
'''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, 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 transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
 from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def 						lowercase_    (      self	:List[str]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      self.config_class(**self.inputs_dict      )
       self.parent.assertTrue(hasattr(_A						,					'hidden_sizes'      )      )
       self.parent.assertTrue(hasattr(_A						,					'num_attention_heads'      )      )
       self.parent.assertTrue(hasattr(_A						,					'num_encoder_blocks'      )      )


class 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]						,					_A	:int						,					_A	:Union[str, Any]=13						,					_A	:Any=64						,					_A	:int=3						,					_A	:int=4						,					_A	:List[str]=[2, 2, 2, 2]						,					_A	:Optional[Any]=[8, 4, 2, 1]						,					_A	:Optional[Any]=[16, 32, 64, 128]						,					_A	:Dict=[1, 4, 8, 16]						,					_A	:Optional[int]=[1, 2, 4, 8]						,					_A	:Tuple=True						,					_A	:Any=True						,					_A	:Optional[Any]="gelu"						,					_A	:str=0.1						,					_A	:int=0.1						,					_A	:Optional[Any]=0.02						,					_A	:Union[str, Any]=16						,					_A	:Dict=3						,					_A	:List[Any]=None						,					)					->      List[str]:







       '''simple docstring'''



       __A													=      parent
       __A													=      batch_size
       __A													=      image_size
       __A													=      num_channels
       __A													=      num_encoder_blocks
       __A													=      sr_ratios
       __A													=      depths
       __A													=      hidden_sizes
       __A													=      downsampling_rates
       __A													=      num_attention_heads
       __A													=      is_training
       __A													=      use_labels
       __A													=      hidden_act
       __A													=      hidden_dropout_prob
       __A													=      attention_probs_dropout_prob
       __A													=      initializer_range
       __A													=      decoder_hidden_size
       __A													=      num_labels
       __A													=      scope






 def 						lowercase_    (      self	:Dict      )					->      List[str]:







       '''simple docstring'''



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

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

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






 def 						lowercase_    (      self	:Any      )					->      Union[str, Any]:







       '''simple docstring'''



       return GLPNConfig(
           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						,					num_attention_heads=self.num_attention_heads						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					initializer_range=self.initializer_range						,					decoder_hidden_size=self.decoder_hidden_size						,					)






 def 						lowercase_    (      self	:int						,					_A	:Optional[Any]						,					_A	:Dict						,					_A	:Tuple      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      GLPNModel(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )
       __A													=      __A													=      self.image_size // (self.downsampling_rates[-1] * 2)
       self.parent.assertEqual(
           result.last_hidden_state.shape						,					(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)      )






 def 						lowercase_    (      self	:Tuple						,					_A	:List[Any]						,					_A	:List[Any]						,					_A	:List[Any]      )					->      Dict:







       '''simple docstring'''



       __A													=      self.num_labels
       __A													=      GLPNForDepthEstimation(_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )
       self.parent.assertEqual(result.predicted_depth.shape						,					(self.batch_size, self.image_size, self.image_size)      )
       __A													=      model(_A						,					labels=_A      )
       self.parent.assertEqual(result.predicted_depth.shape						,					(self.batch_size, self.image_size, self.image_size)      )






 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



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


@require_torch
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Any    					=       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 UpperCAmelCase__     :     Optional[int]    					=       (
     {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
 )

 UpperCAmelCase__     :     Optional[int]    					=       False
 UpperCAmelCase__     :     str    					=       False
 UpperCAmelCase__     :     List[Any]    					=       False






 def 						lowercase_    (      self	:str      )					->      List[Any]:







       '''simple docstring'''



       __A													=      GLPNModelTester(self      )
       __A													=      GLPNConfigTester(self						,					config_class=_A      )






 def 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       self.config_tester.run_common_tests()






 def 						lowercase_    (      self	:List[str]      )					->      Any:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A      )






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







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*_A      )






 @unittest.skip('GLPN does not use inputs_embeds'      )
 def 						lowercase_    (      self	:List[Any]      )					->      List[str]:







       '''simple docstring'''



       pass






 @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'      )
 def 						lowercase_    (      self	:Dict      )					->      int:







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:str      )					->      Tuple:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

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

             __A													=      ['pixel_values']
             self.assertListEqual(arg_names[:1]						,					_A      )






 def 						lowercase_    (      self	:List[str]      )					->      Dict:







       '''simple docstring'''



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

       for model_class in self.all_model_classes:
             __A													=      True
             __A													=      False
             __A													=      True
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )
             __A													=      outputs.attentions

             __A													=      sum(self.model_tester.depths      )
             self.assertEqual(len(_A      )						,					_A      )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             __A													=      True
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )
             __A													=      outputs.attentions

             self.assertEqual(len(_A      )						,					_A      )

             # verify the first attentions (first block, first layer)
             __A													=      (self.model_tester.image_size // 4) ** 2
             __A													=      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(attentions[0].shape[-3:]      )						,					[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]						,					)

             # verify the last attentions (last block, last layer)
             __A													=      (self.model_tester.image_size // 32) ** 2
             __A													=      (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
             self.assertListEqual(
                 list(attentions[-1].shape[-3:]      )						,					[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]						,					)
             __A													=      len(_A      )

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

             self.assertEqual(out_len + 1						,					len(_A      )      )

             __A													=      outputs.attentions

             self.assertEqual(len(_A      )						,					_A      )
             # verify the first attentions (first block, first layer)
             __A													=      (self.model_tester.image_size // 4) ** 2
             __A													=      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]      )						,					[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]						,					)






 def 						lowercase_    (      self	:Optional[Any]      )					->      Optional[int]:







