
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase  :					Any						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           requires_backends(self		,"""vision"""      )
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == """tf"""
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      {}
           if top_k is not None:
                  lowercase							:     int       	=      top_k
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      load_image(snake_case      )
           lowercase							:     List[Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model(**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5      ):



           '''simple docstring'''







           if top_k > self.model.config.num_labels:
                  lowercase							:     Tuple       	=      self.model.config.num_labels

           if self.framework == "pt":
                  lowercase							:     str       	=      model_outputs.logits.softmax(-1      )[0]
                  lowercase	,			lowercase							:     Dict       	=      probs.topk(snake_case      )
           elif self.framework == "tf":
                  lowercase							:     Optional[int]       	=      stable_softmax(model_outputs.logits		,axis=-1      )[0]
                  lowercase							:     Union[str, Any]       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     List[str]       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  raise ValueError(f"Unsupported framework: {self.framework}"      )

           lowercase							:     Tuple       	=      scores.tolist()
           lowercase							:     Dict       	=      ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case		,snake_case      )]





'''simple docstring'''


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
   import torch

   from transformers import (
       MraForMaskedLM,
       MraForMultipleChoice,
       MraForQuestionAnswering,
       MraForSequenceClassification,
       MraForTokenClassification,
       MraModel,
   )
   from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Any=2  ,						UpperCamelCase__      :					Union[str, Any]=8  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[Any]=9_9  ,						UpperCamelCase__      :					List[Any]=1_6  ,						UpperCamelCase__      :					List[str]=5  ,						UpperCamelCase__      :					Dict=2  ,						UpperCamelCase__      :					Optional[int]=3_6  ,						UpperCamelCase__      :					str="gelu"  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Optional[int]=5_1_2  ,						UpperCamelCase__      :					Dict=1_6  ,						UpperCamelCase__      :					List[str]=2  ,						UpperCamelCase__      :					Any=0.0_2  ,						UpperCamelCase__      :					str=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  seq_length
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_input_mask
       UpperCamelCase					=  use_token_type_ids
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  hidden_size
       UpperCamelCase					=  num_hidden_layers
       UpperCamelCase					=  num_attention_heads
       UpperCamelCase					=  intermediate_size
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  hidden_dropout_prob
       UpperCamelCase					=  attention_probs_dropout_prob
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  type_vocab_size
       UpperCamelCase					=  type_sequence_label_size
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  num_choices
       UpperCamelCase					=  scope






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size )

       UpperCamelCase					=  None
       if self.use_input_mask:
          UpperCamelCase					=  random_attention_mask([self.batch_size, self.seq_length] )

       UpperCamelCase					=  None
       if self.use_token_type_ids:
          UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size )

       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       if self.use_labels:
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size )
          UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels )
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.num_choices )

       UpperCamelCase					=  self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return MraConfig(
           vocab_size=self.vocab_size  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						intermediate_size=self.intermediate_size  ,						hidden_act=self.hidden_act  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						max_position_embeddings=self.max_position_embeddings  ,						type_vocab_size=self.type_vocab_size  ,						is_decoder=UpperCamelCase__  ,						initializer_range=self.initializer_range  ,						)






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.get_config()
       UpperCamelCase					=  3_0_0
       return config






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  self.prepare_config_and_inputs()

       UpperCamelCase					=  True
       UpperCamelCase					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  MraModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  True
       UpperCamelCase					=  MraModel(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						encoder_attention_mask=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForQuestionAnswering(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						start_positions=UpperCamelCase__  ,						end_positions=UpperCamelCase__  ,						)
       self.parent.assertEqual(result.start_logits.shape  ,						(self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape  ,						(self.batch_size, self.seq_length) )






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForSequenceClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForTokenClassification(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.num_choices
       UpperCamelCase					=  MraForMultipleChoice(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  input_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  token_type_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  input_mask.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  ,						)
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices) )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  config_and_inputs
       UpperCamelCase					=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False

    _SCREAMING_SNAKE_CASE	  =  ()






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  MraModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					str ):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
          UpperCamelCase					=  type
          self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  MraModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )






    @unittest.skip(reason='MRA does not output attentions' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @slow
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  MraModel.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  torch.Size((1, 2_5_6, 7_6_8) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 2_5_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
       UpperCamelCase					=  torch.arange(4_0_9_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 4_0_9_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )

