




'''simple docstring'''






import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


lowercase	:      Any				=       '''none'''
lowercase	:      List[str]				=       '''none'''




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       lowercase  :		List[Any]						=      value.split(lowerCamelCase      )
       return len(lowerCamelCase      ) > 1 and any((len(lowerCamelCase      ) == 3 for x in split)      )




def 		a_( lowerCamelCase      )				->Any:






       '''simple docstring'''




       if isinstance(lowerCamelCase   ,				lowerCamelCase      ):
              return value
       if isinstance(lowerCamelCase   ,				lowerCamelCase      ):
              return float(lowerCamelCase      )
       if not isinstance(lowerCamelCase   ,				lowerCamelCase      ):
              raise ValueError('''Argument value is not a string. Can\'t parse it as float'''      )
       lowercase  :		str						=      value

       try:
              # Example: 1,000.7
              if "." in sanitized and "," in sanitized:
                     return float(sanitized.replace(''','''   ,				''''''      )      )
              # 1,000
              if "," in sanitized and _split_thousands(''','''   ,				lowerCamelCase      ):
                     return float(sanitized.replace(''','''   ,				''''''      )      )
              # 5,5556
              if "," in sanitized and sanitized.count(''','''      ) == 1 and not _split_thousands(''','''   ,				lowerCamelCase      ):
                     return float(sanitized.replace(''','''   ,				'''.'''      )      )
              # 0.0.0.1
              if sanitized.count('''.'''      ) > 1:
                     return float(sanitized.replace('''.'''   ,				''''''      )      )
              # 0,0,0,1
              if sanitized.count(''','''      ) > 1:
                     return float(sanitized.replace(''','''   ,				''''''      )      )
              return float(lowerCamelCase      )
       except ValueError:
              # Avoid adding the sanitized value in the error message.
              raise ValueError('''Unable to convert value to float'''      )




def 		a_( lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       if answer is None:
              return None
       try:
              lowercase  :		Any						=      convert_to_float(lowerCamelCase      )
              if isinstance(lowerCamelCase   ,				lowerCamelCase      ) and math.isnan(lowerCamelCase      ):
                     return None
              return value
       except ValueError:
              return answer.lower()


lowercase	:      Optional[int]				=       {
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}

class   SCREAMING_SNAKE_CASE_		(       enum.Enum				):
 __snake_case	:     Dict			   =      0
 __snake_case	:     Tuple			   =      1
 __snake_case	:     List[Any]			   =      2
 __snake_case	:     Tuple			   =      3
 __snake_case	:     Union[str, Any]			   =      4
 __snake_case	:     int			   =      5






class   SCREAMING_SNAKE_CASE_		(       enum.Enum				):
 __snake_case	:     Union[str, Any]			   =      0
 __snake_case	:     Any			   =      1
 __snake_case	:     Optional[Any]			   =      2






@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE_		:
 __snake_case	:     Text
 __snake_case	:     _Operator
 __snake_case	:     Any






lowercase	:      Tuple				=       re.compile(r'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)




def 		a_( lowerCamelCase      )				->List[Any]:






       '''simple docstring'''




       return list(_TOKENIZER.findall(x.lower()      )      )




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




       if operator == _Operator.EQUALS:
              return src == tgt
       elif operator == _Operator.GREATER:
              return src > tgt
       elif operator == _Operator.LESSER:
              return src < tgt
       raise ValueError(f"""Unknown operator: {operator}"""      )




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Any:






       '''simple docstring'''




       lowercase  :		Dict						=      table['''types''']
       return _TYPE_CONVERTER[types[column]](lowerCamelCase      )




def 		a_( lowerCamelCase      )				->List[Any]:






       '''simple docstring'''




       return isinstance(lowerCamelCase   ,				lowerCamelCase      )




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       for cond in conditions:
              lowercase  :		Optional[int]						=      row[cond.column]

              lowercase  :		Any						=      _parse_value(lowerCamelCase   ,				cond.column   ,				cond.cmp_value      )

              if _is_string(lowerCamelCase      ) and _is_string(lowerCamelCase      ):
                     lowercase  :		List[Any]						=      _normalize_for_match(lowerCamelCase      )
                     lowercase  :		Any						=      _normalize_for_match(lowerCamelCase      )

              if not isinstance(lowerCamelCase   ,				type(lowerCamelCase      )      ):
                     raise ValueError('''Type difference {} != {}'''.format(type(lowerCamelCase      )   ,				type(lowerCamelCase      )      )      )

              if not _compare(cond.operator   ,				lowerCamelCase   ,				lowerCamelCase      ):
                     return False
       return True




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       if not answer_coordinates:
              if aggregation_op == _Aggregation.COUNT:
                     return 0.0
              else:
                     return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
       if aggregation_op == _Aggregation.COUNT:
              return float(len(lowerCamelCase      )      )

