"""simple docstring"""
import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =	{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

SCREAMING_SNAKE_CASE__       =	{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

SCREAMING_SNAKE_CASE__       =	{
    "openai-gpt": 512,
}





def  lowerCAmelCase__     (      _UpperCamelCase							:					Any       )       ->					Optional[int]:





						"""simple docstring"""






						snake_case             =      text.strip()
						if not text:
												return []
						snake_case             =      text.split()
						return tokens



class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase=True		,     lowerCAmelCase=None		,     lowerCAmelCase=True		,     lowerCAmelCase=None		,     lowerCAmelCase=True		,     ):






										"""simple docstring"""

										if never_split is None:
																snake_case             =      []
										snake_case             =      do_lower_case
										snake_case             =      set(lowerCAmelCase     )
										snake_case             =      tokenize_chinese_chars
										snake_case             =      strip_accents
										snake_case             =      do_split_on_punc

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase=None     ):






										"""simple docstring"""

										snake_case             =      self.never_split.union(set(lowerCAmelCase     )     ) if never_split else self.never_split
										snake_case             =      self._clean_text(lowerCAmelCase     )

										# This was added on November 1st, 2018 for the multilingual and Chinese
										# models. This is also applied to the English models now, but it doesn't
										# matter since the English models were not trained on any Chinese data
										# and generally don't have any Chinese data in them (there are Chinese
										# characters in the vocabulary because Wikipedia does have some Chinese
										# words in the English Wikipedia.).
										if self.tokenize_chinese_chars:
																snake_case             =      self._tokenize_chinese_chars(lowerCAmelCase     )
										# prevents treating the same character with different unicode codepoints as different characters
										snake_case             =      unicodedata.normalize('NFC'		,     lowerCAmelCase     )
										snake_case             =      whitespace_tokenize(lowerCAmelCase     )
										snake_case             =      []
										for token in orig_tokens:
																if token not in never_split:
																						if self.do_lower_case:
																												snake_case             =      token.lower()
																												if self.strip_accents is not False:
																																		snake_case             =      self._run_strip_accents(lowerCAmelCase     )
																						elif self.strip_accents:
																												snake_case             =      self._run_strip_accents(lowerCAmelCase     )
																split_tokens.extend(self._run_split_on_punc(lowerCAmelCase		,     lowerCAmelCase     )     )

										snake_case             =      whitespace_tokenize(' '.join(lowerCAmelCase     )     )
										return output_tokens

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      unicodedata.normalize('NFD'		,     lowerCAmelCase     )
										snake_case             =      []
										for char in text:
																snake_case             =      unicodedata.category(lowerCAmelCase     )
																if cat == "Mn":
																						continue
																output.append(lowerCAmelCase     )
										return "".join(lowerCAmelCase     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase=None     ):






										"""simple docstring"""

										if not self.do_split_on_punc or (never_split is not None and text in never_split):
																return [text]
										snake_case             =      list(lowerCAmelCase     )
										snake_case             =      0
										snake_case             =      True
										snake_case             =      []
										while i < len(lowerCAmelCase     ):
																snake_case             =      chars[i]
																if _is_punctuation(lowerCAmelCase     ):
																						output.append([char]     )
																						snake_case             =      True
																else:
																						if start_new_word:
																												output.append([]     )
																						snake_case             =      False
																						output[-1].append(lowerCAmelCase     )
																i += 1

										return ["".join(lowerCAmelCase     ) for x in output]

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      []
										for char in text:
																snake_case             =      ord(lowerCAmelCase     )
																if self._is_chinese_char(lowerCAmelCase     ):
																						output.append(' '     )
																						output.append(lowerCAmelCase     )
																						output.append(' '     )
																else:
																						output.append(lowerCAmelCase     )
										return "".join(lowerCAmelCase     )

