




from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
  from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							int:
           _A							:  List[Any]    =		self.config_class(**self.inputs_dict       )
           self.parent.assertTrue(hasattr(_a	,  """embed_dim"""       )       )
           self.parent.assertTrue(hasattr(_a	,  """num_heads"""       )       )







class 		lowercase							:

     def __init__(						self	,  _a	,  _a=13	,  _a=64	,  _a=3	,  _a=[16, 48, 96]	,  _a=[1, 3, 6]	,  _a=[1, 2, 10]	,  _a=[7, 3, 3]	,  _a=[4, 2, 2]	,  _a=[2, 1, 1]	,  _a=[2, 2, 2]	,  _a=[False, False, True]	,  _a=[0.0, 0.0, 0.0]	,  _a=0.02	,  _a=1e-12	,  _a=True	,  _a=True	,  _a=2	,  )		->							Optional[int]:
           _A							:  str    =		parent
           _A							:  Union[str, Any]    =		batch_size
           _A							:  int    =		image_size
           _A							:  Optional[int]    =		patch_sizes
           _A							:  List[str]    =		patch_stride
           _A							:  Any    =		patch_padding
           _A							:  int    =		is_training
           _A							:  List[str]    =		use_labels
           _A							:  Tuple    =		num_labels
           _A							:  Tuple    =		num_channels
           _A							:  Optional[int]    =		embed_dim
           _A							:  List[str]    =		num_heads
           _A							:  Union[str, Any]    =		stride_kv
           _A							:  Union[str, Any]    =		depth
           _A							:  Dict    =		cls_token
           _A							:  Dict    =		attention_drop_rate
           _A							:  List[Any]    =		initializer_range
           _A							:  Any    =		layer_norm_eps

     def       a__			(						self       )		->							Dict:
           _A							:  Optional[Any]    =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           _A							:  Union[str, Any]    =		None
           if self.use_labels:
                 # create a random int32 tensor of given shape
                 _A							:  List[Any]    =		ids_tensor([self.batch_size]	,  self.num_labels       )

           _A							:  List[str]    =		self.get_config()
           return config, pixel_values, labels

     def       a__			(						self       )		->							List[Any]:
           return CvtConfig(
               image_size=self.image_size	,  num_labels=self.num_labels	,  num_channels=self.num_channels	,  embed_dim=self.embed_dim	,  num_heads=self.num_heads	,  patch_sizes=self.patch_sizes	,  patch_padding=self.patch_padding	,  patch_stride=self.patch_stride	,  stride_kv=self.stride_kv	,  depth=self.depth	,  cls_token=self.cls_token	,  attention_drop_rate=self.attention_drop_rate	,  initializer_range=self.initializer_range	,  )

     def       a__			(						self	,  _a	,  _a	,  _a       )		->							Optional[Any]:
           _A							:  Dict    =		TFCvtModel(config=_a       )
           _A							:  int    =		model(_a	,  training=_a       )
           _A							:  Union[str, Any]    =		(self.image_size, self.image_size)
           _A    ,     _A							:  Union[str, Any]    =		image_size[0], image_size[1]
           for i in range(len(self.depth       )       ):
                 _A							:  Optional[int]    =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
                 _A							:  Dict    =		floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
           self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.embed_dim[-1], height, width)       )

     def       a__			(						self	,  _a	,  _a	,  _a       )		->							List[str]:
           _A							:  Any    =		self.num_labels
           _A							:  str    =		TFCvtForImageClassification(_a       )
           _A							:  List[Any]    =		model(_a	,  labels=_a	,  training=_a       )
           self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)       )



     def       a__			(						self       )		->							Tuple:
           _A							:  List[Any]    =		self.prepare_config_and_inputs()
           _A    ,     _A    ,     _A							:  Tuple    =		config_and_inputs
           _A							:  Any    =		{"""pixel_values""": pixel_values}
           return config, inputs_dict







@require_tf
class 		lowercase							(     UpperCamelCase__,UpperCamelCase__,unittest.TestCase	):
     _a											=		(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
     _a											=		(
         {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
         if is_tf_available()
         else {}
     )
     _a											=		False
     _a											=		False
     _a											=		False
     _a											=		False
     _a											=		False

     def       a__			(						self       )		->							List[Any]:
           _A							:  Union[str, Any]    =		TFCvtModelTester(self       )
           _A							:  str    =		TFCvtConfigTester(self	,  config_class=_a	,  has_text_modality=_a	,  hidden_size=37       )

     def       a__			(						self       )		->							str:
           self.config_tester.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()

     @unittest.skip(reason="""Cvt does not output attentions"""       )
     def       a__			(						self       )		->							List[str]:
           pass

     @unittest.skip(reason="""Cvt does not use inputs_embeds"""       )
     def       a__			(						self       )		->							List[Any]:
           pass

     @unittest.skip(reason="""Cvt does not support input and output embeddings"""       )
     def       a__			(						self       )		->							str:
           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."""	,  )
     def       a__			(						self       )		->							Union[str, Any]:
           super().test_dataset_conversion()

     @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       a__			(						self       )		->							List[str]:
           super().test_keras_fit()

     @unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"""       )
     def       a__			(						self       )		->							int:
           _A							:  Any    =		tf.keras.mixed_precision.Policy("""mixed_float16"""       )
           tf.keras.mixed_precision.set_global_policy(_a       )
           super().test_keras_fit()
           tf.keras.mixed_precision.set_global_policy("""float32"""       )

     def       a__			(						self       )		->							Union[str, Any]:
           _A    ,     _A							:  Dict    =		self.model_tester.prepare_config_and_inputs_for_common()

