
"""simple docstring"""
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import RegNetForImageClassification, RegNetModel
    from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class      _UpperCAmelCase				:
    def __init__(    self						,    snake_case_						,    snake_case_=3						,    snake_case_=32						,    snake_case_=3						,    snake_case_=10						,    snake_case_=[10, 20, 30, 40]						,    snake_case_=[1, 1, 2, 1]						,    snake_case_=True						,    snake_case_=True						,    snake_case_="relu"						,    snake_case_=3						,    snake_case_=None						,    ):
      _snake_case     :						List[Any]						=   parent
      _snake_case     :						Optional[Any]						=   batch_size
      _snake_case     :						Optional[Any]						=   image_size
      _snake_case     :						str						=   num_channels
      _snake_case     :						str						=   embeddings_size
      _snake_case     :						str						=   hidden_sizes
      _snake_case     :						int						=   depths
      _snake_case     :						Optional[int]						=   is_training
      _snake_case     :						Optional[Any]						=   use_labels
      _snake_case     :						List[Any]						=   hidden_act
      _snake_case     :						Union[str, Any]						=   num_labels
      _snake_case     :						int						=   scope
      _snake_case     :						List[Any]						=   len(snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      _snake_case     :						List[Any]						=   None
      if self.use_labels:
        _snake_case     :						Optional[Any]						=   ids_tensor([self.batch_size]						,    self.num_labels     )

      _snake_case     :						Dict						=   self.get_config()

      return config, pixel_values, labels
    def 					lowerCamelCase__	(    self     ):
      return RegNetConfig(
          num_channels=self.num_channels						,    embeddings_size=self.embeddings_size						,    hidden_sizes=self.hidden_sizes						,    depths=self.depths						,    hidden_act=self.hidden_act						,    num_labels=self.num_labels						,    )
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Optional[int]						=   RegNetModel(config=snake_case_     )
      model.to(snake_case_     )
      model.eval()
      _snake_case     :						str						=   model(snake_case_     )
      # 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 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Any						=   self.num_labels
      _snake_case     :						List[str]						=   RegNetForImageClassification(snake_case_     )
      model.to(snake_case_     )
      model.eval()
      _snake_case     :						List[Any]						=   model(snake_case_						,    labels=snake_case_     )
      self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)     )


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   self.prepare_config_and_inputs()
      _snake_case     :						int						=   config_and_inputs
      _snake_case     :						str						=   {"pixel_values": pixel_values}
      return config, inputs_dict






@require_torch
class      _UpperCAmelCase				(					_snake_case		,					_snake_case		,					unittest.TestCase):
    __lowercase							: Dict  =					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    __lowercase							: Dict  =					(
        {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

    __lowercase							: Optional[Any]  =					False
    __lowercase							: Dict  =					False
    __lowercase							: List[Any]  =					False
    __lowercase							: Optional[int]  =					False
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   RegNetModelTester(self     )
      _snake_case     :						int						=   ConfigTester(self						,    config_class=snake_case_						,    has_text_modality=snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      self.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()
    def 					lowerCamelCase__	(    self     ):
      return
    @unittest.skip(reason="RegNet does not use inputs_embeds"     )
    def 					lowerCamelCase__	(    self     ):
      pass
    @unittest.skip(reason="RegNet does not support input and output embeddings"     )
    def 					lowerCamelCase__	(    self     ):
      pass
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _snake_case     :						Any						=   model_class(snake_case_     )
        _snake_case     :						Optional[int]						=   inspect.signature(model.forward     )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        _snake_case     :						List[str]						=   [*signature.parameters.keys()]

