
'''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 				_a			(_lowerCamelCase):





       """simple docstring"""




       def     UpperCamelCase			(							self )      ->     Optional[Any]:
           _SCREAMING_SNAKE_CASE       	=			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 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__=13    ,							A__=64    ,							A__=3    ,							A__=4    ,							A__=[2, 2, 2, 2]    ,							A__=[8, 4, 2, 1]    ,							A__=[16, 32, 64, 1_28]    ,							A__=[1, 4, 8, 16]    ,							A__=[1, 2, 4, 8]    ,							A__=True    ,							A__=True    ,							A__="gelu"    ,							A__=0.1    ,							A__=0.1    ,							A__=0.02    ,							A__=16    ,							A__=3    ,							A__=None    ,							)      ->     Dict:
           _SCREAMING_SNAKE_CASE       	=			parent
           _SCREAMING_SNAKE_CASE       	=			batch_size
           _SCREAMING_SNAKE_CASE       	=			image_size
           _SCREAMING_SNAKE_CASE       	=			num_channels
           _SCREAMING_SNAKE_CASE       	=			num_encoder_blocks
           _SCREAMING_SNAKE_CASE       	=			sr_ratios
           _SCREAMING_SNAKE_CASE       	=			depths
           _SCREAMING_SNAKE_CASE       	=			hidden_sizes
           _SCREAMING_SNAKE_CASE       	=			downsampling_rates
           _SCREAMING_SNAKE_CASE       	=			num_attention_heads
           _SCREAMING_SNAKE_CASE       	=			is_training
           _SCREAMING_SNAKE_CASE       	=			use_labels
           _SCREAMING_SNAKE_CASE       	=			hidden_act
           _SCREAMING_SNAKE_CASE       	=			hidden_dropout_prob
           _SCREAMING_SNAKE_CASE       	=			attention_probs_dropout_prob
           _SCREAMING_SNAKE_CASE       	=			initializer_range
           _SCREAMING_SNAKE_CASE       	=			decoder_hidden_size
           _SCREAMING_SNAKE_CASE       	=			num_labels
           _SCREAMING_SNAKE_CASE       	=			scope






       def     UpperCamelCase			(							self )      ->     List[Any]:
           _SCREAMING_SNAKE_CASE       	=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

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






       def     UpperCamelCase			(							self )      ->     List[Any]:
           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     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     Dict:
           _SCREAMING_SNAKE_CASE       	=			GLPNModel(config=A__ )
           model.to(A__ )
           model.eval()
           _SCREAMING_SNAKE_CASE       	=			model(A__ )
           _SCREAMING_SNAKE_CASE       	=			_SCREAMING_SNAKE_CASE       	=			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     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     str:
           _SCREAMING_SNAKE_CASE       	=			self.num_labels
           _SCREAMING_SNAKE_CASE       	=			GLPNForDepthEstimation(A__ )
           model.to(A__ )
           model.eval()
           _SCREAMING_SNAKE_CASE       	=			model(A__ )
           self.parent.assertEqual(result.predicted_depth.shape    ,							(self.batch_size, self.image_size, self.image_size) )
           _SCREAMING_SNAKE_CASE       	=			model(A__    ,							labels=A__ )
           self.parent.assertEqual(result.predicted_depth.shape    ,							(self.batch_size, self.image_size, self.image_size) )






       def     UpperCamelCase			(							self )      ->     Tuple:
           _SCREAMING_SNAKE_CASE       	=			self.prepare_config_and_inputs()
           _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			config_and_inputs
           _SCREAMING_SNAKE_CASE       	=			{"""pixel_values""": pixel_values}
           return config, inputs_dict






@require_torch
class 				_a			(_lowerCamelCase					,   _lowerCamelCase					,   unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE =					(
           {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
       )

       SCREAMING_SNAKE_CASE =					False
       SCREAMING_SNAKE_CASE =					False
       SCREAMING_SNAKE_CASE =					False






       def     UpperCamelCase			(							self )      ->     List[str]:
           _SCREAMING_SNAKE_CASE       	=			GLPNModelTester(self )
           _SCREAMING_SNAKE_CASE       	=			GLPNConfigTester(self    ,							config_class=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
           self.config_tester.run_common_tests()






       def     UpperCamelCase			(							self )      ->     Optional[int]:
           _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
           _SCREAMING_SNAKE_CASE       	=			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     UpperCamelCase			(							self )      ->     Dict:
           pass






