


from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
    from transformers import (
        TFBertModel,
        TFCLIPVisionModel,
        TFDeiTModel,
        TFRobertaModel,
        TFVisionTextDualEncoderModel,
        TFViTModel,
        VisionTextDualEncoderConfig,
    )

if is_vision_available():
    from PIL import Image

    from transformers import VisionTextDualEncoderProcessor

def    lowercase_	(	_A    :					Optional[Any]		):

      """simple docstring"""




      if isinstance(_A					,      collections.abc.Iterable		):
            return x
      return (x, x)







@require_tf
class     _lowercase				:


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Dict=None      ,   **__lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						VisionTextDualEncoderConfig.from_vision_text_configs(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						TFVisionTextDualEncoderModel(__lowerCamelCase )

             lowerCamelCase__	:       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 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any]=None      ,   **__lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						self.get_vision_text_model(__lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase      ,   text_model=__lowerCamelCase )

             lowerCamelCase__	:       Optional[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 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    List[str]=None      ,   **__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       int					=						self.get_vision_text_model(__lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						{"vision_model": vision_model, "text_model": text_model}
             lowerCamelCase__	:       Optional[Any]					=						TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowerCamelCase )

             lowerCamelCase__	:       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 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    List[str]=None      ,   **__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       Dict					=						self.get_vision_text_model(__lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase      ,   text_model=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						model(input_ids=__lowerCamelCase      ,   pixel_values=__lowerCamelCase      ,   attention_mask=__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						output[0].numpy()

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(__lowerCamelCase )
                   lowerCamelCase__	:       List[str]					=						TFVisionTextDualEncoderModel.from_pretrained(__lowerCamelCase )

                   lowerCamelCase__	:       List[str]					=						model(input_ids=__lowerCamelCase      ,   pixel_values=__lowerCamelCase      ,   attention_mask=__lowerCamelCase )
                   lowerCamelCase__	:       List[str]					=						after_output[0].numpy()
                   lowerCamelCase__	:       Tuple					=						np.amax(np.abs(out_a - out_a ) )
                   self.assertLessEqual(__lowerCamelCase      ,   1E-5 )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[int]=None      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						self.get_vision_text_model(__lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       int					=						TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase      ,   text_model=__lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						model(
                 input_ids=__lowerCamelCase      ,   pixel_values=__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   output_attentions=__lowerCamelCase )

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

             lowerCamelCase__	:       List[Any]					=						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 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    float ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.prepare_config_and_inputs()
             self.check_vision_text_dual_encoder_model(**__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.prepare_config_and_inputs()
             self.check_model_from_pretrained_configs(**__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.prepare_config_and_inputs()
             self.check_vision_text_dual_encoder_from_pretrained(**__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.prepare_config_and_inputs()
             self.check_save_load(**__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.prepare_config_and_inputs()
             self.check_vision_text_output_attention(**__lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						self.get_pretrained_model_and_inputs()

             lowerCamelCase__	:       Tuple					=						model_a(**__lowerCamelCase )
             lowerCamelCase__	:       int					=						outputs[0].numpy()

             with tempfile.TemporaryDirectory() as tmp_dirname:
                   model_a.save_pretrained(__lowerCamelCase )
                   lowerCamelCase__	:       Tuple					=						TFVisionTextDualEncoderModel.from_pretrained(__lowerCamelCase )

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







@require_tf
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						TFVisionTextDualEncoderModel.from_vision_text_pretrained(
                 "hf-internal-testing/tiny-random-vit"      ,   "hf-internal-testing/tiny-random-bert" )
             lowerCamelCase__	:       Optional[int]					=						13
             lowerCamelCase__	:       List[str]					=						floats_tensor(
                 [
                     batch_size,
                     model.vision_model.config.num_channels,
                     model.vision_model.config.image_size,
                     model.vision_model.config.image_size,
                 ] )
             lowerCamelCase__	:       List[Any]					=						ids_tensor([batch_size, 4]      ,   model.text_model.config.vocab_size )
             lowerCamelCase__	:       List[Any]					=						random_attention_mask([batch_size, 4] )
             lowerCamelCase__	:       List[Any]					=						{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

             return model, inputs





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						TFViTModel(__lowerCamelCase      ,   name="vision_model" )
             lowerCamelCase__	:       int					=						TFBertModel(__lowerCamelCase      ,   name="text_model" )
             return vision_model, text_model





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFViTModelTester(self )
             lowerCamelCase__	:       int					=						TFBertModelTester(self )
             lowerCamelCase__	:       Union[str, Any]					=						vit_model_tester.prepare_config_and_inputs()
             lowerCamelCase__	:       str					=						bert_model_tester.prepare_config_and_inputs()

             lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						vision_config_and_inputs

             (
                 (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     
             )	:       Tuple					=						text_config_and_inputs

             return {
                 "text_config": text_config,
                 "vision_config": vision_config,
                 "pixel_values": pixel_values,
                 "attention_mask": input_mask,
                 "input_ids": input_ids,
                 "text_token_type_ids": token_type_ids,
                 "text_sequence_labels": sequence_labels,
                 "text_token_labels": token_labels,
                 "text_choice_labels": choice_labels,
             }







