
"""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


_A      :	Optional[int]       		=	"""none"""
_A      :	List[str]       		=	"""none"""


def   __magic_name__     (				__snake_case    :	Dict					,			__snake_case    :	Tuple  )			->    str:
       lowercase   :    Tuple			=							value.split(__snake_case  )
       return len(__snake_case  ) > 1 and any((len(__snake_case  ) == 3 for x in split)  )


def   __magic_name__     (				__snake_case    :	Optional[int]  )			->    List[Any]:
       if isinstance(__snake_case					,			__snake_case  ):
              return value
       if isinstance(__snake_case					,			__snake_case  ):
              return float(__snake_case  )
       if not isinstance(__snake_case					,			__snake_case  ):
              raise ValueError("Argument value is not a string. Can't parse it as float"  )
       lowercase   :    List[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(","					,			__snake_case  ):
                     return float(sanitized.replace(","					,			""  )  )
              # 5,5556
              if "," in sanitized and sanitized.count(","  ) == 1 and not _split_thousands(","					,			__snake_case  ):
                     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(__snake_case  )
       except ValueError:
              # Avoid adding the sanitized value in the error message.
              raise ValueError("Unable to convert value to float"  )


def   __magic_name__     (				__snake_case    :	Dict  )			->    Optional[Any]:
       if answer is None:
              return None
       try:
              lowercase   :    Tuple			=							convert_to_float(__snake_case  )
              if isinstance(__snake_case					,			__snake_case  ) and math.isnan(__snake_case  ):
                     return None
              return value
       except ValueError:
              return answer.lower()


_A      :	Optional[Any]       		=	{
    """text""": lambda x: x,
    """real""": convert_to_float,
}

class 		a__						(				enum.Enum						):
   __lowerCAmelCase						=    0
   __lowerCAmelCase						=    1
   __lowerCAmelCase						=    2
   __lowerCAmelCase						=    3
   __lowerCAmelCase						=    4
   __lowerCAmelCase						=    5


class 		a__						(				enum.Enum						):
   __lowerCAmelCase						=    0
   __lowerCAmelCase						=    1
   __lowerCAmelCase						=    2


@dataclasses.dataclass
class 		a__						:
   __lowerCAmelCase						=    42
   __lowerCAmelCase						=    42
   __lowerCAmelCase						=    42


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


def   __magic_name__     (				__snake_case    :	Optional[Any]  )			->    Union[str, Any]:
       return list(_TOKENIZER.findall(x.lower()  )  )


def   __magic_name__     (				__snake_case    :	int					,			__snake_case    :	Union[str, Any]					,			__snake_case    :	List[Any]  )			->    Optional[int]:
       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   __magic_name__     (				__snake_case    :	Dict					,			__snake_case    :	Tuple					,			__snake_case    :	Optional[int]  )			->    Any:
       lowercase   :    int			=							table["types"]
       return _TYPE_CONVERTER[types[column]](__snake_case  )


def   __magic_name__     (				__snake_case    :	int  )			->    List[str]:
       return isinstance(__snake_case					,			__snake_case  )


def   __magic_name__     (				__snake_case    :	List[str]					,			__snake_case    :	Tuple					,			__snake_case    :	Any  )			->    Tuple:
       for cond in conditions:
              lowercase   :    List[Any]			=							row[cond.column]

              lowercase   :    Tuple			=							_parse_value(__snake_case					,			cond.column					,			cond.cmp_value  )

              if _is_string(__snake_case  ) and _is_string(__snake_case  ):
                     lowercase   :    Union[str, Any]			=							_normalize_for_match(__snake_case  )
                     lowercase   :    Tuple			=							_normalize_for_match(__snake_case  )

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

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


def   __magic_name__     (				__snake_case    :	Optional[Any]					,			__snake_case    :	List[Any]					,			__snake_case    :	Any  )			->    int:
       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(__snake_case  )  )

       # If we have just one answer, if float returns it or try a conversion.
       lowercase   :    Union[str, Any]			=							[table["rows"][i][j] for (i, j) in answer_coordinates]
       if len(__snake_case  ) == 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(__snake_case					,			(int, float)  ) for v in values)  ):
              return None