       # If we have just one answer, if float returns it or try a conversion.
       lowercase  :		Tuple						=      [table['''rows'''][i][j] for (i, j) in answer_coordinates]
       if len(lowerCamelCase      ) == 1:
              try:
                     return convert_to_float(values[0]      )
              except ValueError as e:
                     if aggregation_op != _Aggregation.NONE:
                            raise e

       if aggregation_op == _Aggregation.NONE:
              return None

       # Other aggregation only support numeric values. Bail out if we have strings.
       if not all((isinstance(lowerCamelCase   ,				(int, float)      ) for v in values)      ):
              return None

       if aggregation_op == _Aggregation.SUM:
              return float(sum(lowerCamelCase      )      )
       elif aggregation_op == _Aggregation.AVERAGE:
              return sum(lowerCamelCase      ) / len(lowerCamelCase      )
       else:
              raise ValueError(f"""Unknown aggregation: {aggregation_op}"""      )




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Tuple:






       '''simple docstring'''




       lowercase  :		Tuple						=      sql_query['''agg''']
       if aggregation_op_index >= 3:
              lowercase  :		Tuple						=      _Aggregation(lowerCamelCase      )
       else:
              lowercase  :		str						=      _Aggregation.NONE

       lowercase  :		Optional[Any]						=      sql_query['''sel''']
       lowercase  :		int						=      [
           _Condition(lowerCamelCase   ,				_Operator(lowerCamelCase      )   ,				lowerCamelCase      )
           for column, operator, cmp_value in zip(
               sql_query['''conds''']['''column_index''']   ,				sql_query['''conds''']['''operator_index''']   ,				sql_query['''conds''']['''condition''']      )
       ]

       lowercase  :		Optional[int]						=      []
       for row in range(len(table['''rows''']      )      ):
              if _respect_conditions(lowerCamelCase   ,				table['''rows'''][row]   ,				lowerCamelCase      ):
                     indices.append((row, target_column)      )

       if not indices:
              return [], aggregation_op

       if len(lowerCamelCase      ) == 1:
              return indices, aggregation_op

       # Parsing of MIN/MAX.
       if aggregation_op_index in (1, 2):
              lowercase  :		List[str]						=      {2: min, 1: max}
              lowercase  :		Dict						=      [(table['''rows'''][i][j], index) for index, (i, j) in enumerate(lowerCamelCase      )]
              lowercase  :		Tuple						=      functools.reduce(operators[sql_query['''agg''']]   ,				lowerCamelCase      )

              lowercase  :		Optional[Any]						=      [indices[reduced[1]]]
              return ret, _Aggregation.NONE

       return indices, aggregation_op




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Optional[Any]:






       '''simple docstring'''




       if float_answer is not None:
              return [str(lowerCamelCase      )]
       return [str(table['''real_rows'''][r][c]      ) for r, c in answer_coordinates]





def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->List:






       '''simple docstring'''




       lowercase,     lowercase  :		Union[str, Any]						=      _get_answer_coordinates(lowerCamelCase   ,				lowerCamelCase      )
       lowercase  :		List[Any]						=      _get_float_answer(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       lowercase  :		Union[str, Any]						=      _get_answer_text(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       # keep the original data the same with TaPas
       if len(lowerCamelCase      ) == 0:
              lowercase  :		Any						=      [EMPTY_ANSWER]
       return answer_text










'''simple docstring'''






import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          DataaVecVisionForImageClassification,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
      )
      from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A   ,		A=100   ,		A=13   ,		A=30   ,		A=2   ,		A=3   ,		A=True   ,		A=True   ,		A=32   ,		A=4   ,		A=4   ,		A=37   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=10   ,		A=0.0_2   ,		A=3   ,		A=None   ,		A=[0, 1, 2, 3]   ,		):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      parent
        lowercase  :		str						=      100
        lowercase  :		Dict						=      batch_size
        lowercase  :		Tuple						=      image_size
        lowercase  :		int						=      patch_size
        lowercase  :		Optional[int]						=      num_channels
        lowercase  :		Any						=      is_training
        lowercase  :		Dict						=      use_labels
        lowercase  :		Any						=      hidden_size
        lowercase  :		List[Any]						=      num_hidden_layers
        lowercase  :		int						=      num_attention_heads
        lowercase  :		Dict						=      intermediate_size
        lowercase  :		Optional[int]						=      hidden_act
        lowercase  :		Union[str, Any]						=      hidden_dropout_prob
        lowercase  :		str						=      attention_probs_dropout_prob
        lowercase  :		List[str]						=      type_sequence_label_size
        lowercase  :		Dict						=      initializer_range
        lowercase  :		str						=      scope
        lowercase  :		Tuple						=      out_indices
        lowercase  :		Union[str, Any]						=      num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase  :		Union[str, Any]						=      (image_size // patch_size) ** 2
        lowercase  :		List[Any]						=      num_patches + 1



