

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  ( lowerCamelCase__		):



   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Union[str, Any]								=				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							:  List[str],	__A							:  Any,	__A							:  List[str]=1_3,	__A							:  List[str]=6_4,	__A							:  int=3,	__A							:  Dict=4,	__A							:  List[Any]=[2, 2, 2, 2],	__A							:  Optional[int]=[8, 4, 2, 1],	__A							:  Union[str, Any]=[1_6, 3_2, 6_4, 1_2_8],	__A							:  Dict=[1, 4, 8, 1_6],	__A							:  Any=[1, 2, 4, 8],	__A							:  Dict=True,	__A							:  Optional[Any]=True,	__A							:  Tuple="gelu",	__A							:  int=0.1,	__A							:  List[Any]=0.1,	__A							:  Tuple=0.0_2,	__A							:  int=1_6,	__A							:  List[Any]=3,	__A							:  Tuple=None,	):
     UpperCAmelCase :							List[Any]								=				parent
     UpperCAmelCase :							Optional[Any]								=				batch_size
     UpperCAmelCase :							int								=				image_size
     UpperCAmelCase :							int								=				num_channels
     UpperCAmelCase :							Optional[Any]								=				num_encoder_blocks
     UpperCAmelCase :							Union[str, Any]								=				sr_ratios
     UpperCAmelCase :							Optional[int]								=				depths
     UpperCAmelCase :							Dict								=				hidden_sizes
     UpperCAmelCase :							Union[str, Any]								=				downsampling_rates
     UpperCAmelCase :							Any								=				num_attention_heads
     UpperCAmelCase :							Optional[Any]								=				is_training
     UpperCAmelCase :							List[str]								=				use_labels
     UpperCAmelCase :							int								=				hidden_act
     UpperCAmelCase :							Optional[int]								=				hidden_dropout_prob
     UpperCAmelCase :							Optional[int]								=				attention_probs_dropout_prob
     UpperCAmelCase :							Union[str, Any]								=				initializer_range
     UpperCAmelCase :							Any								=				decoder_hidden_size
     UpperCAmelCase :							Optional[int]								=				num_labels
     UpperCAmelCase :							Dict								=				scope



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Dict								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     UpperCAmelCase :							Optional[Any]								=				None
     if self.use_labels:
       UpperCAmelCase :							Tuple								=				ids_tensor([self.batch_size, self.image_size, self.image_size],	self.num_labels		)

     UpperCAmelCase :							Any								=				self.get_config()
     return config, pixel_values, labels



   def  __magic_name__					(							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  __magic_name__					(							self							:  str,	__A							:  Any,	__A							:  List[str],	__A							:  Optional[Any]		):
     UpperCAmelCase :							Tuple								=				GLPNModel(config=__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase :							int								=				model(__A		)
     UpperCAmelCase :							List[str]								=				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  __magic_name__					(							self							:  str,	__A							:  Union[str, Any],	__A							:  Optional[Any],	__A							:  Tuple		):
     UpperCAmelCase :							int								=				self.num_labels
     UpperCAmelCase :							Tuple								=				GLPNForDepthEstimation(__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase :							List[Any]								=				model(__A		)
     self.parent.assertEqual(result.predicted_depth.shape,	(self.batch_size, self.image_size, self.image_size)		)
     UpperCAmelCase :							Optional[Any]								=				model(__A,	labels=__A		)
     self.parent.assertEqual(result.predicted_depth.shape,	(self.batch_size, self.image_size, self.image_size)		)





   def  __magic_name__					(							self							:  List[Any]		):
     UpperCAmelCase :							Any								=				self.prepare_config_and_inputs()
     UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							Union[str, Any]								=				config_and_inputs
     UpperCAmelCase :							Union[str, Any]								=				{'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class       __UpperCAmelCase  ( lowerCamelCase__    ,						lowerCamelCase__    ,						unittest.TestCase		):
   UpperCamelCase       =  (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   UpperCamelCase       =  (
       {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
   )

   UpperCamelCase       =  False
   UpperCamelCase       =  False
   UpperCamelCase       =  False



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Tuple								=				GLPNModelTester(self		)
     UpperCAmelCase :							Any								=				GLPNConfigTester(self,	config_class=__A		)



   def  __magic_name__					(							self							:  str		):
     self.config_tester.run_common_tests()



   def  __magic_name__					(							self							:  int		):
     UpperCAmelCase :							Tuple								=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A		)



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							int								=				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  __magic_name__					(							self							:  List[str]		):
     pass