       '''simple docstring'''



       def check_hidden_states_output(_A	:List[Any]						,					_A	:List[str]						,					_A	:Dict      ):
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()

             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )

             __A													=      outputs.hidden_states

             __A													=      self.model_tester.num_encoder_blocks
             self.assertEqual(len(_A      )						,					_A      )

             # 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,
                 ]						,					)

       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A													=      True
             check_hidden_states_output(_A						,					_A						,					_A      )

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

             check_hidden_states_output(_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:int      )					->      Dict:







       '''simple docstring'''



       if not self.model_tester.is_training:
             return

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

       for model_class in self.all_model_classes:
             if model_class in get_values(_A      ):
                   continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             if model_class.__name__ == "GLPNForDepthEstimation":
                   __A						,   __A						,   __A						,   __A													=      inputs_dict['pixel_values'].shape
                   __A													=      torch.zeros(
                       [self.model_tester.batch_size, height, width]						,					device=_A      ).long()
             __A													=      model_class(_A      )
             model.to(_A      )
             model.train()
             __A													=      self._prepare_for_class(_A						,					_A						,					return_labels=_A      )
             __A													=      model(**_A      ).loss
             loss.backward()






 @slow
 def 						lowercase_    (      self	:Optional[int]      )					->      str:







       '''simple docstring'''



       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A													=      GLPNModel.from_pretrained(_A      )
             self.assertIsNotNone(_A      )





def 							snake_case				(		)->      Optional[Any]:







      """simple docstring"""
      __A													=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      return image


@require_torch
@require_vision
@slow
class 			UpperCamelCase__	(     unittest.TestCase):






 @slow
 def 						lowercase_    (      self	:Tuple      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
       __A													=      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(_A      )

       __A													=      prepare_img()
       __A													=      image_processor(images=_A						,					return_tensors='pt'      ).to(_A      )

       # forward pass
       with torch.no_grad():
             __A													=      model(**_A      )

       # verify the predicted depth
       __A													=      torch.Size([1, 480, 640]      )
       self.assertEqual(outputs.predicted_depth.shape						,					_A      )

       __A													=      torch.tensor(
           [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]      ).to(_A      )

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







'''simple docstring'''





import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				List[str]=100				,	snake_case       :				Any=" "  )   ->				Any:







     """simple docstring"""
     a					:							Union[str, Any]							= text.split(snake_case  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0				,	len(snake_case  )				,	snake_case  )]






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[int]  )   ->				Union[str, Any]:







     """simple docstring"""
     a							,			a					:							int							= [], []
     for title, text in zip(documents['title']				,	documents['text']  ):
          if text is not None:
               for passage in split_text(snake_case  ):
                    titles.append(title if title is not None else ''  )
                    texts.append(snake_case  )
     return {"title": titles, "text": texts}






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				str				,	snake_case       :				List[Any]				,	snake_case       :				Dict				,	snake_case       :				Union[str, Any]				,	snake_case       :				int  )   ->				Dict:







     """simple docstring"""
     a					:							Tuple							= load_dataset(
         'csv'				,	data_files=[csv_path]				,	split='train'				,	delimiter='\t'				,	column_names=['title', 'text']  )
     a					:							Union[str, Any]							= kb_dataset.map(
         snake_case				,	batched=snake_case				,	num_proc=1  )  # if you want you can load already splitted csv.
     a					:							Optional[Any]							= [kb_dataset.shard(snake_case				,	snake_case				,	contiguous=snake_case  ) for i in range(snake_case  )]
     a					:							List[Any]							= kb_list[process_num]

     a					:							Dict							= 'data_' + str(snake_case  )
     a					:							int							= os.path.join(snake_case				,	snake_case  )

     a					:							Any							= DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
     a					:							List[str]							= ctx_encoder.to(device=snake_case  )

     def embed(
         snake_case       :				dict				,	snake_case       :				DPRContextEncoder				,	snake_case       :				DPRContextEncoderTokenizerFast				,	snake_case       :				Any  ) -> dict:
          a					:							Tuple							= ctx_tokenizer(
              documents['title']				,	documents['text']				,	truncation=snake_case				,	padding='longest'				,	return_tensors='pt'  )['input_ids']
          a					:							Any							= ctx_encoder(input_ids.to(device=snake_case  )				,	return_dict=snake_case  ).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}

     a					:							Union[str, Any]							= Features(
         {'text': Value('string'  ), 'title': Value('string'  ), 'embeddings': Sequence(Value('float32'  )  )}  )  # optional, save as float32 instead of float64 to save space

     a					:							List[str]							= data_shrad.map(
         partial(snake_case				,	ctx_encoder=snake_case				,	ctx_tokenizer=snake_case				,	device=snake_case  )				,	batched=snake_case				,	batch_size=16				,	features=snake_case				,	)
     dataset.save_to_disk(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Any  )   ->				List[Any]:







     """simple docstring"""
     a					:							int							= []

     for shard_address in glob(str(snake_case  ) + '/*/'  ):
          data_shard_list.append(load_from_disk(snake_case  )  )

     a					:							Tuple							= concatenate_datasets(snake_case  )
     faiss.omp_set_num_threads(96  )

     a					:							List[str]							= faiss.IndexHNSWFlat(768				,	128				,	faiss.METRIC_INNER_PRODUCT  )
     concat.add_faiss_index('embeddings'				,	custom_index=snake_case  )
     concat.get_index('embeddings'  ).save(
         snake_case  )  # since we load the index in to memory,we can directly update the index in the disk