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


def   __magic_name__     (				__snake_case    :	Union[str, Any]					,			__snake_case    :	int  )			->    Dict:
       lowercase   :    Union[str, Any]			=							sql_query["agg"]
       if aggregation_op_index >= 3:
              lowercase   :    List[Any]			=							_Aggregation(__snake_case  )
       else:
              lowercase   :    Any			=							_Aggregation.NONE

       lowercase   :    List[str]			=							sql_query["sel"]
       lowercase   :    List[Any]			=							[
           _Condition(__snake_case					,			_Operator(__snake_case  )					,			__snake_case  )
           for column, operator, cmp_value in zip(
               sql_query["conds"]["column_index"]					,			sql_query["conds"]["operator_index"]					,			sql_query["conds"]["condition"]  )
       ]

       lowercase   :    List[Any]			=							[]
       for row in range(len(table["rows"]  )  ):
              if _respect_conditions(__snake_case					,			table["rows"][row]					,			__snake_case  ):
                     indices.append((row, target_column)  )

       if not indices:
              return [], aggregation_op

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

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

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

       return indices, aggregation_op


def   __magic_name__     (				__snake_case    :	Optional[int]					,			__snake_case    :	Optional[int]					,			__snake_case    :	int  )			->    Union[str, Any]:
       if float_answer is not None:
              return [str(__snake_case  )]
       return [str(table["real_rows"][r][c]  ) for r, c in answer_coordinates]


def   __magic_name__     (				__snake_case    :	List[str]					,			__snake_case    :	Optional[int]  )			->    List:
       lowercase		,   lowercase   :    int			=							_get_answer_coordinates(__snake_case					,			__snake_case  )
       lowercase   :    str			=							_get_float_answer(__snake_case					,			__snake_case					,			__snake_case  )
       lowercase   :    Optional[int]			=							_get_answer_text(__snake_case					,			__snake_case					,			__snake_case  )
       # keep the original data the same with TaPas
       if len(__snake_case  ) == 0:
              lowercase   :    int			=							[EMPTY_ANSWER]
       return answer_text




"""simple docstring"""




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
   from PIL import Image

   from transformers import ConvNextImageProcessor

class 		a__						:
   def __init__(      self     ,   _a     ,   _a=13     ,   _a=32     ,   _a=3     ,   _a=4     ,   _a=[10, 20, 30, 40]     ,   _a=[2, 2, 3, 2]     ,   _a=True     ,   _a=True     ,   _a=37     ,   _a="gelu"     ,   _a=10     ,   _a=0.0_2     ,   _a=3     ,   _a=None     ,   ):
          lowercase   :    Any			=							parent
          lowercase   :    Optional[int]			=							batch_size
          lowercase   :    Optional[Any]			=							image_size
          lowercase   :    Any			=							num_channels
          lowercase   :    str			=							num_stages
          lowercase   :    Union[str, Any]			=							hidden_sizes
          lowercase   :    Optional[Any]			=							depths
          lowercase   :    str			=							is_training
          lowercase   :    int			=							use_labels
          lowercase   :    str			=							intermediate_size
          lowercase   :    Dict			=							hidden_act
          lowercase   :    int			=							type_sequence_label_size
          lowercase   :    Optional[int]			=							initializer_range
          lowercase   :    Dict			=							scope
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

          lowercase   :    Any			=							None
          if self.use_labels:
                 lowercase   :    List[str]			=							ids_tensor([self.batch_size]     ,   self.type_sequence_label_size    )

          lowercase   :    str			=							self.get_config()

          return config, pixel_values, labels
   def       __magic_name__      (      self    ):
          return ConvNextConfig(
              num_channels=self.num_channels     ,   hidden_sizes=self.hidden_sizes     ,   depths=self.depths     ,   num_stages=self.num_stages     ,   hidden_act=self.hidden_act     ,   is_decoder=_a     ,   initializer_range=self.initializer_range     ,   )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    Any			=							TFConvNextModel(config=_a    )
          lowercase   :    int			=							model(_a     ,   training=_a    )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,   )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    List[Any]			=							self.type_sequence_label_size
          lowercase   :    List[str]			=							TFConvNextForImageClassification(_a    )
          lowercase   :    int			=							model(_a     ,   labels=_a     ,   training=_a    )
          self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)    )

