



"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase    : Any							=							get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_lowerCAmelCase    : Tuple							=							25_60_47
_lowerCAmelCase    : List[Any]							=							25_61_45






@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       List[str]								=			NllbTokenizer
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			NllbTokenizerFast
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			True
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			True
      __SCREAMING_SNAKE_CASE						:       str								=			{}



      def 						snake_case_ (					self					:     Optional[Any]						):
           super().setUp()

           # We have a SentencePiece fixture for testing
           _UpperCAmelCase      :     Optional[Any]        =   NllbTokenizer(A					,     keep_accents=A						)
           tokenizer.save_pretrained(self.tmpdirname						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     int        =   NllbTokenizer(A					,     keep_accents=A						)

           _UpperCAmelCase      :     Any        =   tokenizer.tokenize("This is a test"						)
           self.assertListEqual(A					,     ["▁This", "▁is", "▁a", "▁t", "est"]						)

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(A						)					,     [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]					,     )

           _UpperCAmelCase      :     str        =   tokenizer.tokenize("I was born in 92000, and this is falsé."						)
           self.assertListEqual(
               A					,     [
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "9",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "é",
                   ".",
               ]					,     )
           _UpperCAmelCase      :     Dict        =   tokenizer.convert_tokens_to_ids(A						)
           self.assertListEqual(
               A					,     [
                   value + tokenizer.fairseq_offset
                   for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
               ]					,     )

           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.convert_ids_to_tokens(A						)
           self.assertListEqual(
               A					,     [
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "<unk>",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "<unk>",
                   ".",
               ]					,     )



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     int        =   (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                     _UpperCAmelCase      :     Tuple        =   self.rust_tokenizer_class.from_pretrained(A					,     **A						)
                     _UpperCAmelCase      :     Any        =   self.tokenizer_class.from_pretrained(A					,     **A						)

                     _UpperCAmelCase      :     List[str]        =   tempfile.mkdtemp()

                     _UpperCAmelCase      :     Any        =   tokenizer_r.save_pretrained(A						)
                     _UpperCAmelCase      :     Optional[int]        =   tokenizer_p.save_pretrained(A						)

                     # Checks it save with the same files + the tokenizer.json file for the fast one
                     self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files						)						)
                     _UpperCAmelCase      :     str        =   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f						)
                     self.assertSequenceEqual(A					,     A						)

                     # Checks everything loads correctly in the same way
                     _UpperCAmelCase      :     Any        =   tokenizer_r.from_pretrained(A						)
                     _UpperCAmelCase      :     int        =   tokenizer_p.from_pretrained(A						)

                     # Check special tokens are set accordingly on Rust and Python
                     for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(A					,     A						)						)

                     shutil.rmtree(A						)

                     # Save tokenizer rust, legacy_format=True
                     _UpperCAmelCase      :     int        =   tempfile.mkdtemp()

                     _UpperCAmelCase      :     Dict        =   tokenizer_r.save_pretrained(A					,     legacy_format=A						)
                     _UpperCAmelCase      :     Optional[int]        =   tokenizer_p.save_pretrained(A						)

                     # Checks it save with the same files
                     self.assertSequenceEqual(A					,     A						)

                     # Checks everything loads correctly in the same way
                     _UpperCAmelCase      :     List[Any]        =   tokenizer_r.from_pretrained(A						)
                     _UpperCAmelCase      :     Union[str, Any]        =   tokenizer_p.from_pretrained(A						)

                     # Check special tokens are set accordingly on Rust and Python
                     for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(A					,     A						)						)

                     shutil.rmtree(A						)

                     # Save tokenizer rust, legacy_format=False
                     _UpperCAmelCase      :     Optional[int]        =   tempfile.mkdtemp()

                     _UpperCAmelCase      :     str        =   tokenizer_r.save_pretrained(A					,     legacy_format=A						)
                     _UpperCAmelCase      :     Dict        =   tokenizer_p.save_pretrained(A						)

                     # Checks it saved the tokenizer.json file
                     self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files						)						)

                     # Checks everything loads correctly in the same way
                     _UpperCAmelCase      :     Dict        =   tokenizer_r.from_pretrained(A						)
                     _UpperCAmelCase      :     Union[str, Any]        =   tokenizer_p.from_pretrained(A						)

                     # Check special tokens are set accordingly on Rust and Python
                     for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(A					,     A						)						)

                     shutil.rmtree(A						)



      @require_torch
      def 						snake_case_ (					self					:     Any						):
           if not self.test_seqaseq:
                return