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        lowercase  :		Union[str, Any]						=      None
        lowercase  :		int						=      None
        if self.use_labels:
               lowercase  :		Dict						=      ids_tensor([self.batch_size]   ,		self.type_sequence_label_size )
               lowercase  :		Tuple						=      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,		self.num_labels )

        lowercase  :		int						=      self.get_config()

        return config, pixel_values, labels, pixel_labels



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return DataaVecVisionConfig(
            vocab_size=self.vocab_size   ,		image_size=self.image_size   ,		patch_size=self.patch_size   ,		num_channels=self.num_channels   ,		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   ,		is_decoder=A   ,		initializer_range=self.initializer_range   ,		out_indices=self.out_indices   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      DataaVecVisionModel(config=A )
        model.to(A )
        model.eval()
        lowercase  :		Tuple						=      model(A )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase  :		Union[str, Any]						=      (self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, num_patches + 1, self.hidden_size) )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		Any						=      self.type_sequence_label_size
        lowercase  :		Any						=      DataaVecVisionForImageClassification(A )
        model.to(A )
        model.eval()
        lowercase  :		Dict						=      model(A   ,		labels=A )
        self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size) )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      self.num_labels
        lowercase  :		List[Any]						=      DataaVecVisionForSemanticSegmentation(A )
        model.to(A )
        model.eval()
        lowercase  :		int						=      model(A )
        self.parent.assertEqual(
            result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
        lowercase  :		List[str]						=      model(A   ,		labels=A )
        self.parent.assertEqual(
            result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      self.prepare_config_and_inputs()
        lowercase,     lowercase,     lowercase,     lowercase  :		Any						=      config_and_inputs
        lowercase  :		Union[str, Any]						=      {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Tuple			   =      (
     (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __snake_case	:     Optional[Any]			   =      (
     {
         "feature-extraction": DataaVecVisionModel,
         "image-classification": DataaVecVisionForImageClassification,
         "image-segmentation": DataaVecVisionForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __snake_case	:     str			   =      False
 __snake_case	:     str			   =      False
 __snake_case	:     int			   =      False



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      DataaVecVisionModelTester(self )
        lowercase  :		List[str]						=      ConfigTester(
            self   ,		config_class=A   ,		has_text_modality=A   ,		hidden_size=37 )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.config_tester.run_common_tests()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase  :		int						=      model_class(A )
               self.assertIsInstance(model.get_input_embeddings()   ,		(nn.Module) )
               lowercase  :		Optional[int]						=      model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(A   ,		nn.Linear ) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Dict						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase  :		Tuple						=      model_class(A )
               lowercase  :		List[str]						=      inspect.signature(model.forward )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase  :		Tuple						=      [*signature.parameters.keys()]

               lowercase  :		List[Any]						=      ['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        if not self.model_tester.is_training:
               return

        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
        lowercase  :		Union[str, Any]						=      True

        for model_class in self.all_model_classes:
               if model_class in [*get_values(A )]:
                      continue

               lowercase  :		Optional[int]						=      model_class(A )
               model.to(A )
               model.train()
               lowercase  :		int						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		List[str]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
               return

        lowercase  :		List[str]						=      False
        lowercase  :		str						=      True

        for model_class in self.all_model_classes:
               if model_class in [*get_values(A )] or not model_class.supports_gradient_checkpointing:
                      continue
               # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
               # this can then be incorporated into _prepare_for_class in test_modeling_common.py
               elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                      lowercase,     lowercase,     lowercase,     lowercase  :		Tuple						=      inputs_dict['''pixel_values'''].shape
                      lowercase  :		int						=      torch.zeros(
                          [self.model_tester.batch_size, height, width]   ,		device=A ).long()
               lowercase  :		Optional[Any]						=      model_class(A )
               model.gradient_checkpointing_enable()
               model.to(A )
               model.train()
               lowercase  :		Any						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		Optional[int]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		List[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()

        lowercase  :		Union[str, Any]						=      _config_zero_init(A )
        for model_class in self.all_model_classes:
               lowercase  :		Dict						=      model_class(config=A )
               for name, param in model.named_parameters():
                      # we skip lambda parameters as these require special initial values
                      # determined by config.layer_scale_init_value
                      if "lambda" in name:
                             continue
                      if param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()   ,		[0.0, 1.0]   ,		msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A=2E-4   ,		A="outputs"   ,		A=None ):






        '''simple docstring'''




        super().check_pt_tf_outputs(A   ,		A   ,		A   ,		A   ,		A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase  :		List[Any]						=      DataaVecVisionModel.from_pretrained(A )
               self.assertIsNotNone(A )






def 		a_( )				->Any:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       return image





@require_torch
@require_vision
class   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return (
            BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ) if is_vision_available() else None
        )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ).to(
            A )

        lowercase  :		List[str]						=      self.default_image_processor
        lowercase  :		List[Any]						=      prepare_img()
        lowercase  :		List[Any]						=      image_processor(images=A   ,		return_tensors='''pt''' ).to(A )

        # forward pass
        with torch.no_grad():
               lowercase  :		List[Any]						=      model(**A )
        lowercase  :		Any						=      outputs.logits

        # verify the logits
        lowercase  :		List[Any]						=      torch.Size((1, 1000) )
        self.assertEqual(logits.shape   ,		A )

        lowercase  :		str						=      torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1] ).to(A )

        self.assertTrue(torch.allclose(logits[0, :3]   ,		A   ,		atol=1E-4 ) )

        lowercase  :		Dict						=      [model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
        self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist()   ,		A )