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										if (
										    (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
										    or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
										    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
										    or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
										    or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
										    or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
										    or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
										    or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
										):  #
																return True

										return False

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      []
										for char in text:
																snake_case             =      ord(lowerCAmelCase     )
																if cp == 0 or cp == 0xF_F_F_D or _is_control(lowerCAmelCase     ):
																						continue
																if _is_whitespace(lowerCAmelCase     ):
																						output.append(' '     )
																else:
																						output.append(lowerCAmelCase     )
										return "".join(lowerCAmelCase     )





def  lowerCAmelCase__     (      _UpperCamelCase							:					Tuple       )       ->					Optional[int]:





						"""simple docstring"""






						snake_case             =      set()
						snake_case             =      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)       )
												snake_case             =      char
						return pairs





def  lowerCAmelCase__     (      _UpperCamelCase							:					List[Any]       )       ->					int:





						"""simple docstring"""






						snake_case             =      text.replace('—'			,      '-'       )
						snake_case             =      text.replace('–'			,      '-'       )
						snake_case             =      text.replace('―'			,      '-'       )
						snake_case             =      text.replace('…'			,      '...'       )
						snake_case             =      text.replace('´'			,      '\''       )
						snake_case             =      re.sub(r'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'			,      r' \1 '			,      _UpperCamelCase       )
						snake_case             =      re.sub(r'\s*\n\s*'			,      ' \n '			,      _UpperCamelCase       )
						snake_case             =      re.sub(r'[^\S\n]+'			,      ' '			,      _UpperCamelCase       )
						return text.strip()



class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     str      	=    VOCAB_FILES_NAMES
				_lowerCAmelCase  :     Any      	=    PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase  :     List[str]      	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase  :     Optional[Any]      	=    ["""input_ids""", """attention_mask"""]

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase="<unk>"		,     **lowerCAmelCase     ):






										"""simple docstring"""

										super().__init__(unk_token=lowerCAmelCase		,     **lowerCAmelCase     )

										try:
																import ftfy
																from spacy.lang.en import English

																snake_case             =      English()
																snake_case             =      _nlp.tokenizer
																snake_case             =      ftfy.fix_text
										except ImportError:
																logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'     )
																snake_case             =      BasicTokenizer(do_lower_case=lowerCAmelCase     )
																snake_case             =      None

										with open(lowerCAmelCase		,     encoding='utf-8'     ) as vocab_handle:
																snake_case             =      json.load(lowerCAmelCase     )
										snake_case             =      {v: k for k, v in self.encoder.items()}
										with open(lowerCAmelCase		,     encoding='utf-8'     ) as merges_handle:
																snake_case             =      merges_handle.read().split('\n'     )[1:-1]
										snake_case             =      [tuple(merge.split()     ) for merge in merges]
										snake_case             =      dict(zip(lowerCAmelCase		,     range(len(lowerCAmelCase     )     )     )     )
										snake_case             =      {}

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										return True

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										return len(self.encoder     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										return dict(self.encoder		,     **self.added_tokens_encoder     )

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      tuple(token[:-1]     ) + (token[-1] + '</w>',)
										if token in self.cache:
																return self.cache[token]
										snake_case             =      get_pairs(lowerCAmelCase     )

										if not pairs:
																return token + "</w>"

										while True:
																snake_case             =      min(lowerCAmelCase		,     key=lambda lowerCAmelCase     : self.bpe_ranks.get(lowerCAmelCase		,     float('inf'     )     )     )
																if bigram not in self.bpe_ranks:
																						break
																snake_case       ,snake_case             =      bigram
																snake_case             =      []
																snake_case             =      0
																while i < len(lowerCAmelCase     ):
																						try:
																												snake_case             =      word.index(lowerCAmelCase		,     lowerCAmelCase     )
																						except ValueError:
																												new_word.extend(word[i:]     )
																												break
																						else:
																												new_word.extend(word[i:j]     )
																												snake_case             =      j

																						if word[i] == first and i < len(lowerCAmelCase     ) - 1 and word[i + 1] == second:
																												new_word.append(first + second     )
																												i += 2
																						else:
																												new_word.append(word[i]     )
																												i += 1
																snake_case             =      tuple(lowerCAmelCase     )
																snake_case             =      new_word
																if len(lowerCAmelCase     ) == 1:
																						break
																else:
																						snake_case             =      get_pairs(lowerCAmelCase     )
										snake_case             =      ' '.join(lowerCAmelCase     )
										if word == "\n  </w>":
																snake_case             =      '\n</w>'
										snake_case             =      word
										return word