           _UpperCAmelCase      :     Optional[int]        =   self.get_tokenizers()
           for tokenizer in tokenizers:
                with self.subTest(f'{tokenizer.__class__.__name__}'						):
                     # Longer text that will definitely require truncation.
                     _UpperCAmelCase      :     int        =   [
                         " UN Chief Says There Is No Military Solution in Syria",
                         " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                         " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                         " will only worsen the violence and misery for millions of people.",
                     ]
                     _UpperCAmelCase      :     Optional[int]        =   [
                         "Şeful ONU declară că nu există o soluţie militară în Siria",
                         "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                         " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                         " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
                     ]
                     try:
                          _UpperCAmelCase      :     int        =   tokenizer.prepare_seqaseq_batch(
                              src_texts=A					,     tgt_texts=A					,     max_length=3					,     max_target_length=1_0					,     return_tensors="pt"					,     src_lang="eng_Latn"					,     tgt_lang="ron_Latn"					,     )
                     except NotImplementedError:
                          return
                     self.assertEqual(batch.input_ids.shape[1]					,     3						)
                     self.assertEqual(batch.labels.shape[1]					,     1_0						)
                     # max_target_length will default to max_length if not specified
                     _UpperCAmelCase      :     Optional[int]        =   tokenizer.prepare_seqaseq_batch(
                         A					,     tgt_texts=A					,     max_length=3					,     return_tensors="pt"						)
                     self.assertEqual(batch.input_ids.shape[1]					,     3						)
                     self.assertEqual(batch.labels.shape[1]					,     3						)

                     _UpperCAmelCase      :     Optional[Any]        =   tokenizer.prepare_seqaseq_batch(
                         src_texts=A					,     max_length=3					,     max_target_length=1_0					,     return_tensors="pt"						)
                     self.assertEqual(batch_encoder_only.input_ids.shape[1]					,     3						)
                     self.assertEqual(batch_encoder_only.attention_mask.shape[1]					,     3						)
                     self.assertNotIn("decoder_input_ids"					,     A						)



      @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."						)
      def 						snake_case_ (					self					:     List[str]						):
           pass



      def 						snake_case_ (					self					:     Optional[Any]						):
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                     _UpperCAmelCase      :     Dict        =   [AddedToken("<special>"					,     lstrip=A						)]

                     _UpperCAmelCase      :     List[str]        =   self.rust_tokenizer_class.from_pretrained(
                         A					,     additional_special_tokens=A					,     **A						)
                     _UpperCAmelCase      :     int        =   tokenizer_r.encode("Hey this is a <special> token"						)

                     _UpperCAmelCase      :     List[Any]        =   tokenizer_r.encode("<special>"					,     add_special_tokens=A						)[0]

                     self.assertTrue(special_token_id in r_output						)

                     if self.test_slow_tokenizer:
                          _UpperCAmelCase      :     str        =   self.rust_tokenizer_class.from_pretrained(
                              A					,     additional_special_tokens=A					,     **A					,     )
                          _UpperCAmelCase      :     Tuple        =   self.tokenizer_class.from_pretrained(
                              A					,     additional_special_tokens=A					,     **A						)

                          _UpperCAmelCase      :     Optional[Any]        =   tokenizer_p.encode("Hey this is a <special> token"						)

                          _UpperCAmelCase      :     int        =   tokenizer_cr.encode("Hey this is a <special> token"						)

                          self.assertEqual(A					,     A						)
                          self.assertEqual(A					,     A						)
                          self.assertTrue(special_token_id in p_output						)
                          self.assertTrue(special_token_id in cr_output						)







@require_torch
@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase_  (    unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			'facebook/nllb-200-distilled-600M'
      __SCREAMING_SNAKE_CASE						:       Any								=			[
          ' UN Chief Says There Is No Military Solution in Syria',
          ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
      ]
      __SCREAMING_SNAKE_CASE						:       List[str]								=			[
          'Şeful ONU declară că nu există o soluţie militară în Siria',
          'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
          ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
          ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
      ]
      __SCREAMING_SNAKE_CASE						:       int								=			[
          2_5_6_0_4_7,
          1_6_2_9_7,
          1_3_4_4_0_8,
          8_1_6_5,
          2_4_8_0_6_6,
          1_4_7_3_4,
          9_5_0,
          1_1_3_5,
          1_0_5_7_2_1,
          3_5_7_3,
          8_3,
          2_7_3_5_2,
          1_0_8,
          4_9_4_8_6,
          2,
      ]



