'''simple docstring'''






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwiftFormerForImageClassification, SwiftFormerModel
     from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor

class 							a__:







    def __init__(				self			:  Dict						,      __snake_case			:  Optional[int]						,      __snake_case			:  Dict=13						,      __snake_case			:  int=3						,      __snake_case			:  Optional[int]=True						,      __snake_case			:  int=True						,      __snake_case			:  int=0.1						,      __snake_case			:  str=0.1						,      __snake_case			:  Dict=2_24						,      __snake_case			:  List[str]=10_00						,      __snake_case			:  str=[3, 3, 6, 4]						,      __snake_case			:  Dict=[48, 56, 1_12, 2_20]						,      ):
      a						: Tuple					       =					parent
      a						: Optional[int]					       =					batch_size
      a						: Dict					       =					num_channels
      a						: Optional[Any]					       =					is_training
      a						: Any					       =					use_labels
      a						: Dict					       =					hidden_dropout_prob
      a						: List[str]					       =					attention_probs_dropout_prob
      a						: Union[str, Any]					       =					num_labels
      a						: str					       =					image_size
      a						: List[Any]					       =					layer_depths
      a						: Any					       =					embed_dims







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Any					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      a						: str					       =					None
      if self.use_labels:
        a						: Optional[int]					       =					ids_tensor([self.batch_size]						,      self.num_labels     )

      a						: Union[str, Any]					       =					self.get_config()

      return config, pixel_values, labels







    def       lowercase_	(				self			:  str     ):
      return SwiftFormerConfig(
          depths=self.layer_depths						,      embed_dims=self.embed_dims						,      mlp_ratio=4						,      downsamples=[True, True, True, True]						,      hidden_act='gelu'						,      num_labels=self.num_labels						,      down_patch_size=3						,      down_stride=2						,      down_pad=1						,      drop_rate=0.0						,      drop_path_rate=0.0						,      use_layer_scale=__snake_case						,      layer_scale_init_value=1e-5						,      )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Tuple						,      __snake_case			:  str     ):
      a						: Optional[int]					       =					SwiftFormerModel(config=__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Tuple					       =					model(__snake_case     )
      self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.embed_dims[-1], 7, 7)     )







    def       lowercase_	(				self			:  Any						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  List[Any]     ):
      a						: List[str]					       =					self.num_labels
      a						: List[Any]					       =					SwiftFormerForImageClassification(__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Optional[Any]					       =					model(__snake_case						,      labels=__snake_case     )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)     )

      a						: List[Any]					       =					SwiftFormerForImageClassification(__snake_case     )
      model.to(__snake_case     )
      model.eval()

      a						: Union[str, Any]					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
      a						: Union[str, Any]					       =					model(__snake_case     )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)     )







    def       lowercase_	(				self			:  List[str]     ):
      (a)						: List[Any]					       =					self.prepare_config_and_inputs()
      a						: Dict					       =					{'pixel_values': pixel_values}
      return config, inputs_dict

@require_torch
class 							a__( lowerCamelCase__		,			lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase__			=   (
        {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: List[Any]					       =					SwiftFormerModelTester(self     )
      a						: Tuple					       =					ConfigTester(
          self						,      config_class=__snake_case						,      has_text_modality=__snake_case						,      hidden_size=37						,      num_attention_heads=12						,      num_hidden_layers=12						,      )







    def       lowercase_	(				self			:  List[str]     ):
      self.config_tester.run_common_tests()







    @unittest.skip(reason='SwiftFormer does not use inputs_embeds'     )
    def       lowercase_	(				self			:  Any     ):
      pass







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: List[str]					       =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						: Optional[Any]					       =					model_class(__snake_case     )
        a						: Tuple					       =					model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__snake_case						,      nn.Linear     )     )







    def       lowercase_	(				self			:  List[str]     ):
      a						: Union[str, Any]					       =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						: List[str]					       =					model_class(__snake_case     )
        a						: Union[str, Any]					       =					inspect.signature(model.forward     )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a						: Any					       =					[*signature.parameters.keys()]

        a						: Optional[Any]					       =					['pixel_values']
        self.assertListEqual(arg_names[:1]						,      __snake_case     )







    def       lowercase_	(				self			:  str     ):
      a						: Any					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: List[str]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__snake_case     )







    @slow
    def       lowercase_	(				self			:  Optional[Any]     ):
      for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						: Union[str, Any]					       =					SwiftFormerModel.from_pretrained(__snake_case     )
        self.assertIsNotNone(__snake_case     )