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      []
										if self.fix_text is None:
																# Using BERT's BasicTokenizer
																snake_case             =      self.nlp.tokenize(lowerCAmelCase     )
																for token in text:
																						split_tokens.extend(list(self.bpe(lowerCAmelCase     ).split(' '     )     )     )
										else:
																# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
																snake_case             =      self.nlp(text_standardize(self.fix_text(lowerCAmelCase     )     )     )
																for token in text:
																						split_tokens.extend(list(self.bpe(token.text.lower()     ).split(' '     )     )     )
										return split_tokens

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										return self.encoder.get(lowerCAmelCase		,     self.encoder.get(self.unk_token     )     )

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										return self.decoder.get(lowerCAmelCase		,     self.unk_token     )

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      ''.join(lowerCAmelCase     ).replace('</w>'		,     ' '     ).strip()
										return out_string

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase = None     ):






										"""simple docstring"""

										if not os.path.isdir(lowerCAmelCase     ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
																return
										snake_case             =      os.path.join(
										    lowerCAmelCase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
										snake_case             =      os.path.join(
										    lowerCAmelCase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

										with open(lowerCAmelCase		,     'w'		,     encoding='utf-8'     ) as f:
																f.write(json.dumps(self.encoder		,     indent=2		,     sort_keys=lowerCAmelCase		,     ensure_ascii=lowerCAmelCase     ) + '\n'     )

										snake_case             =      0
										with open(lowerCAmelCase		,     'w'		,     encoding='utf-8'     ) as writer:
																writer.write('#version: 0.2\n'     )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda lowerCAmelCase     : kv[1]     ):
																						if index != token_index:
																												logger.warning(
																												    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																												    ' Please check that the tokenizer is not corrupted!'     )
																												snake_case             =      token_index
																						writer.write(' '.join(lowerCAmelCase     ) + '\n'     )
																						index += 1

										return vocab_file, merge_file



"""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 	lowerCAmelCase_     :



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=2		,     lowerCAmelCase=32		,     lowerCAmelCase=16		,     lowerCAmelCase=3		,     lowerCAmelCase=True		,     lowerCAmelCase=True		,     lowerCAmelCase=32		,     lowerCAmelCase=4		,     lowerCAmelCase=[0, 1, 2, 3]		,     lowerCAmelCase=4		,     lowerCAmelCase=37		,     lowerCAmelCase="gelu"		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.1		,     lowerCAmelCase=0.02		,     lowerCAmelCase=3		,     lowerCAmelCase=False		,     lowerCAmelCase=None		,     ):






										"""simple docstring"""

										snake_case             =      parent
										snake_case             =      batch_size
										snake_case             =      image_size
										snake_case             =      patch_size
										snake_case             =      num_channels
										snake_case             =      is_training
										snake_case             =      use_labels
										snake_case             =      hidden_size
										snake_case             =      num_hidden_layers
										snake_case             =      backbone_out_indices
										snake_case             =      num_attention_heads
										snake_case             =      intermediate_size
										snake_case             =      hidden_act
										snake_case             =      hidden_dropout_prob
										snake_case             =      attention_probs_dropout_prob
										snake_case             =      initializer_range
										snake_case             =      num_labels
										snake_case             =      scope
										snake_case             =      is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										snake_case             =      (image_size // patch_size) ** 2
										snake_case             =      num_patches + 1

				def 		snake_case			(					self     ):






										"""simple docstring"""

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

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

										snake_case             =      self.get_config()

										return config, pixel_values, labels

				def 		snake_case			(					self     ):






										"""simple docstring"""