      @classmethod
      def 						snake_case_ (					cls					:     Optional[Any]						):
           _UpperCAmelCase      :     NllbTokenizer        =   NllbTokenizer.from_pretrained(
               cls.checkpoint_name					,     src_lang="eng_Latn"					,     tgt_lang="ron_Latn"						)
           _UpperCAmelCase      :     Optional[Any]        =   1
           return cls



      def 						snake_case_ (					self					:     Dict						):
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]					,     2_5_6_0_0_1						)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]					,     2_5_6_0_0_2						)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]					,     2_5_6_0_5_7						)



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
           self.assertListEqual(self.expected_src_tokens					,     A						)



      def 						snake_case_ (					self					:     int						):
           self.assertIn(A					,     self.tokenizer.all_special_ids						)
           # fmt: off
           _UpperCAmelCase      :     int        =   [RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
           # fmt: on

           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer.decode(A					,     skip_special_tokens=A						)
           _UpperCAmelCase      :     List[str]        =   self.tokenizer.decode(generated_ids[1:]					,     skip_special_tokens=A						)
           self.assertEqual(A					,     A						)
           self.assertNotIn(self.tokenizer.eos_token					,     A						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Any        =   ["this is gunna be a long sentence " * 2_0]
           assert isinstance(src_text[0]					,     A						)
           _UpperCAmelCase      :     Optional[int]        =   1_0
           _UpperCAmelCase      :     int        =   self.tokenizer(A					,     max_length=A					,     truncation=A						).input_ids[0]
           self.assertEqual(ids[-1]					,     2						)
           self.assertEqual(ids[0]					,     A						)
           self.assertEqual(len(A						)					,     A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]						)					,     [2_5_6_2_0_3, 3]						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[str]        =   tempfile.mkdtemp()
           _UpperCAmelCase      :     int        =   self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(A						)
           _UpperCAmelCase      :     List[Any]        =   NllbTokenizer.from_pretrained(A						)
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,     A						)



      @require_torch
      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer(
               self.src_text					,     text_target=self.tgt_text					,     padding=A					,     truncation=A					,     max_length=len(self.expected_src_tokens						)					,     return_tensors="pt"					,     )
           _UpperCAmelCase      :     int        =   shift_tokens_right(
               batch["labels"]					,     self.tokenizer.pad_token_id					,     self.tokenizer.lang_code_to_id["ron_Latn"]						)

           self.assertIsInstance(A					,     A						)

           self.assertEqual((2, 1_5)					,     batch.input_ids.shape						)
           self.assertEqual((2, 1_5)					,     batch.attention_mask.shape						)
           _UpperCAmelCase      :     Optional[int]        =   batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens					,     A						)
           self.assertEqual(A					,     batch.decoder_input_ids[0, 0]						)  # EOS
           # Test that special tokens are reset
           self.assertEqual(self.tokenizer.prefix_tokens					,     [EN_CODE]						)
           self.assertEqual(self.tokenizer.suffix_tokens					,     [self.tokenizer.eos_token_id]						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     int        =   self.tokenizer(self.src_text					,     padding=A					,     truncation=A					,     max_length=3					,     return_tensors="pt"						)
           _UpperCAmelCase      :     str        =   self.tokenizer(
               text_target=self.tgt_text					,     padding=A					,     truncation=A					,     max_length=1_0					,     return_tensors="pt"						)
           _UpperCAmelCase      :     str        =   targets["input_ids"]
           _UpperCAmelCase      :     List[Any]        =   shift_tokens_right(
               A					,     self.tokenizer.pad_token_id					,     decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]					,     )

           self.assertEqual(batch.input_ids.shape[1]					,     3						)
           self.assertEqual(batch.decoder_input_ids.shape[1]					,     1_0						)



      @require_torch
      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer._build_translation_inputs(
               "A test"					,     return_tensors="pt"					,     src_lang="eng_Latn"					,     tgt_lang="fra_Latn"						)

           self.assertEqual(
               nested_simplify(A						)					,     {
                   # A, test, EOS, en_XX
                   "input_ids": [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
                   "attention_mask": [[1, 1, 1, 1]],
                   # ar_AR
                   "forced_bos_token_id": 2_5_6_0_5_7,
               }					,     )



      @require_torch
      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Any        =   True
           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer(
               "UN Chief says there is no military solution in Syria"					,     src_lang="eng_Latn"					,     tgt_lang="fra_Latn"						)
           self.assertEqual(
               inputs.input_ids					,     [1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7]						)

