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		(snake_case_						):



    """simple docstring"""

    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.config_class(**self.inputs_dict							)
       self.parent.assertTrue(hasattr(_UpperCAmelCase					,    '''hidden_sizes'''							)							)
       self.parent.assertTrue(hasattr(_UpperCAmelCase					,    '''num_attention_heads'''							)							)
       self.parent.assertTrue(hasattr(_UpperCAmelCase					,    '''num_encoder_blocks'''							)							)



class 					UpperCAmelCase		:



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase					,    _UpperCAmelCase=13					,    _UpperCAmelCase=64					,    _UpperCAmelCase=3					,    _UpperCAmelCase=4					,    _UpperCAmelCase=[2, 2, 2, 2]					,    _UpperCAmelCase=[8, 4, 2, 1]					,    _UpperCAmelCase=[16, 32, 64, 128]					,    _UpperCAmelCase=[1, 4, 8, 16]					,    _UpperCAmelCase=[1, 2, 4, 8]					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase="gelu"					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.02					,    _UpperCAmelCase=16					,    _UpperCAmelCase=3					,    _UpperCAmelCase=None					,    ):
       lowercase__:     Tuple      =   parent
       lowercase__:     Optional[int]      =   batch_size
       lowercase__:     Optional[Any]      =   image_size
       lowercase__:     List[str]      =   num_channels
       lowercase__:     Union[str, Any]      =   num_encoder_blocks
       lowercase__:     Optional[int]      =   sr_ratios
       lowercase__:     str      =   depths
       lowercase__:     int      =   hidden_sizes
       lowercase__:     int      =   downsampling_rates
       lowercase__:     List[Any]      =   num_attention_heads
       lowercase__:     Optional[int]      =   is_training
       lowercase__:     Optional[Any]      =   use_labels
       lowercase__:     List[Any]      =   hidden_act
       lowercase__:     Optional[Any]      =   hidden_dropout_prob
       lowercase__:     Optional[Any]      =   attention_probs_dropout_prob
       lowercase__:     Tuple      =   initializer_range
       lowercase__:     Any      =   decoder_hidden_size
       lowercase__:     Union[str, Any]      =   num_labels
       lowercase__:     Optional[Any]      =   scope






    def _snake_case    (     self							):
       lowercase__:     List[str]      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       lowercase__:     Dict      =   None
       if self.use_labels:
          lowercase__:     Optional[int]      =   ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels							)

       lowercase__:     List[Any]      =   self.get_config()
       return config, pixel_values, labels






    def _snake_case    (     self							):
       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 _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       lowercase__:     str      =   GLPNModel(config=_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:     List[Any]      =   model(_UpperCAmelCase							)
       lowercase__:     Tuple      =   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 _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       lowercase__:     Any      =   self.num_labels
       lowercase__:     Any      =   GLPNForDepthEstimation(_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:     int      =   model(_UpperCAmelCase							)
       self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)							)
       lowercase__:     int      =   model(_UpperCAmelCase					,    labels=_UpperCAmelCase							)
       self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)							)






    def _snake_case    (     self							):
       lowercase__:     Optional[int]      =   self.prepare_config_and_inputs()
       lowercase__,				lowercase__,				lowercase__:     Tuple      =   config_and_inputs
       lowercase__:     List[Any]      =   {'''pixel_values''': pixel_values}
       return config, inputs_dict



@require_torch
class 					UpperCAmelCase		(snake_case_					,snake_case_					,unittest.TestCase						):



    """simple docstring"""

    _UpperCAmelCase			:Any							=				(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
    _UpperCAmelCase			:Dict							=				(
        {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
    )

    _UpperCAmelCase			:Union[str, Any]							=				False
    _UpperCAmelCase			:str							=				False
    _UpperCAmelCase			:Tuple							=				False






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   GLPNModelTester(self							)
       lowercase__:     Tuple      =   GLPNConfigTester(self					,    config_class=_UpperCAmelCase							)






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






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase							)






    @unittest.skip('''GLPN does not use inputs_embeds'''							)
    def _snake_case    (     self							):
       pass






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






    def _snake_case    (     self							):
       lowercase__,				lowercase__:     Any      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__:     Any      =   model_class(_UpperCAmelCase							)
          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[str]      =   ['''pixel_values''']
          self.assertListEqual(arg_names[:1]					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__,				lowercase__:     List[Any]      =   self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__:     Optional[Any]      =   True

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

          lowercase__:     str      =   sum(self.model_tester.depths							)
          self.assertEqual(len(_UpperCAmelCase							)					,    _UpperCAmelCase							)

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

          self.assertEqual(len(_UpperCAmelCase							)					,    _UpperCAmelCase							)

          # verify the first attentions (first block, first layer)
          lowercase__:     Dict      =   (self.model_tester.image_size // 4) ** 2
          lowercase__:     str      =   (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)
          lowercase__:     List[Any]      =   (self.model_tester.image_size // 32) ** 2
          lowercase__:     Optional[int]      =   (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]					,    )
          lowercase__:     Tuple      =   len(_UpperCAmelCase							)