       @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods""" )
       def     UpperCamelCase			(							self )      ->     Dict:
           pass






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

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

               _SCREAMING_SNAKE_CASE       	=			["""pixel_values"""]
               self.assertListEqual(arg_names[:1]    ,							A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
           _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs_for_common()
           _SCREAMING_SNAKE_CASE       	=			True

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

               _SCREAMING_SNAKE_CASE       	=			sum(self.model_tester.depths )
               self.assertEqual(len(A__ )    ,							A__ )

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

               self.assertEqual(len(A__ )    ,							A__ )

               # verify the first attentions (first block, first layer)
               _SCREAMING_SNAKE_CASE       	=			(self.model_tester.image_size // 4) ** 2
               _SCREAMING_SNAKE_CASE       	=			(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)
               _SCREAMING_SNAKE_CASE       	=			(self.model_tester.image_size // 32) ** 2
               _SCREAMING_SNAKE_CASE       	=			(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]    ,							)
               _SCREAMING_SNAKE_CASE       	=			len(A__ )

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

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

               _SCREAMING_SNAKE_CASE       	=			outputs.attentions

               self.assertEqual(len(A__ )    ,							A__ )
               # verify the first attentions (first block, first layer)
               _SCREAMING_SNAKE_CASE       	=			(self.model_tester.image_size // 4) ** 2
               _SCREAMING_SNAKE_CASE       	=			(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     UpperCamelCase			(							self )      ->     Dict:
           def check_hidden_states_output(A__    ,							A__    ,							A__ ):
               _SCREAMING_SNAKE_CASE       	=			model_class(A__ )
               model.to(A__ )
               model.eval()

               with torch.no_grad():
                   _SCREAMING_SNAKE_CASE       	=			model(**self._prepare_for_class(A__    ,							A__ ) )

               _SCREAMING_SNAKE_CASE       	=			outputs.hidden_states

               _SCREAMING_SNAKE_CASE       	=			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,
                   ]    ,							)

           _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _SCREAMING_SNAKE_CASE       	=			True
               check_hidden_states_output(A__    ,							A__    ,							A__ )

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

               check_hidden_states_output(A__    ,							A__    ,							A__ )






       def     UpperCamelCase			(							self )      ->     str:
           if not self.model_tester.is_training:
               return

           _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs_for_common()
           _SCREAMING_SNAKE_CASE       	=			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":
                   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			inputs_dict["""pixel_values"""].shape
                   _SCREAMING_SNAKE_CASE       	=			torch.zeros(
                       [self.model_tester.batch_size, height, width]    ,							device=A__ ).long()
               _SCREAMING_SNAKE_CASE       	=			model_class(A__ )
               model.to(A__ )
               model.train()
               _SCREAMING_SNAKE_CASE       	=			self._prepare_for_class(A__    ,							A__    ,							return_labels=A__ )
               _SCREAMING_SNAKE_CASE       	=			model(**A__ ).loss
               loss.backward()






       @slow
       def     UpperCamelCase			(							self )      ->     Optional[Any]:
           for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _SCREAMING_SNAKE_CASE       	=			GLPNModel.from_pretrained(A__ )
               self.assertIsNotNone(A__ )






def 				lowerCAmelCase_		(			)       ->       Dict:




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







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





       """simple docstring"""




       @slow
       def     UpperCamelCase			(							self )      ->     Optional[Any]:
           _SCREAMING_SNAKE_CASE       	=			GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
           _SCREAMING_SNAKE_CASE       	=			GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A__ )

           _SCREAMING_SNAKE_CASE       	=			prepare_img()
           _SCREAMING_SNAKE_CASE       	=			image_processor(images=A__    ,							return_tensors="""pt""" ).to(A__ )

           # forward pass
           with torch.no_grad():
               _SCREAMING_SNAKE_CASE       	=			model(**A__ )

           # verify the predicted depth
           _SCREAMING_SNAKE_CASE       	=			torch.Size([1, 4_80, 6_40] )
           self.assertEqual(outputs.predicted_depth.shape    ,							A__ )

           _SCREAMING_SNAKE_CASE       	=			torch.tensor(
               [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] ).to(A__ )