           _UpperCAmelCase      :     Optional[int]        =   False
           _UpperCAmelCase      :     Tuple        =   self.tokenizer(
               "UN Chief says there is no military solution in Syria"					,     src_lang="eng_Latn"					,     tgt_lang="fra_Latn"						)
           self.assertEqual(
               inputs.input_ids					,     [2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2]						)





"""simple docstring"""




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
 from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor






class      UpperCAmelCase_  :



      def __init__(					self					:     Tuple					,     A					:     str					,     A					:     Dict=2					,     A					:     List[Any]=3_2					,     A					:     Optional[Any]=1_6					,     A					:     Tuple=3					,     A					:     Optional[Any]=True					,     A					:     List[Any]=True					,     A					:     Optional[int]=3_2					,     A					:     Optional[int]=4					,     A					:     Tuple=[0, 1, 2, 3]					,     A					:     Optional[int]=4					,     A					:     Tuple=3_7					,     A					:     List[Any]="gelu"					,     A					:     List[Any]=0.1					,     A					:     List[str]=0.1					,     A					:     Union[str, Any]=0.02					,     A					:     Optional[int]=3					,     A					:     Optional[Any]=[1, 3_8_4, 2_4, 2_4]					,     A					:     Union[str, Any]=True					,     A					:     Any=None					,     ):
           _UpperCAmelCase      :     str        =   parent
           _UpperCAmelCase      :     int        =   batch_size
           _UpperCAmelCase      :     List[str]        =   image_size
           _UpperCAmelCase      :     Tuple        =   patch_size
           _UpperCAmelCase      :     Any        =   num_channels
           _UpperCAmelCase      :     List[str]        =   is_training
           _UpperCAmelCase      :     Optional[Any]        =   use_labels
           _UpperCAmelCase      :     str        =   hidden_size
           _UpperCAmelCase      :     Dict        =   num_hidden_layers
           _UpperCAmelCase      :     List[Any]        =   backbone_out_indices
           _UpperCAmelCase      :     Optional[Any]        =   num_attention_heads
           _UpperCAmelCase      :     Optional[int]        =   intermediate_size
           _UpperCAmelCase      :     str        =   hidden_act
           _UpperCAmelCase      :     int        =   hidden_dropout_prob
           _UpperCAmelCase      :     Any        =   attention_probs_dropout_prob
           _UpperCAmelCase      :     Optional[Any]        =   initializer_range
           _UpperCAmelCase      :     Optional[Any]        =   num_labels
           _UpperCAmelCase      :     Tuple        =   backbone_featmap_shape
           _UpperCAmelCase      :     Optional[Any]        =   scope
           _UpperCAmelCase      :     Union[str, Any]        =   is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           _UpperCAmelCase      :     Optional[int]        =   (image_size // patch_size) ** 2
           _UpperCAmelCase      :     int        =   num_patches + 1



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Tuple        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

           _UpperCAmelCase      :     Dict        =   None
           if self.use_labels:
                _UpperCAmelCase      :     List[str]        =   ids_tensor([self.batch_size, self.image_size, self.image_size]					,     self.num_labels						)

           _UpperCAmelCase      :     str        =   self.get_config()

           return config, pixel_values, labels



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   {
               "global_padding": "same",
               "layer_type": "bottleneck",
               "depths": [3, 4, 9],
               "out_features": ["stage1", "stage2", "stage3"],
               "embedding_dynamic_padding": True,
               "hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
               "num_groups": 2,
           }

           return DPTConfig(
               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					,     backbone_out_indices=self.backbone_out_indices					,     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					,     is_hybrid=self.is_hybrid					,     backbone_config=A					,     backbone_featmap_shape=self.backbone_featmap_shape					,     )



      def 						snake_case_ (					self					:     Any					,     A					:     Optional[Any]					,     A					:     str					,     A					:     Dict						):
           _UpperCAmelCase      :     List[str]        =   DPTModel(config=A						)
           model.to(A						)
           model.eval()
           _UpperCAmelCase      :     List[str]        =   model(A						)
           self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     str					,     A					:     Any					,     A					:     List[Any]						):
           _UpperCAmelCase      :     str        =   self.num_labels
           _UpperCAmelCase      :     Any        =   DPTForDepthEstimation(A						)
           model.to(A						)
           model.eval()
           _UpperCAmelCase      :     Optional[int]        =   model(A						)
           self.parent.assertEqual(result.predicted_depth.shape					,     (self.batch_size, self.image_size, self.image_size)						)



      def 						snake_case_ (					self					:     List[Any]					,     A					:     Any					,     A					:     Optional[int]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   self.num_labels
           _UpperCAmelCase      :     Union[str, Any]        =   DPTForSemanticSegmentation(A						)
           model.to(A						)
           model.eval()
           _UpperCAmelCase      :     int        =   model(A					,     labels=A						)
           self.parent.assertEqual(
               result.logits.shape					,     (self.batch_size, self.num_labels, self.image_size, self.image_size)						)