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

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

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

          self.assertEqual(len(_UpperCAmelCase							)					,    _UpperCAmelCase							)
          # verify the first attentions (first block, first layer)
          lowercase__:     Optional[int]      =   (self.model_tester.image_size // 4) ** 2
          lowercase__:     int      =   (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 _snake_case    (     self							):
       def check_hidden_states_output(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							):
          lowercase__:     int      =   model_class(_UpperCAmelCase							)
          model.to(_UpperCAmelCase							)
          model.eval()

          with torch.no_grad():
             lowercase__:     str      =   model(**self._prepare_for_class(_UpperCAmelCase					,    _UpperCAmelCase							)							)

          lowercase__:     Any      =   outputs.hidden_states

          lowercase__:     str      =   self.model_tester.num_encoder_blocks
          self.assertEqual(len(_UpperCAmelCase							)					,    _UpperCAmelCase							)

          # 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__:     Union[str, Any]      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__:     Dict      =   True
          check_hidden_states_output(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)

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

          check_hidden_states_output(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)






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

       lowercase__,				lowercase__:     int      =   self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__:     List[Any]      =   True

       for model_class in self.all_model_classes:
          if model_class in get_values(_UpperCAmelCase							):
             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":
             lowercase__,				lowercase__,				lowercase__,				lowercase__:     int      =   inputs_dict['''pixel_values'''].shape
             lowercase__:     Dict      =   torch.zeros(
                 [self.model_tester.batch_size, height, width]					,    device=_UpperCAmelCase							).long()
          lowercase__:     Optional[int]      =   model_class(_UpperCAmelCase							)
          model.to(_UpperCAmelCase							)
          model.train()
          lowercase__:     List[Any]      =   self._prepare_for_class(_UpperCAmelCase					,    _UpperCAmelCase					,    return_labels=_UpperCAmelCase							)
          lowercase__:     int      =   model(**_UpperCAmelCase							).loss
          loss.backward()






    @slow
    def _snake_case    (     self							):
       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:     Dict      =   GLPNModel.from_pretrained(_UpperCAmelCase							)
          self.assertIsNotNone(_UpperCAmelCase							)



def SCREAMING_SNAKE_CASE__    (     )						->						Union[str, Any]:
   lowercase__:     Optional[Any]      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
   return image




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



    """simple docstring"""

    @slow
    def _snake_case    (     self							):
       lowercase__:     Any      =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
       lowercase__:     Any      =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(_UpperCAmelCase							)

       lowercase__:     str      =   prepare_img()
       lowercase__:     Any      =   image_processor(images=_UpperCAmelCase					,    return_tensors='''pt'''							).to(_UpperCAmelCase							)

       # forward pass
       with torch.no_grad():
          lowercase__:     List[str]      =   model(**_UpperCAmelCase							)

       # verify the predicted depth
       lowercase__:     Union[str, Any]      =   torch.Size([1, 480, 640]							)
       self.assertEqual(outputs.predicted_depth.shape					,    _UpperCAmelCase							)

       lowercase__:     Tuple      =   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(_UpperCAmelCase							)

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

"""simple docstring"""






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__A         =				get_tests_dir("fixtures/test_sentencepiece.model")


@require_torch
@require_torchaudio
@require_sentencepiece
class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    def _snake_case    (     self							):
       lowercase__:     Dict      =   tempfile.mkdtemp()

       lowercase__:     str      =   ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       lowercase__:     Optional[Any]      =   dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase							)							)							)							)
       lowercase__:     List[Any]      =   Path(self.tmpdirname							)
       save_json(_UpperCAmelCase					,    save_dir / VOCAB_FILES_NAMES['''vocab_file''']							)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
          copyfile(_UpperCAmelCase					,    save_dir / VOCAB_FILES_NAMES['''spm_file''']							)

       lowercase__:     Tuple      =   SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)

       lowercase__:     Optional[int]      =   {
           '''feature_size''': 24,
           '''num_mel_bins''': 24,
           '''padding_value''': 0.0,
           '''sampling_rate''': 16000,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(_UpperCAmelCase					,    save_dir / FEATURE_EXTRACTOR_NAME							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_tokenizer()
       lowercase__:     Optional[int]      =   self.get_feature_extractor()

       lowercase__:     int      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       processor.save_pretrained(self.tmpdirname							)
       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()							)
       processor.save_pretrained(self.tmpdirname							)

       lowercase__:     Any      =   self.get_tokenizer(bos_token='''(BOS)'''					,    eos_token='''(EOS)'''							)
       lowercase__:     Tuple      =   self.get_feature_extractor(do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

       lowercase__:     List[str]      =   SpeechaTextProcessor.from_pretrained(
           self.tmpdirname					,    bos_token='''(BOS)'''					,    eos_token='''(EOS)'''					,    do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     List[Any]      =   self.get_tokenizer()

       lowercase__:     Any      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     int      =   floats_list((3, 1000)							)

       lowercase__:     Optional[Any]      =   feature_extractor(_UpperCAmelCase					,    return_tensors='''np'''							)
       lowercase__:     Dict      =   processor(_UpperCAmelCase					,    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 _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     Dict      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     Dict      =   '''This is a test string'''

       lowercase__:     int      =   processor(text=_UpperCAmelCase							)

       lowercase__:     Union[str, Any]      =   tokenizer(_UpperCAmelCase							)

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






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.get_feature_extractor()
       lowercase__:     Union[str, Any]      =   self.get_tokenizer()

       lowercase__:     Tuple      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     List[str]      =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase__:     List[Any]      =   processor.batch_decode(_UpperCAmelCase							)
       lowercase__:     Dict      =   tokenizer.batch_decode(_UpperCAmelCase							)

       self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   self.get_feature_extractor()
       lowercase__:     List[str]      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

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