@require_tf
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						TFVisionTextDualEncoderModel.from_vision_text_pretrained(
                 "Rocketknight1/tiny-random-deit-tf"      ,   "hf-internal-testing/tiny-random-roberta" )
             lowerCamelCase__	:       List[Any]					=						13
             lowerCamelCase__	:       List[str]					=						floats_tensor(
                 [
                     batch_size,
                     model.vision_model.config.num_channels,
                     model.vision_model.config.image_size,
                     model.vision_model.config.image_size,
                 ] )
             lowerCamelCase__	:       Tuple					=						ids_tensor([batch_size, 4]      ,   model.text_model.config.vocab_size )
             lowerCamelCase__	:       Optional[Any]					=						random_attention_mask([batch_size, 4] )
             lowerCamelCase__	:       Dict					=						{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

             return model, inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Any=None      ,   **__lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       int					=						self.get_vision_text_model(__lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						TFVisionTextDualEncoderModel(vision_model=__lowerCamelCase      ,   text_model=__lowerCamelCase )

             lowerCamelCase__	:       List[Any]					=						model(
                 input_ids=__lowerCamelCase      ,   pixel_values=__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   output_attentions=__lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						output.vision_model_output.attentions
             self.assertEqual(len(__lowerCamelCase )      ,   vision_config.num_hidden_layers )

             # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
             lowerCamelCase__	:       Tuple					=						to_atuple(vision_model.config.image_size )
             lowerCamelCase__	:       Tuple					=						to_atuple(vision_model.config.patch_size )
             lowerCamelCase__	:       Any					=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
             lowerCamelCase__	:       List[Any]					=						num_patches + 2
             self.assertEqual(vision_attentions[0].shape[-3:]      ,   (vision_config.num_attention_heads, seq_len, seq_len) )

             lowerCamelCase__	:       Optional[int]					=						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 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						TFDeiTModel(__lowerCamelCase      ,   name="vision_model" )
             lowerCamelCase__	:       Optional[int]					=						TFRobertaModel(__lowerCamelCase      ,   name="text_model" )
             return vision_model, text_model





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						TFDeiTModelTester(self )
             lowerCamelCase__	:       List[Any]					=						TFRobertaModelTester(self )
             lowerCamelCase__	:       int					=						vit_model_tester.prepare_config_and_inputs()
             lowerCamelCase__	:       Dict					=						bert_model_tester.prepare_config_and_inputs()

             lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       str					=						vision_config_and_inputs

             (
                 (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     
             )	:       Tuple					=						text_config_and_inputs

             return {
                 "text_config": text_config,
                 "vision_config": vision_config,
                 "pixel_values": pixel_values,
                 "attention_mask": input_mask,
                 "input_ids": input_ids,
                 "text_token_type_ids": token_type_ids,
                 "text_sequence_labels": sequence_labels,
                 "text_token_labels": token_labels,
                 "text_choice_labels": choice_labels,
             }







@require_tf
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						TFVisionTextDualEncoderModel.from_vision_text_pretrained(
                 "Rocketknight1/tiny-random-clip-tf"      ,   "hf-internal-testing/tiny-random-bert" )
             lowerCamelCase__	:       Any					=						13
             lowerCamelCase__	:       int					=						floats_tensor(
                 [
                     batch_size,
                     model.vision_model.config.num_channels,
                     model.vision_model.config.image_size,
                     model.vision_model.config.image_size,
                 ] )
             lowerCamelCase__	:       Dict					=						ids_tensor([batch_size, 4]      ,   model.text_model.config.vocab_size )
             lowerCamelCase__	:       int					=						random_attention_mask([batch_size, 4] )
             lowerCamelCase__	:       Any					=						{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

             return model, inputs





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						TFCLIPVisionModel(__lowerCamelCase      ,   name="vision_model" )
             lowerCamelCase__	:       Union[str, Any]					=						TFBertModel(__lowerCamelCase      ,   name="text_model" )
             return vision_model, text_model





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						TFCLIPVisionModelTester(self )
             lowerCamelCase__	:       Optional[int]					=						TFBertModelTester(self )
             lowerCamelCase__	:       List[str]					=						clip_model_tester.prepare_config_and_inputs()
             lowerCamelCase__	:       List[Any]					=						bert_model_tester.prepare_config_and_inputs()

             lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						vision_config_and_inputs

             (
                 (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     
             )	:       Optional[int]					=						text_config_and_inputs

             return {
                 "text_config": text_config,
                 "vision_config": vision_config,
                 "pixel_values": pixel_values,
                 "attention_mask": input_mask,
                 "input_ids": input_ids,
                 "text_token_type_ids": token_type_ids,
                 "text_sequence_labels": sequence_labels,
                 "text_token_labels": token_labels,
                 "text_choice_labels": choice_labels,
             }







@require_vision
@require_tf
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						TFVisionTextDualEncoderModel.from_pretrained(
                 "clip-italian/clip-italian"      ,   logit_scale_init_value=1.0      ,   from_pt=__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )

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

             lowerCamelCase__	:       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])      ,   )

             lowerCamelCase__	:       Optional[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.numpy()      ,   __lowerCamelCase      ,   atol=1E-3 ) )






from collections import OrderedDict
from typing import Mapping

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


A   :					Dict	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      {
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "roberta"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Dict=50265      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Optional[int]=12      ,   __lowerCamelCase							:    int=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    Optional[int]=1E-1_2      ,   __lowerCamelCase							:    List[Any]=1      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Any=2      ,   __lowerCamelCase							:    Tuple="absolute"      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    str=None      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

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







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       int					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Optional[Any]					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