    @unittest.skip(reason='SwiftFormer does not output attentions'     )
    def       lowercase_	(				self			:  Dict     ):
      pass







    def       lowercase_	(				self			:  List[str]     ):
      def check_hidden_states_output(__snake_case			:  str						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[int]     ):
        a						: str					       =					model_class(__snake_case     )
        model.to(__snake_case     )
        model.eval()

        with torch.no_grad():
          a						: str					       =					model(**self._prepare_for_class(__snake_case						,      __snake_case     )     )

        a						: List[str]					       =					outputs.hidden_states

        a						: Tuple					       =					8
        self.assertEqual(len(__snake_case     )						,      __snake_case     )  # TODO

        # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
        # with the width and height being successively divided by 2, after every 2 blocks
        for i in range(len(__snake_case     )     ):
          self.assertEqual(
              hidden_states[i].shape						,      torch.Size(
                  [
                      self.model_tester.batch_size,
                      self.model_tester.embed_dims[i // 2],
                      (self.model_tester.image_size // 4) // 2 ** (i // 2),
                      (self.model_tester.image_size // 4) // 2 ** (i // 2),
                  ]     )						,      )

      a						: str					       =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						: Optional[Any]					       =					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"]
        a						: List[str]					       =					True

        check_hidden_states_output(__snake_case						,      __snake_case						,      __snake_case     )







    def       lowercase_	(				self			:  Any     ):
      def _config_zero_init(__snake_case			:  Tuple     ):
        a						: Optional[Any]					       =					copy.deepcopy(__snake_case     )
        for key in configs_no_init.__dict__.keys():
          if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
            setattr(__snake_case						,      __snake_case						,      1e-1_0     )
          if isinstance(getattr(__snake_case						,      __snake_case						,      __snake_case     )						,      __snake_case     ):
            a						: Optional[int]					       =					_config_zero_init(getattr(__snake_case						,      __snake_case     )     )
            setattr(__snake_case						,      __snake_case						,      __snake_case     )
        return configs_no_init

      a						: List[str]					       =					self.model_tester.prepare_config_and_inputs_for_common()

      a						: Optional[int]					       =					_config_zero_init(__snake_case     )
      for model_class in self.all_model_classes:
        a						: str					       =					model_class(config=__snake_case     )
        for name, param in model.named_parameters():
          if param.requires_grad:
            self.assertIn(
                ((param.data.mean() * 1e9) / 1e9).round().item()						,      [0.0, 1.0]						,      msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""						,      )







    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
    def       lowercase_	(				self			:  int     ):
      pass
def 				lowerCamelCase__	(						):
  a						: List[Any]					       =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
  return image

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







    @cached_property
    def       lowercase_	(				self			:  Dict     ):
      return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'     ) if is_vision_available() else None







    @slow
    def       lowercase_	(				self			:  Tuple     ):
      a						: Optional[int]					       =					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'     ).to(__snake_case     )

      a						: Tuple					       =					self.default_image_processor
      a						: Optional[Any]					       =					prepare_img()
      a						: int					       =					image_processor(images=__snake_case						,      return_tensors='pt'     ).to(__snake_case     )

      # forward pass
      with torch.no_grad():
        a						: Any					       =					model(**__snake_case     )

      # verify the logits
      a						: str					       =					torch.Size((1, 10_00)     )
      self.assertEqual(outputs.logits.shape						,      __snake_case     )

      a						: Optional[int]					       =					torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]     ).to(__snake_case     )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]						,      __snake_case						,      atol=1e-4     )     )




'''simple docstring'''






import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 							a__( lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CTRLTokenizer
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  Dict     ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a						: Tuple					       =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
      a						: Union[str, Any]					       =					dict(zip(__snake_case						,      range(len(__snake_case     )     )     )     )
      a						: Union[str, Any]					       =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
      a						: Optional[Any]					       =					{'unk_token': '<unk>'}

      a						: Dict					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']     )
      a						: Optional[Any]					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['merges_file']     )
      with open(self.vocab_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write(json.dumps(__snake_case     ) + '\n'     )
      with open(self.merges_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write('\n'.join(__snake_case     )     )







    def       lowercase_	(				self			:  int						,      **__snake_case			:  str     ):
      kwargs.update(self.special_tokens_map     )
      return CTRLTokenizer.from_pretrained(self.tmpdirname						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Any     ):
      a						: int					       =					'adapt react readapt apt'
      a						: Any					       =					'adapt react readapt apt'
      return input_text, output_text







    def       lowercase_	(				self			:  Dict     ):
      a						: Dict					       =					CTRLTokenizer(self.vocab_file						,      self.merges_file						,      **self.special_tokens_map     )
      a						: List[str]					       =					'adapt react readapt apt'
      a						: Dict					       =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
      a						: Any					       =					tokenizer.tokenize(__snake_case     )
      self.assertListEqual(__snake_case						,      __snake_case     )

      a						: Dict					       =					tokens + [tokenizer.unk_token]

      a						: Optional[Any]					       =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case     )						,      __snake_case     )