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.prepare_config_and_inputs()
           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   config_and_inputs
           _UpperCAmelCase      :     int        =   {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       List[str]								=			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE						:       Any								=			(
          {
              'depth-estimation': DPTForDepthEstimation,
              'feature-extraction': DPTModel,
              'image-segmentation': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE						:       Dict								=			False
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			False



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     List[str]        =   DPTModelTester(self						)
           _UpperCAmelCase      :     Union[str, Any]        =   ConfigTester(self					,     config_class=A					,     has_text_modality=A					,     hidden_size=3_7						)



      def 						snake_case_ (					self					:     int						):
           self.config_tester.run_common_tests()



      @unittest.skip(reason="DPT does not use inputs_embeds"						)
      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     str        =   model_class(A						)
                self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)						)
                _UpperCAmelCase      :     Dict        =   model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(A					,     nn.Linear						)						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase      :     Any        =   ["pixel_values"]
                self.assertListEqual(arg_names[:1]					,     A						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Dict        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Optional[Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*A						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Tuple        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

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

                if model_class in get_values(A						):
                     continue

                _UpperCAmelCase      :     int        =   model_class(A						)
                model.to(A						)
                model.train()
                _UpperCAmelCase      :     Optional[Any]        =   self._prepare_for_class(A					,     A					,     return_labels=A						)
                _UpperCAmelCase      :     Optional[Any]        =   model(**A						).loss
                loss.backward()



      def 						snake_case_ (					self					:     Dict						):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

                _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs_for_common()
                _UpperCAmelCase      :     Tuple        =   False
                _UpperCAmelCase      :     Tuple        =   True

                if model_class in get_values(A						) or not model_class.supports_gradient_checkpointing:
                     continue
                _UpperCAmelCase      :     int        =   model_class(A						)
                model.to(A						)
                model.gradient_checkpointing_enable()
                model.train()
                _UpperCAmelCase      :     str        =   self._prepare_for_class(A					,     A					,     return_labels=A						)
                _UpperCAmelCase      :     List[str]        =   model(**A						).loss
                loss.backward()



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   self.model_tester.prepare_config_and_inputs_for_common()

           _UpperCAmelCase      :     int        =   _config_zero_init(A						)
           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     List[Any]        =   model_class(config=A						)
                # Skip the check for the backbone
                _UpperCAmelCase      :     Dict        =   []
                for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          _UpperCAmelCase      :     List[str]        =   [f'{name}.{key}' for key in module.state_dict().keys()]
                          break

                for name, param in model.named_parameters():
                     if param.requires_grad:
                          if name in backbone_params:
                               continue
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()					,     [0.0, 1.0]					,     msg=f'Parameter {name} of model {model_class} seems not properly initialized'					,     )



      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
      def 						snake_case_ (					self					:     int						):
           pass



      @slow
      def 						snake_case_ (					self					:     Dict						):
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                _UpperCAmelCase      :     Any        =   DPTModel.from_pretrained(A						)
                self.assertIsNotNone(A						)



      def 						snake_case_ (					self					:     Tuple						):
           # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase      :     Optional[Any]        =   "add"
           with self.assertRaises(A						):
                _UpperCAmelCase      :     List[Any]        =   DPTForDepthEstimation(A						)







def 						__snake_case						(				)  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     return image






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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Optional[int]        =   DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"						)
           _UpperCAmelCase      :     Any        =   DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"						).to(A						)

           _UpperCAmelCase      :     str        =   prepare_img()
           _UpperCAmelCase      :     Tuple        =   image_processor(images=A					,     return_tensors="pt"						).to(A						)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase      :     int        =   model(**A						)
                _UpperCAmelCase      :     List[str]        =   outputs.predicted_depth

           # verify the predicted depth
           _UpperCAmelCase      :     int        =   torch.Size((1, 3_8_4, 3_8_4)						)
           self.assertEqual(predicted_depth.shape					,     A						)

           _UpperCAmelCase      :     int        =   torch.tensor(
               [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]						).to(A						)

           self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0					,     A					,     atol=1e-4						)						)