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


@require_tf
class 		a__						(				a_,      a_,      unittest.TestCase						):
   __lowerCAmelCase						=    (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
   __lowerCAmelCase						=    (
       {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							TFConvNextModelTester(self    )
          lowercase   :    int			=							ConfigTester(
              self     ,   config_class=_a     ,   has_text_modality=_a     ,   hidden_size=37     ,   )
   @unittest.skip(reason="ConvNext does not use inputs_embeds"    )
   def       __magic_name__      (      self    ):
          pass
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) == 0     ,   reason="TF does not support backprop for grouped convolutions on CPU."     ,   )
   @slow
   def       __magic_name__      (      self    ):
          super().test_keras_fit()
   @unittest.skip(reason="ConvNext does not support input and output embeddings"    )
   def       __magic_name__      (      self    ):
          pass
   def       __magic_name__      (      self    ):
          lowercase		,   lowercase   :    List[str]			=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase   :    Dict			=							model_class(_a    )
                 lowercase   :    str			=							inspect.signature(model.call    )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowercase   :    Optional[int]			=							[*signature.parameters.keys()]

                 lowercase   :    Optional[Any]			=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase   :    Union[str, Any]			=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a    )
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) == 0     ,   reason="TF does not support backprop for grouped convolutions on CPU."     ,   )
   def       __magic_name__      (      self    ):
          super().test_dataset_conversion()
   def       __magic_name__      (      self    ):
          def check_hidden_states_output(_a     ,   _a     ,   _a    ):
                 lowercase   :    str			=							model_class(_a    )

                 lowercase   :    Any			=							model(**self._prepare_for_class(_a     ,   _a    )    )
                 lowercase   :    Optional[Any]			=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowercase   :    str			=							self.model_tester.num_stages
                 self.assertEqual(len(_a    )     ,   expected_num_stages + 1    )

                 # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]    )     ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,   )

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

          for model_class in self.all_model_classes:
                 lowercase   :    Union[str, Any]			=							True
                 check_hidden_states_output(_a     ,   _a     ,   _a    )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowercase   :    Dict			=							True

                 check_hidden_states_output(_a     ,   _a     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase		,   lowercase   :    List[str]			=							self.model_tester.prepare_config_and_inputs_for_common()

          def check_equivalence(_a     ,   _a     ,   _a     ,   _a={}    ):
                 lowercase   :    Union[str, Any]			=							model(_a     ,   return_dict=_a     ,   **_a    )
                 lowercase   :    Dict			=							model(_a     ,   return_dict=_a     ,   **_a    ).to_tuple()

                 def recursive_check(_a     ,   _a    ):
                        if isinstance(_a     ,   (List, Tuple)    ):
                               for tuple_iterable_value, dict_iterable_value in zip(_a     ,   _a    ):
                                      recursive_check(_a     ,   _a    )
                        elif tuple_object is None:
                               return
                        else:
                               self.assertTrue(
                                   all(tf.equal(_a     ,   _a    )    )     ,   msg=(
                                       "Tuple and dict output are not equal. Difference:"
                                       f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object    )    )}"""
                                   )     ,   )

                        recursive_check(_a     ,   _a    )

          for model_class in self.all_model_classes:
                 lowercase   :    str			=							model_class(_a    )

                 lowercase   :    int			=							self._prepare_for_class(_a     ,   _a    )
                 lowercase   :    Dict			=							self._prepare_for_class(_a     ,   _a    )
                 check_equivalence(_a     ,   _a     ,   _a    )

                 lowercase   :    Tuple			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 lowercase   :    Union[str, Any]			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 check_equivalence(_a     ,   _a     ,   _a    )

                 lowercase   :    Any			=							self._prepare_for_class(_a     ,   _a    )
                 lowercase   :    List[str]			=							self._prepare_for_class(_a     ,   _a    )
                 check_equivalence(_a     ,   _a     ,   _a     ,   {"output_hidden_states": True}    )

                 lowercase   :    Union[str, Any]			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 lowercase   :    Optional[Any]			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 check_equivalence(_a     ,   _a     ,   _a     ,   {"output_hidden_states": True}    )
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a    )

   @slow
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"    )
          self.assertIsNotNone(_a    )


def   __magic_name__     (				)			->    Any:
       lowercase   :    List[str]			=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image

@require_tf
@require_vision
class 		a__						(				unittest.TestCase						):
   @cached_property
   def       __magic_name__      (      self    ):
          return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"    ) if is_vision_available() else None

   @slow
   def       __magic_name__      (      self    ):
          lowercase   :    Dict			=							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"    )

          lowercase   :    List[str]			=							self.default_image_processor
          lowercase   :    List[str]			=							prepare_img()
          lowercase   :    Union[str, Any]			=							image_processor(images=_a     ,   return_tensors="tf"    )

          # forward pass
          lowercase   :    str			=							model(**_a    )

          # verify the logits
          lowercase   :    List[str]			=							tf.TensorShape((1, 1_000)    )
          self.assertEqual(outputs.logits.shape     ,   _a    )

          lowercase   :    int			=							tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]    )

          tf.debugging.assert_near(outputs.logits[0, :3]     ,   _a     ,   atol=1E-4    )