        _snake_case     :						Dict						=   ["pixel_values"]
        self.assertListEqual(arg_names[:1]						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[Any]						=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _snake_case     :						Union[str, Any]						=   model_class(config=snake_case_     )
        for name, module in model.named_modules():
          if isinstance(snake_case_						,    (nn.BatchNormad, nn.GroupNorm)     ):
            self.assertTrue(
                torch.all(module.weight == 1     )						,    msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,    )
            self.assertTrue(
                torch.all(module.bias == 0     )						,    msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,    )
    def 					lowerCamelCase__	(    self     ):
      def check_hidden_states_output(snake_case_						,    snake_case_						,    snake_case_     ):
        _snake_case     :						List[str]						=   model_class(snake_case_     )
        model.to(snake_case_     )
        model.eval()

        with torch.no_grad():
          _snake_case     :						Tuple						=   model(**self._prepare_for_class(snake_case_						,    snake_case_     )     )

        _snake_case     :						str						=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        _snake_case     :						Optional[int]						=   self.model_tester.num_stages
        self.assertEqual(len(snake_case_     )						,    expected_num_stages + 1     )

        # RegNet'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 // 2, self.model_tester.image_size // 2]						,    )

      _snake_case     :						List[str]						=   self.model_tester.prepare_config_and_inputs_for_common()
      _snake_case     :						str						=   ["basic", "bottleneck"]
      for model_class in self.all_model_classes:
        for layer_type in layers_type:
          _snake_case     :						str						=   layer_type
          _snake_case     :						List[str]						=   True
          check_hidden_states_output(snake_case_						,    snake_case_						,    snake_case_     )

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

          check_hidden_states_output(snake_case_						,    snake_case_						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*snake_case_     )


    @slow
    def 					lowerCamelCase__	(    self     ):
      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _snake_case     :						Tuple						=   RegNetModel.from_pretrained(snake_case_     )
        self.assertIsNotNone(snake_case_     )


def 				a__   (   ):

  """simple docstring"""







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






@require_torch
@require_vision
class      _UpperCAmelCase				(					unittest.TestCase):
    @cached_property
    def 					lowerCamelCase__	(    self     ):
      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
          if is_vision_available()
          else None
      )


    @slow
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(snake_case_     )

      _snake_case     :						Dict						=   self.default_image_processor
      _snake_case     :						List[Any]						=   prepare_img()
      _snake_case     :						Tuple						=   image_processor(images=snake_case_						,    return_tensors="pt"     ).to(snake_case_     )

      # forward pass
      with torch.no_grad():
        _snake_case     :						int						=   model(**snake_case_     )

      # verify the logits
      _snake_case     :						List[str]						=   torch.Size((1, 10_00)     )
      self.assertEqual(outputs.logits.shape						,    snake_case_     )

      _snake_case     :						Optional[int]						=   torch.tensor([-0.4180, -1.5051, -3.4836]     ).to(snake_case_     )

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



"""simple docstring"""
from __future__ import annotations






class      _UpperCAmelCase				:
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      _snake_case			,					_snake_case     :						Dict						=   text, pattern
      _snake_case			,					_snake_case     :						int						=   len(snake_case_     ), len(snake_case_     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if char == self.pattern[i]:
          return i
      return -1
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if self.pattern[i] != self.text[current_pos + i]:
          return current_pos + i
      return -1


    def 					lowerCamelCase__	(    self     ):
      # searches pattern in text and returns index positions
      _snake_case     :						List[str]						=   []
      for i in range(self.textLen - self.patLen + 1     ):
        _snake_case     :						Union[str, Any]						=   self.mismatch_in_text(snake_case_     )
        if mismatch_index == -1:
          positions.append(snake_case_     )
        else:
          _snake_case     :						Tuple						=   self.match_in_pattern(self.text[mismatch_index]     )
          _snake_case     :						Tuple						=   (
              mismatch_index - match_index
          )  # shifting index lgtm [py/multiple-definition]
      return positions


_a      :			List[Any]					=							"""ABAABA"""
_a      :			str					=							"""AB"""
_a      :			List[Any]					=							BoyerMooreSearch(text, pattern)
_a      :			Any					=							bms.bad_character_heuristic()

if len(positions) == 0:
    print("""No match found""")
else:
    print("""Pattern found in following positions: """)
    print(positions)