										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=lowerCAmelCase		,     initializer_range=self.initializer_range		,     is_hybrid=self.is_hybrid		,     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      DPTModel(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )
										self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self.num_labels
										snake_case             =      DPTForDepthEstimation(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )
										self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size)     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      self.num_labels
										snake_case             =      DPTForSemanticSegmentation(lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase		,     labels=lowerCAmelCase     )
										self.parent.assertEqual(
										    result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size, self.image_size)     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

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





@require_torch
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     int      	=    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				_lowerCAmelCase  :     List[str]      	=    (
				    {
				        """depth-estimation""": DPTForDepthEstimation,
				        """feature-extraction""": DPTModel,
				        """image-segmentation""": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				_lowerCAmelCase  :     str      	=    False
				_lowerCAmelCase  :     Any      	=    False
				_lowerCAmelCase  :     Dict      	=    False

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTModelTester(self     )
										snake_case             =      ConfigTester(self		,     config_class=lowerCAmelCase		,     has_text_modality=lowerCAmelCase		,     hidden_size=37     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				@unittest.skip(reason='DPT does not use inputs_embeds'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case             =      ['pixel_values']
																self.assertListEqual(arg_names[:1]		,     lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																						continue

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

																if model_class in get_values(lowerCAmelCase     ):
																						continue

																snake_case             =      model_class(lowerCAmelCase     )
																model.to(lowerCAmelCase     )
																model.train()
																snake_case             =      self._prepare_for_class(lowerCAmelCase		,     lowerCAmelCase		,     return_labels=lowerCAmelCase     )
																snake_case             =      model(**lowerCAmelCase     ).loss
																loss.backward()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()
																snake_case             =      False
																snake_case             =      True

																if model_class in get_values(lowerCAmelCase     ) or not model_class.supports_gradient_checkpointing:
																						continue
																snake_case             =      model_class(lowerCAmelCase     )
																model.to(lowerCAmelCase     )
																model.gradient_checkpointing_enable()
																model.train()
																snake_case             =      self._prepare_for_class(lowerCAmelCase		,     lowerCAmelCase		,     return_labels=lowerCAmelCase     )
																snake_case             =      model(**lowerCAmelCase     ).loss
																loss.backward()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										snake_case             =      _config_zero_init(lowerCAmelCase     )
										for model_class in self.all_model_classes:
																snake_case             =      model_class(config=lowerCAmelCase     )
																# Skip the check for the backbone
																snake_case             =      []
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																												snake_case             =      [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     ):






										"""simple docstring"""

										pass

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case             =      DPTModel.from_pretrained(lowerCAmelCase     )
																self.assertIsNotNone(lowerCAmelCase     )





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





						"""simple docstring"""






						snake_case             =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
						return image



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



				"""simple docstring"""

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large'     )
										snake_case             =      DPTForDepthEstimation.from_pretrained('Intel/dpt-large'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )
																snake_case             =      outputs.predicted_depth

										# verify the predicted depth
										snake_case             =      torch.Size((1, 3_84, 3_84)     )
										self.assertEqual(predicted_depth.shape		,     lowerCAmelCase     )

										snake_case             =      torch.tensor(
										    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]     ).to(lowerCAmelCase     )

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
										snake_case             =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )

										# verify the logits
										snake_case             =      torch.Size((1, 1_50, 4_80, 4_80)     )
										self.assertEqual(outputs.logits.shape		,     lowerCAmelCase     )

										snake_case             =      torch.tensor(
										    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]     ).to(lowerCAmelCase     )

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
										snake_case             =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCAmelCase     )

										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

										# forward pass
										with torch.no_grad():
																snake_case             =      model(**lowerCAmelCase     )

										snake_case             =      outputs.logits.detach().cpu()

										snake_case             =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase		,     target_sizes=[(5_00, 3_00)]     )
										snake_case             =      torch.Size((5_00, 3_00)     )
										self.assertEqual(segmentation[0].shape		,     lowerCAmelCase     )

										snake_case             =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase     )
										snake_case             =      torch.Size((4_80, 4_80)     )
										self.assertEqual(segmentation[0].shape		,     lowerCAmelCase     )