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

                 _A							:  Dict    =		["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]	,  _a       )

     def       a__			(						self       )		->							Optional[Any]:
           def check_hidden_states_output(_a	,  _a	,  _a       ):
                 _A							:  Union[str, Any]    =		model_class(_a       )

                 _A							:  Union[str, Any]    =		model(**self._prepare_for_class(_a	,  _a       )       )
                 _A							:  Optional[int]    =		outputs.hidden_states

                 _A							:  Optional[Any]    =		len(self.model_tester.depth       )
                 self.assertEqual(len(_a       )	,  _a       )

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:]       )	,  [
                         self.model_tester.embed_dim[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]	,  )

           _A    ,     _A							:  Optional[Any]    =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 _A							:  Any    =		True
                 check_hidden_states_output(_a	,  _a	,  _a       )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 _A							:  Optional[Any]    =		True

                 check_hidden_states_output(_a	,  _a	,  _a       )

     def       a__			(						self       )		->							Dict:
           _A							:  str    =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_a       )

     def       a__			(						self       )		->							Tuple:
           _A							:  str    =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_a       )



     @slow
     def       a__			(						self       )		->							Any:
           for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A							:  Optional[Any]    =		TFCvtModel.from_pretrained(_a       )
                 self.assertIsNotNone(_a       )







def        lowerCAmelCase_			(  ):
      _A							:  str    =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
      return image







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

     @cached_property
     def       a__			(						self       )		->							int:
           return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )



     @slow
     def       a__			(						self       )		->							Tuple:
           _A							:  Dict    =		TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

           _A							:  Optional[Any]    =		self.default_image_processor
           _A							:  Tuple    =		prepare_img()
           _A							:  Optional[Any]    =		image_processor(images=_a	,  return_tensors="""tf"""       )

           # forward pass
           _A							:  int    =		model(**_a       )

           # verify the logits
           _A							:  Dict    =		tf.TensorShape((1, 1000)       )
           self.assertEqual(outputs.logits.shape	,  _a       )

           _A							:  Union[str, Any]    =		tf.constant([0.9285, 0.9015, -0.3150]       )
           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()	,  _a	,  atol=1e-4       )       )







from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		list(snake_case_  )
      _A							:  List[Any]    =		list(snake_case_  )
      _A							:  Tuple    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count += 1
                  _A							:  Optional[Any]    =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]
      while True:
            _A							:  int    =		["""$"""] * len(snake_case_  )
            _A							:  Any    =		[]
            for i in range(len(snake_case_  )  ):
                  for j in range(i + 1,len(snake_case_  )  ):
                        _A							:  Tuple    =		compare_string(binary[i],binary[j]  )
                        if k is False:
                              _A							:  str    =		"""*"""
                              _A							:  str    =		"""*"""
                              temp.append("""X"""  )
            for i in range(len(snake_case_  )  ):
                  if checka[i] == "$":
                        pi.append(binary[i]  )
            if len(snake_case_  ) == 0:
                  return pi
            _A							:  Dict    =		list(set(snake_case_  )  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		[]
      for minterm in minterms:
            _A							:  Tuple    =		""""""
            for _ in range(snake_case_  ):
                  _A							:  Optional[Any]    =		str(minterm % 2  ) + string
                  minterm //= 2
            temp.append(snake_case_  )
      return temp







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		list(snake_case_  )
      _A							:  Tuple    =		list(snake_case_  )
      _A							:  Dict    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[]
      _A							:  str    =		[0] * len(snake_case_  )
      for i in range(len(chart[0]  )  ):
            _A							:  Union[str, Any]    =		0
            _A							:  Optional[Any]    =		-1
            for j in range(len(snake_case_  )  ):
                  if chart[j][i] == 1:
                        count += 1
                        _A							:  Dict    =		j
            if count == 1:
                  _A							:  int    =		1
      for i in range(len(snake_case_  )  ):
            if select[i] == 1:
                  for j in range(len(chart[0]  )  ):
                        if chart[i][j] == 1:
                              for k in range(len(snake_case_  )  ):
                                    _A							:  int    =		0
                  temp.append(prime_implicants[i]  )
      while True:
            _A							:  Optional[Any]    =		0
            _A							:  Tuple    =		-1
            _A							:  List[Any]    =		0
            for i in range(len(snake_case_  )  ):
                  _A							:  List[str]    =		chart[i].count(1  )
                  if count_n > max_n:
                        _A							:  Optional[int]    =		count_n
                        _A							:  Tuple    =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]  )

            for i in range(len(chart[0]  )  ):
                  if chart[rem][i] == 1:
                        for j in range(len(snake_case_  )  ):
                              _A							:  Optional[int]    =		0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[[0 for x in range(len(snake_case_  )  )] for x in range(len(snake_case_  )  )]
      for i in range(len(snake_case_  )  ):
            _A							:  List[Any]    =		prime_implicants[i].count("""_"""  )
            for j in range(len(snake_case_  )  ):
                  if is_for_table(prime_implicants[i],binary[j],snake_case_  ):
                        _A							:  Union[str, Any]    =		1

      return chart







def        lowerCAmelCase_			(  ):
      _A							:  Dict    =		int(input("""Enter the no. of variables\n"""  )  )
      _A							:  Dict    =		[
          float(snake_case_  )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""  ).split()
      ]
      _A							:  int    =		decimal_to_binary(snake_case_,snake_case_  )

      _A							:  Optional[Any]    =		check(snake_case_  )
      print("""Prime Implicants are:"""  )
      print(snake_case_  )
      _A							:  int    =		prime_implicant_chart(snake_case_,snake_case_  )

      _A							:  int    =		selection(snake_case_,snake_case_  )
      print("""Essential Prime Implicants are:"""  )
      print(snake_case_  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