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








'''simple docstring'''



#
# Sort large text files in a minimum amount of memory
#
import argparse
import os

class 				_a			:





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'block_{0}.dat'






       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			filename
          _SCREAMING_SNAKE_CASE       	=			[]






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.BLOCK_FILENAME_FORMAT.format(A__ )
          with open(A__    ,							"""w""" ) as file:
             file.write(A__ )
          self.block_filenames.append(A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          return self.block_filenames






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			0
          with open(self.filename ) as file:
             while True:
                _SCREAMING_SNAKE_CASE       	=			file.readlines(A__ )

                if lines == []:
                   break

                if sort_key is None:
                   lines.sort()
                else:
                   lines.sort(key=A__ )

                self.write_block("""""".join(A__ )    ,							A__ )
                i += 1






       def     UpperCamelCase			(							self )      ->     List[Any]:
          map(os.remove    ,							self.block_filenames )






class 				_a			:





       """simple docstring"""




       def     UpperCamelCase			(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			-1
          _SCREAMING_SNAKE_CASE       	=			None

          for i in range(len(A__ ) ):
             if min_str is None or choices[i] < min_str:
                _SCREAMING_SNAKE_CASE       	=			i

          return min_index






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			files
          _SCREAMING_SNAKE_CASE       	=			set()
          _SCREAMING_SNAKE_CASE       	=			len(A__ )
          _SCREAMING_SNAKE_CASE       	=			{i: None for i in range(self.num_buffers )}






       def     UpperCamelCase			(							self )      ->     Dict:
          return {
              i: self.buffers[i] for i in range(self.num_buffers ) if i not in self.empty
          }






       def     UpperCamelCase			(							self )      ->     Tuple:
          for i in range(self.num_buffers ):
             if self.buffers[i] is None and i not in self.empty:
                _SCREAMING_SNAKE_CASE       	=			self.files[i].readline()

                if self.buffers[i] == "":
                   self.empty.add(A__ )
                   self.files[i].close()

          if len(self.empty ) == self.num_buffers:
             return False

          return True






       def     UpperCamelCase			(							self    ,							A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.buffers[index]
          _SCREAMING_SNAKE_CASE       	=			None

          return value






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			merge_strategy






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			FilesArray(self.get_file_handles(A__    ,							A__ ) )
          with open(A__    ,							"""w"""    ,							A__ ) as outfile:
             while buffers.refresh():
                _SCREAMING_SNAKE_CASE       	=			self.merge_strategy.select(buffers.get_dict() )
                outfile.write(buffers.unshift(A__ ) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			{}

          for i in range(len(A__ ) ):
             _SCREAMING_SNAKE_CASE       	=			open(filenames[i]    ,							"""r"""    ,							A__ )  # noqa: UP015

          return files






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			block_size






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_number_blocks(A__    ,							self.block_size )
          _SCREAMING_SNAKE_CASE       	=			FileSplitter(A__ )
          splitter.split(self.block_size    ,							A__ )

          _SCREAMING_SNAKE_CASE       	=			FileMerger(NWayMerge() )
          _SCREAMING_SNAKE_CASE       	=			self.block_size / (num_blocks + 1)
          merger.merge(splitter.get_block_filenames()    ,							filename + """.out"""    ,							A__ )

          splitter.cleanup()






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Any:
          return (os.stat(A__ ).st_size / block_size) + 1






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   if string[-1].lower() == "k":
      return int(string[:-1]  ) * 10_24
   elif string[-1].lower() == "m":
      return int(string[:-1]  ) * 10_24 * 10_24
   elif string[-1].lower() == "g":
      return int(string[:-1]  ) * 10_24 * 10_24 * 10_24
   else:
      return int(SCREAMING_SNAKE_CASE_  )
def 				lowerCAmelCase_		(			)       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser()
   parser.add_argument(
       """-m"""      , """--mem"""      , help="""amount of memory to use for sorting"""      , default="""100M"""  )
   parser.add_argument(
       """filename"""      , metavar="""<filename>"""      , nargs=1      , help="""name of file to sort"""  )
   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   _SCREAMING_SNAKE_CASE       	=			ExternalSort(parse_memory(args.mem  )  )
   sorter.sort(args.filename[0]  )


if __name__ == "__main__":
     main()






