional[Any]						     =     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   :  Optional[int]						     =     13
          UpperCamelCase   :  Tuple						     =     floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ]	)
          UpperCamelCase   :  Optional[int]						     =     ids_tensor([batch_size, 4],      model.config.text_config.vocab_size	)
          UpperCamelCase   :  Optional[Any]						     =     random_attention_mask([batch_size, 4]	)
          UpperCamelCase   :  List[Any]						     =     {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

          return model, inputs
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  Optional[int]						     =     FlaxViTModel(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     FlaxBertModel(SCREAMING_SNAKE_CASE_	)
          return vision_model, text_model






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

          UpperCamelCase     ,					UpperCamelCase   :  Optional[Any]						     =     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_		(  a__	,   unittest.TestCase							):
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  List[Any]						     =     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   :  Dict						     =     13
          UpperCamelCase   :  List[Any]						     =     floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ]	)
          UpperCamelCase   :  Tuple						     =     ids_tensor([batch_size, 4],      model.config.text_config.vocab_size	)
          UpperCamelCase   :  Optional[int]						     =     random_attention_mask([batch_size, 4]	)
          UpperCamelCase   :  str						     =     {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

          return model, inputs
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     FlaxCLIPVisionModel(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     FlaxBertModel(SCREAMING_SNAKE_CASE_	)
          return vision_model, text_model






       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Optional[int]						     =     FlaxCLIPVisionModelTester(self	)
          UpperCamelCase   :  int						     =     FlaxBertModelTester(self	)
          UpperCamelCase   :  Optional[Any]						     =     clip_model_tester.prepare_config_and_inputs()
          UpperCamelCase   :  Tuple						     =     bert_model_tester.prepare_config_and_inputs()

          UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     vision_config_and_inputs

          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Dict						     =     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							):
       @slow
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Optional[Any]						     =     FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian',      logit_scale_init_value=1.0	)
          UpperCamelCase   :  Dict						     =     VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'	)

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

          UpperCamelCase   :  Union[str, Any]						     =     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   :  int						     =     np.array([[1.2_28_47_27, 0.3_10_41_22]]	)

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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  "data2vec-text"
       def __init__(      self,      SCREAMING_SNAKE_CASE_=3_0522,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_="absolute",      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,      bos_token_id=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     vocab_size
          UpperCamelCase   :  Tuple						     =     hidden_size
          UpperCamelCase   :  int						     =     num_hidden_layers
          UpperCamelCase   :  Dict						     =     num_attention_heads
          UpperCamelCase   :  str						     =     hidden_act
          UpperCamelCase   :  List[str]						     =     intermediate_size
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  List[str]						     =     type_vocab_size
          UpperCamelCase   :  List[Any]						     =     initializer_range
          UpperCamelCase   :  List[str]						     =     layer_norm_eps
          UpperCamelCase   :  List[str]						     =     position_embedding_type
          UpperCamelCase   :  Any						     =     use_cache
          UpperCamelCase   :  Any						     =     classifier_dropout






class     lowerCAmelCase_		(  a__							):
       @property
       def  snake_case_	(      self	)					->		Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             UpperCamelCase   :  List[Any]						     =     {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
             UpperCamelCase   :  int						     =     {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]	)



