"""simple docstring"""





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_UpperCamelCase     :		Any											=					[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def        a_  (   _lowerCAmelCase						:				Optional[Any]=True  ):





       '''simple docstring'''

       if with_config:
              return [
                  {
                      "testcase_name": d["dataset"] + "/" + d["config_name"],
                      "dataset": d["dataset"],
                      "config_name": d["config_name"],
                  }
                  for d in DATASETS_ON_HF_GCP
              ]
       else:
              return [
                  {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
              ]


@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_a))
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					str										=						None
      lowerCamelCase__	:					Optional[Any]										=						None


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      List[Any]:
             with TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[str]     					=			dataset_module_factory(a	,    cache_dir=a							)

                    lowercase__    :	List[Any]     					=			import_main_class(dataset_module.module_path	,    dataset=a							)

                    lowercase__    :	DatasetBuilder     					=			builder_cls(
                        cache_dir=a	,    config_name=a	,    hash=dataset_module.hash	,    )

                    lowercase__    :	Union[str, Any]     					=			'/'.join(
                        [
                            HF_GCP_BASE_URL,
                            builder_instance._relative_data_dir(with_hash=a							).replace(os.sep	,    '/'							),
                            config.DATASET_INFO_FILENAME,
                        ]							)
                    lowercase__    :	Union[str, Any]     					=			cached_path(a	,    cache_dir=a							)
                    self.assertTrue(os.path.exists(a							)							)





@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
       lowercase__    :	int     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	Optional[int]     					=			import_main_class(dataset_module.module_path  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
       lowercase__    :	Optional[int]     					=			None
       builder_instance.download_and_prepare()
       lowercase__    :	Optional[int]     					=			builder_instance.as_dataset()
       assert ds






@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	List[str]     					=			import_main_class(dataset_module.module_path     ,				dataset=_lowerCAmelCase  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       lowercase__    :	Union[str, Any]     					=			builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  )
       assert "train" in ds
       assert isinstance(ds['train']     ,				_lowerCAmelCase  )
       assert next(iter(ds['train']  )  )


"""simple docstring"""





import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import YolosForObjectDetection, YolosModel
       from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=[3_0, 3_0]	,    a=2	,    a=3	,    a=True	,    a=True	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=1_0	,    a=0.02	,    a=3	,    a=None	,    a=8	,    a=1_0	,    ) ->      Any:
             lowercase__    :	List[str]     					=			parent
             lowercase__    :	Optional[Any]     					=			batch_size
             lowercase__    :	Optional[int]     					=			image_size
             lowercase__    :	List[Any]     					=			patch_size
             lowercase__    :	Optional[Any]     					=			num_channels
             lowercase__    :	str     					=			is_training
             lowercase__    :	Optional[Any]     					=			use_labels
             lowercase__    :	Optional[Any]     					=			hidden_size
             lowercase__    :	Dict     					=			num_hidden_layers
             lowercase__    :	Optional[Any]     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[Any]     					=			hidden_act
             lowercase__    :	List[Any]     					=			hidden_dropout_prob
             lowercase__    :	Any     					=			attention_probs_dropout_prob
             lowercase__    :	Any     					=			type_sequence_label_size
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	Union[str, Any]     					=			num_labels
             lowercase__    :	Tuple     					=			scope
             lowercase__    :	Tuple     					=			n_targets
             lowercase__    :	Optional[int]     					=			num_detection_tokens
             # we set the expected sequence length (which is used in several tests)
             # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
             lowercase__    :	Optional[Any]     					=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
             lowercase__    :	Tuple     					=			num_patches + 1 + self.num_detection_tokens


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Union[str, Any]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

             lowercase__    :	Tuple     					=			None
             if self.use_labels:
                    # labels is a list of Dict (each Dict being the labels for a given example in the batch)
                    lowercase__    :	int     					=			[]
                    for i in range(self.batch_size							):
                           lowercase__    :	Optional[Any]     					=			{}
                           lowercase__    :	Any     					=			torch.randint(
                               high=self.num_labels	,    size=(self.n_targets,)	,    device=a							)
                           lowercase__    :	List[str]     					=			torch.rand(self.n_targets	,    4	,    device=a							)
                           labels.append(a							)

             lowercase__    :	Tuple     					=			self.get_config()

             return config, pixel_values, labels


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return YolosConfig(
                 image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    is_decoder=a	,    initializer_range=self.initializer_range	,    num_detection_tokens=self.num_detection_tokens	,    num_labels=self.num_labels	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      int:
             lowercase__    :	List[str]     					=			YolosModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	List[Any]     					=			model(a							)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,    (self.batch_size, self.expected_seq_len, self.hidden_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Union[str, Any]:
             lowercase__    :	str     					=			YolosForObjectDetection(a							)
             model.to(a							)
             model.eval()

             lowercase__    :	Dict     					=			model(pixel_values=a							)
             lowercase__    :	Tuple     					=			model(a							)

             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
             self.parent.assertEqual(result.pred_boxes.shape	,    (self.batch_size, self.num_detection_tokens, 4)							)

             lowercase__    :	str     					=			model(pixel_values=a	,    labels=a							)

             self.parent.assertEqual(result.loss.shape	,    ()							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
             self.parent.assertEqual(result.pred_boxes.shape	,    (self.batch_size, self.num_detection_tokens, 4)							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	int     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__    :	Any     					=			config_and_inputs
             lowercase__    :	Any     					=			{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[int]										=						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      lowerCamelCase__	:					List[str]										=						(
          {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
      )

      lowerCamelCase__	:					List[Any]										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					Tuple										=						False
      lowerCamelCase__	:					Union[str, Any]										=						False