   @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''		)
   def  __magic_name__					(							self							:  List[Any]		):
     pass



   def  __magic_name__					(							self							:  List[str]		):
     UpperCAmelCase				,		UpperCAmelCase :							Tuple								=				self.model_tester.prepare_config_and_inputs_for_common()

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

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



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase				,		UpperCAmelCase :							List[str]								=				self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase :							int								=				True

     for model_class in self.all_model_classes:
       UpperCAmelCase :							Tuple								=				True
       UpperCAmelCase :							int								=				False
       UpperCAmelCase :							int								=				True
       UpperCAmelCase :							Dict								=				model_class(__A		)
       model.to(__A		)
       model.eval()
       with torch.no_grad():
         UpperCAmelCase :							Tuple								=				model(**self._prepare_for_class(__A,	__A		)		)
       UpperCAmelCase :							Optional[Any]								=				outputs.attentions

       UpperCAmelCase :							Optional[Any]								=				sum(self.model_tester.depths		)
       self.assertEqual(len(__A		),	__A		)

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

       self.assertEqual(len(__A		),	__A		)

       # verify the first attentions (first block, first layer)
       UpperCAmelCase :							Optional[Any]								=				(self.model_tester.image_size // 4) ** 2
       UpperCAmelCase :							int								=				(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)
       UpperCAmelCase :							int								=				(self.model_tester.image_size // 3_2) ** 2
       UpperCAmelCase :							List[str]								=				(self.model_tester.image_size // (3_2 * 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],	)
       UpperCAmelCase :							int								=				len(__A		)

       # Check attention is always last and order is fine
       UpperCAmelCase :							List[str]								=				True
       UpperCAmelCase :							Tuple								=				True
       UpperCAmelCase :							Optional[int]								=				model_class(__A		)
       model.to(__A		)
       model.eval()
       with torch.no_grad():
         UpperCAmelCase :							Union[str, Any]								=				model(**self._prepare_for_class(__A,	__A		)		)

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

       UpperCAmelCase :							Union[str, Any]								=				outputs.attentions

       self.assertEqual(len(__A		),	__A		)
       # verify the first attentions (first block, first layer)
       UpperCAmelCase :							Optional[int]								=				(self.model_tester.image_size // 4) ** 2
       UpperCAmelCase :							Union[str, Any]								=				(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  __magic_name__					(							self							:  Union[str, Any]		):
     def check_hidden_states_output(__A							:  List[Any],	__A							:  Union[str, Any],	__A							:  Dict		):
       UpperCAmelCase :							List[str]								=				model_class(__A		)
       model.to(__A		)
       model.eval()

       with torch.no_grad():
         UpperCAmelCase :							Optional[int]								=				model(**self._prepare_for_class(__A,	__A		)		)

       UpperCAmelCase :							List[Any]								=				outputs.hidden_states

       UpperCAmelCase :							Union[str, Any]								=				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,
           ],	)

     UpperCAmelCase				,		UpperCAmelCase :							Tuple								=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

       check_hidden_states_output(__A,	__A,	__A		)



   def  __magic_name__					(							self							:  Optional[Any]		):
     if not self.model_tester.is_training:
       return

     UpperCAmelCase				,		UpperCAmelCase :							Dict								=				self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase :							Union[str, Any]								=				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":
         UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							Optional[Any]								=				inputs_dict['''pixel_values'''].shape
         UpperCAmelCase :							int								=				torch.zeros(
             [self.model_tester.batch_size, height, width],	device=__A		).long()
       UpperCAmelCase :							Dict								=				model_class(__A		)
       model.to(__A		)
       model.train()
       UpperCAmelCase :							Dict								=				self._prepare_for_class(__A,	__A,	return_labels=__A		)
       UpperCAmelCase :							Any								=				model(**__A		).loss
       loss.backward()





   @slow
   def  __magic_name__					(							self							:  int		):
     for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase :							List[Any]								=				GLPNModel.from_pretrained(__A		)
       self.assertIsNotNone(__A		)






def     a__  (	)						->		Any:
  UpperCAmelCase :							Dict								=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
  return image






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



   @slow
   def  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							int								=				GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
     UpperCAmelCase :							Optional[Any]								=				GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__A		)

     UpperCAmelCase :							Dict								=				prepare_img()
     UpperCAmelCase :							List[Any]								=				image_processor(images=__A,	return_tensors='''pt'''		).to(__A		)

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

     # verify the predicted depth
     UpperCAmelCase :							Any								=				torch.Size([1, 4_8_0, 6_4_0]		)
     self.assertEqual(outputs.predicted_depth.shape,	__A		)

     UpperCAmelCase :							int								=				torch.tensor(
         [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		).to(__A		)

