






"""simple docstring"""



import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
    from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import PoolFormerImageProcessor

class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.config_class(**self.inputs_dict			)
      self.parent.assertTrue(hasattr(_lowerCamelCase				,						'hidden_sizes'			)			)
      self.parent.assertTrue(hasattr(_lowerCamelCase				,						'num_encoder_blocks'			)			)


class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=1_3				,						_lowerCamelCase=6_4				,						_lowerCamelCase=3				,						_lowerCamelCase=4				,						_lowerCamelCase=[2, 2, 2, 2]				,						_lowerCamelCase=[8, 4, 2, 1]				,						_lowerCamelCase=[1_6, 3_2, 6_4, 1_2_8]				,						_lowerCamelCase=[1, 4, 8, 1_6]				,						_lowerCamelCase=False				,						_lowerCamelCase=True				,						_lowerCamelCase="gelu"				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase=3				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			num_channels
      lowercase				       =			num_encoder_blocks
      lowercase				       =			sr_ratios
      lowercase				       =			depths
      lowercase				       =			hidden_sizes
      lowercase				       =			downsampling_rates
      lowercase				       =			is_training
      lowercase				       =			use_labels
      lowercase				       =			hidden_act
      lowercase				       =			hidden_dropout_prob
      lowercase				       =			initializer_range
      lowercase				       =			num_labels
      lowercase				       =			scope







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      lowercase				       =			None
      if self.use_labels:
        lowercase				       =			ids_tensor([self.batch_size, self.image_size, self.image_size]				,						self.num_labels			)

      lowercase				       =			PoolFormerConfig(
          image_size=self.image_size				,						num_channels=self.num_channels				,						num_encoder_blocks=self.num_encoder_blocks				,						depths=self.depths				,						hidden_sizes=self.hidden_sizes				,						hidden_act=self.hidden_act				,						hidden_dropout_prob=self.hidden_dropout_prob				,						initializer_range=self.initializer_range				,						)

      return config, pixel_values, labels







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			PoolFormerModel(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase			)
      lowercase				       =			lowercase				       =			self.image_size // 3_2.0
      self.parent.assertEqual(
          result.last_hidden_state.shape				,						(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      lowercase       ,	lowercase       ,	lowercase				       =			config_and_inputs
      lowercase				       =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	List[str]      	=(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
    UpperCAmelCase_					:	List[str]      	=(
        {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase_					:	List[Any]      	=False
    UpperCAmelCase_					:	Union[str, Any]      	=False
    UpperCAmelCase_					:	int      	=False
    UpperCAmelCase_					:	List[str]      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			PoolFormerModelTester(self			)
      lowercase				       =			PoolFormerConfigTester(self				,						config_class=_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      self.config_tester.run_common_tests()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase			)







    @unittest.skip('PoolFormer does not use inputs_embeds'			)
    def      UpperCamelCase_ (     self			):
      pass







    @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'			)
    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      def check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.eval()

        with torch.no_grad():
          lowercase				       =			model(**self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)			)

        lowercase				       =			outputs.hidden_states

        lowercase				       =			self.model_tester.num_encoder_blocks
        self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)

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

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			True
        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)

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

        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      if not self.model_tester.is_training:
        return

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()
      lowercase				       =			True

      for model_class in self.all_model_classes:
        if model_class in get_values(_lowerCamelCase			):
          continue
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.train()
        lowercase				       =			self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase				,						return_labels=_lowerCamelCase			)
        lowercase				       =			model(**_lowerCamelCase			).loss
        loss.backward()







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

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

        lowercase				       =			['pixel_values']
        self.assertListEqual(arg_names[:1]				,						_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase				       =			PoolFormerModel.from_pretrained(_lowerCamelCase			)
        self.assertIsNotNone(_lowerCamelCase			)


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image



@require_torch
class 			a	(  unittest.TestCase      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			PoolFormerImageProcessor()
      lowercase				       =			PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'			).to(_lowerCamelCase			)

      lowercase				       =			image_processor(images=prepare_img()				,						return_tensors='pt'			).to(_lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(**_lowerCamelCase			)

      # verify the logits
      lowercase				       =			torch.Size((1, 1_0_0_0)			)
      self.assertEqual(outputs.logits.shape				,						_lowerCamelCase			)

      lowercase				       =			torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]			).to(_lowerCamelCase			)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)










"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list

class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			'<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '			)
      lowercase				       =			dict(zip(_lowerCamelCase				,						range(len(_lowerCamelCase			)			)			)			)

      lowercase				       =			{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      lowercase				       =			{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }

      lowercase				       =			tempfile.mkdtemp()
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)
      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)

      with open(self.feature_extraction_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(json.dumps(_lowerCamelCase			) + '\n'			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      lowercase				       =			self.add_kwargs_tokens_map.copy()
      kwargs.update(_lowerCamelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_feature_extractor()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      processor.save_pretrained(self.tmpdirname			)
      lowercase				       =			WavaVecaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			WavaVecaProcessor(tokenizer=self.get_tokenizer()				,						feature_extractor=self.get_feature_extractor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_feature_extractor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			WavaVecaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.feature_extractor.to_json_string()				,						feature_extractor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.feature_extractor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			floats_list((3, 1_0_0_0)			)

      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			'This is a test string'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      self.assertListEqual(
          processor.model_input_names				,						feature_extractor.model_input_names				,						msg='`processor` and `feature_extractor` model input names do not match'				,						)


