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     _A   (    __UpperCamelCase							):






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.config_class(**self.inputs_dict	)
       self.parent.assertTrue(hasattr(A__   ,				'''embed_dim'''	)	)
       self.parent.assertTrue(hasattr(A__   ,				'''num_heads'''	)	)



class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=64   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=[16, 48, 96]   ,				SCREAMING_SNAKE_CASE_=[1, 3, 6]   ,				SCREAMING_SNAKE_CASE_=[1, 2, 10]   ,				SCREAMING_SNAKE_CASE_=[7, 3, 3]   ,				SCREAMING_SNAKE_CASE_=[4, 2, 2]   ,				SCREAMING_SNAKE_CASE_=[2, 1, 1]   ,				SCREAMING_SNAKE_CASE_=[2, 2, 2]   ,				SCREAMING_SNAKE_CASE_=[False, False, True]   ,				SCREAMING_SNAKE_CASE_=[0.0, 0.0, 0.0]   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=2   ,				)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_sizes
       UpperCamelCase__										=      patch_stride
       UpperCamelCase__										=      patch_padding
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_labels
       UpperCamelCase__										=      num_labels
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      embed_dim
       UpperCamelCase__										=      num_heads
       UpperCamelCase__										=      stride_kv
       UpperCamelCase__										=      depth
       UpperCamelCase__										=      cls_token
       UpperCamelCase__										=      attention_drop_rate
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

       UpperCamelCase__										=      self.get_config()
       return config, pixel_values, labels






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      TFCvtModel(config=A__	)
       UpperCamelCase__										=      model(A__   ,				training=A__	)
       UpperCamelCase__										=      (self.image_size, self.image_size)
       UpperCamelCase__										=      image_size[0], image_size[1]
       for i in range(len(self.depth	)	):
            UpperCamelCase__										=      floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
            UpperCamelCase__										=      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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.num_labels
       UpperCamelCase__										=      TFCvtForImageClassification(A__	)
       UpperCamelCase__										=      model(A__   ,				labels=A__   ,				training=A__	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__										=      config_and_inputs
       UpperCamelCase__										=      {"""pixel_values""": pixel_values}
       return config, inputs_dict



@require_tf
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	str						=(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE_	:	List[Any]						=(
      {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE_	:	str						=False
  SCREAMING_SNAKE_CASE_	:	Any						=False
  SCREAMING_SNAKE_CASE_	:	Union[str, Any]						=False
  SCREAMING_SNAKE_CASE_	:	int						=False
  SCREAMING_SNAKE_CASE_	:	Tuple						=False






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      TFCvtModelTester(self	)
       UpperCamelCase__										=      TFCvtConfigTester(self   ,				config_class=A__   ,				has_text_modality=A__   ,				hidden_size=37	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       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]:





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       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	)		->      List[str]:





       '''simple docstring'''





       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	)		->      Dict:





       '''simple docstring'''





       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:





       '''simple docstring'''





       UpperCamelCase__										=      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	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCamelCase__										=      ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]   ,				A__	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       def check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      model_class(A__	)

            UpperCamelCase__										=      model(**self._prepare_for_class(A__   ,				A__	)	)
            UpperCamelCase__										=      outputs.hidden_states

            UpperCamelCase__										=      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,
                ]   ,				)

       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__										=      True
            check_hidden_states_output(A__   ,				A__   ,				A__	)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCamelCase__										=      True

            check_hidden_states_output(A__   ,				A__   ,				A__	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





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






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A__	)






  @slow
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__										=      TFCvtModel.from_pretrained(A__	)
            self.assertIsNotNone(A__	)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image



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






  @cached_property
  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)






  @slow
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=A__   ,				return_tensors='''tf'''	)

       # forward pass
       UpperCamelCase__										=      model(**A__	)

       # verify the logits
       UpperCamelCase__										=      tf.TensorShape((1, 1000)	)
       self.assertEqual(outputs.logits.shape   ,				A__	)

       UpperCamelCase__										=      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

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     UpperCamelCase__										=      list(''' ''' + ascii_lowercase							)
     # what is our total sum of probabilities.
     UpperCamelCase__										=      sum(single_char_strings.values()							)

     # one length string
     UpperCamelCase__										=      0
     # for each alpha we go in our dict and if it is in it we calculate entropy
     for ch in my_alphas:
          if ch in single_char_strings:
               UpperCamelCase__										=      single_char_strings[ch]
               UpperCamelCase__										=      my_str / all_sum
               my_fir_sum += prob * math.loga(A							)  # entropy formula.

    # print entropy
     print(f"{round(-1 * my_fir_sum							):.1f}"							)

     # two len string
     UpperCamelCase__										=      sum(two_char_strings.values()							)
     UpperCamelCase__										=      0
     # for each alpha (two in size) calculate entropy.
     for cha in my_alphas:
          for cha in my_alphas:
               UpperCamelCase__										=      cha + cha
               if sequence in two_char_strings:
                    UpperCamelCase__										=      two_char_strings[sequence]
                    UpperCamelCase__										=      int(A							) / all_sum
                    my_sec_sum += prob * math.loga(A							)

    # print second entropy
     print(f"{round(-1 * my_sec_sum							):.1f}"							)

     # print the difference between them
     print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      Counter()  # type: ignore
     UpperCamelCase__										=      Counter()  # type: ignore
     single_char_strings[text[-1]] += 1

     # first case when we have space at start.
     two_char_strings[" " + text[0]] += 1
     for i in range(0 ,							len(A							) - 1							):
          single_char_strings[text[i]] += 1
          two_char_strings[text[i : i + 2]] += 1
     return single_char_strings, two_char_strings







def  __UpperCamelCase    (						):
     import doctest

     doctest.testmod()
     # text = (
     #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
     #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
     #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
     #     "beloved evident through no service elderly is. Blind there if every no so "
     #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
     #     "of message cottage windows do besides against uncivil.  Delightful "
     #     "unreserved impossible few estimating men favourable see entreaties. She "
     #     "propriety immediate was improving. He or entrance humoured likewise "
     #     "moderate. Much nor game son say feel. Fat make met can must form into "
     #     "gate. Me we offending prevailed discovery. "
     # )

     # calculate_prob(text)


if __name__ == "__main__":
     main()