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








from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase       :     str           =							logging.get_logger(__name__)





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  ["""input_values""", """attention_mask"""]



   def __init__(							self							:  Union[str, Any],	__A							:  Dict=1,	__A							:  Any=1_6_0_0_0,	__A							:  List[str]=1_2_8,	__A							:  str=1_0_2_4,	__A							:  int=0.0,	__A							:  str=True,	__A							:  Any=-4.2_6_7_7_3_9_3,	__A							:  Optional[int]=4.5_6_8_9_9_7_4,	__A							:  Optional[int]=False,	**__A							:  str,	):
     super().__init__(feature_size=__A,	sampling_rate=__A,	padding_value=__A,	**__A		)
     UpperCAmelCase :							int								=				num_mel_bins
     UpperCAmelCase :							Tuple								=				max_length
     UpperCAmelCase :							str								=				do_normalize
     UpperCAmelCase :							List[Any]								=				mean
     UpperCAmelCase :							Union[str, Any]								=				std
     UpperCAmelCase :							Any								=				return_attention_mask



   def  __magic_name__					(							self							:  Dict,	__A							:  np.ndarray,	__A							:  int,	):
     UpperCAmelCase :							Union[str, Any]								=				torch.from_numpy(__A		).unsqueeze(0		)
     UpperCAmelCase :							List[str]								=				ta_kaldi.fbank(
         __A,	htk_compat=__A,	sample_frequency=self.sampling_rate,	use_energy=__A,	window_type='''hanning''',	num_mel_bins=self.num_mel_bins,	dither=0.0,	frame_shift=1_0,	)

     UpperCAmelCase :							int								=				fbank.shape[0]
     UpperCAmelCase :							int								=				max_length - n_frames

     # pad or truncate, depending on difference
     if difference > 0:
       UpperCAmelCase :							List[Any]								=				torch.nn.ZeroPadad((0, 0, 0, difference)		)
       UpperCAmelCase :							int								=				pad_module(__A		)
     elif difference < 0:
       UpperCAmelCase :							Dict								=				fbank[0:max_length, :]

     UpperCAmelCase :							int								=				fbank.numpy()

     return fbank



   def  __magic_name__					(							self							:  Optional[Any],	__A							:  np.ndarray		):
     return (input_values - (self.mean)) / (self.std * 2)





   def __call__(							self							:  Union[str, Any],	__A							:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],	__A							:  Optional[int] = None,	__A							:  Optional[Union[str, TensorType]] = None,	**__A							:  Optional[Any],	):

     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
             F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
             F''' {self.sampling_rate} and not {sampling_rate}.'''		)
     else:
       logger.warning(
           '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
           '''Failing to do so can result in silent errors that might be hard to debug.'''		)

     UpperCAmelCase :							Any								=				isinstance(__A,	np.ndarray		) and len(raw_speech.shape		) > 1
     if is_batched_numpy and len(raw_speech.shape		) > 2:
       raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''		)
     UpperCAmelCase :							Dict								=				is_batched_numpy or (
         isinstance(__A,	(list, tuple)		) and (isinstance(raw_speech[0],	(np.ndarray, tuple, list)		))
     )

     if is_batched:
       UpperCAmelCase :							List[Any]								=				[np.asarray(__A,	dtype=np.floataa		) for speech in raw_speech]
     elif not is_batched and not isinstance(__A,	np.ndarray		):
       UpperCAmelCase :							Union[str, Any]								=				np.asarray(__A,	dtype=np.floataa		)
     elif isinstance(__A,	np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
       UpperCAmelCase :							int								=				raw_speech.astype(np.floataa		)

     # always return batch
     if not is_batched:
       UpperCAmelCase :							str								=				[raw_speech]

     # extract fbank features and pad/truncate to max_length
     UpperCAmelCase :							Union[str, Any]								=				[self._extract_fbank_features(__A,	max_length=self.max_length		) for waveform in raw_speech]

     # convert into BatchFeature
     UpperCAmelCase :							Optional[Any]								=				BatchFeature({'''input_values''': features}		)

     # make sure list is in array format
     UpperCAmelCase :							Optional[int]								=				padded_inputs.get('''input_values'''		)
     if isinstance(input_values[0],	__A		):
       UpperCAmelCase :							Dict								=				[np.asarray(__A,	dtype=np.floataa		) for feature in input_values]

     # normalization
     if self.do_normalize:
       UpperCAmelCase :							str								=				[self.normalize(__A		) for feature in input_values]

     if return_tensors is not None:
       UpperCAmelCase :							Tuple								=				padded_inputs.convert_to_tensors(__A		)

     return padded_inputs