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a=False							) ->      Dict:
             lowercase__    :	List[str]     					=			super()._prepare_for_class(a	,    a	,    return_labels=a							)

             if return_labels:
                    if model_class.__name__ == "YolosForObjectDetection":
                           lowercase__    :	Optional[Any]     					=			[]
                           for i in range(self.model_tester.batch_size							):
                                  lowercase__    :	Dict     					=			{}
                                  lowercase__    :	Dict     					=			torch.ones(
                                      size=(self.model_tester.n_targets,)	,    device=a	,    dtype=torch.long							)
                                  lowercase__    :	Optional[Any]     					=			torch.ones(
                                      self.model_tester.n_targets	,    4	,    device=a	,    dtype=torch.float							)
                                  labels.append(a							)
                           lowercase__    :	Union[str, Any]     					=			labels

             return inputs_dict


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Dict     					=			YolosModelTester(self							)
             lowercase__    :	Optional[int]     					=			ConfigTester(self	,    config_class=a	,    has_text_modality=a	,    hidden_size=3_7							)


      def 	_UpperCAmelCase (     self							) ->      str:
             self.config_tester.run_common_tests()


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             # YOLOS does not use inputs_embeds
             pass


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	List[str]     					=			model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)							)
                    lowercase__    :	List[str]     					=			model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a	,    nn.Linear							)							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

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


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__					,   lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	Dict     					=			True

             # in YOLOS, the seq_len is different
             lowercase__    :	Tuple     					=			self.model_tester.expected_seq_len
             for model_class in self.all_model_classes:
                    lowercase__    :	Optional[int]     					=			True
                    lowercase__    :	str     					=			False
                    lowercase__    :	str     					=			True
                    lowercase__    :	List[str]     					=			model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__    :	Any     					=			model(**self._prepare_for_class(a	,    a							)							)
                    lowercase__    :	str     					=			outputs.attentions
                    self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    lowercase__    :	Optional[int]     					=			True
                    lowercase__    :	List[Any]     					=			model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__    :	Union[str, Any]     					=			model(**self._prepare_for_class(a	,    a							)							)
                    lowercase__    :	List[str]     					=			outputs.attentions
                    self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]							)	,    [self.model_tester.num_attention_heads, seq_len, seq_len]	,    )
                    lowercase__    :	Dict     					=			len(a							)

                    # Check attention is always last and order is fine
                    lowercase__    :	Any     					=			True
                    lowercase__    :	int     					=			True
                    lowercase__    :	int     					=			model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__    :	Any     					=			model(**self._prepare_for_class(a	,    a							)							)

                    lowercase__    :	Optional[Any]     					=			1
                    self.assertEqual(out_len + added_hidden_states	,    len(a							)							)

                    lowercase__    :	Tuple     					=			outputs.attentions

                    self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)
                    self.assertListEqual(
                        list(self_attentions[0].shape[-3:]							)	,    [self.model_tester.num_attention_heads, seq_len, seq_len]	,    )


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             def check_hidden_states_output(a	,    a	,    a							):
                    lowercase__    :	str     					=			model_class(a							)
                    model.to(a							)
                    model.eval()

                    with torch.no_grad():
                           lowercase__    :	int     					=			model(**self._prepare_for_class(a	,    a							)							)

                    lowercase__    :	int     					=			outputs.hidden_states

                    lowercase__    :	Any     					=			getattr(
                        self.model_tester	,    'expected_num_hidden_layers'	,    self.model_tester.num_hidden_layers + 1							)
                    self.assertEqual(len(a							)	,    a							)

                    # YOLOS has a different seq_length
                    lowercase__    :	Optional[int]     					=			self.model_tester.expected_seq_len

                    self.assertListEqual(
                        list(hidden_states[0].shape[-2:]							)	,    [seq_length, self.model_tester.hidden_size]	,    )

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

             for model_class in self.all_model_classes:
                    lowercase__    :	Any     					=			True
                    check_hidden_states_output(a	,    a	,    a							)

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

                    check_hidden_states_output(a	,    a	,    a							)


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	List[Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_object_detection(*a							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	int     					=			YolosModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
       return image






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


      @cached_property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return AutoImageProcessor.from_pretrained('hustvl/yolos-small'							) if is_vision_available() else None


      @slow
      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small'							).to(a							)

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

             # forward pass
             with torch.no_grad():
                    lowercase__    :	int     					=			model(inputs.pixel_values							)

             # verify outputs
             lowercase__    :	Tuple     					=			torch.Size((1, 1_0_0, 9_2)							)
             self.assertEqual(outputs.logits.shape	,    a							)

             lowercase__    :	Any     					=			torch.tensor(
                 [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]	,    device=a	,    )
             lowercase__    :	List[str]     					=			torch.tensor(
                 [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]	,    device=a							)
             self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,    a	,    atol=1e-4							)							)
             self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,    a	,    atol=1e-4							)							)

             # verify postprocessing
             lowercase__    :	Optional[Any]     					=			image_processor.post_process_object_detection(
                 a	,    threshold=0.3	,    target_sizes=[image.size[::-1]]							)[0]
             lowercase__    :	str     					=			torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]							).to(a							)
             lowercase__    :	Any     					=			[7_5, 7_5, 1_7, 6_3, 1_7]
             lowercase__    :	Optional[int]     					=			torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]							).to(a							)

             self.assertEqual(len(results['scores']							)	,    5							)
             self.assertTrue(torch.allclose(results['scores']	,    a	,    atol=1e-4							)							)
             self.assertSequenceEqual(results['labels'].tolist()	,    a							)
             self.assertTrue(torch.allclose(results['boxes'][0, :]	,    a							)							)

