

import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

A_						:     Tuple			  =				logging.get_logger(__name__)

A_						:     int			  =				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

A_						:     Dict			  =				{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

A_						:     List[Any]			  =				{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

A_						:     Tuple			  =				{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def  __a    (				) ->       List[Any]:






    '''simple docstring'''
    __UpperCAmelCase			   =     (
        list(range(ord('''!''' )    ,				ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' )    ,				ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' )    ,				ord('''ÿ''' ) + 1 ) )
    )
    __UpperCAmelCase			   =     bs[:]
    __UpperCAmelCase			   =     0
    for b in range(2**8 ):
        if b not in bs:
            bs.append(SCREAMING_SNAKE_CASE )
            cs.append(2**8 + n )
            n += 1
    __UpperCAmelCase			   =     [chr(SCREAMING_SNAKE_CASE ) for n in cs]
    return dict(zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) )


def  __a    (				SCREAMING_SNAKE_CASE ) ->       List[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     set()
    __UpperCAmelCase			   =     word[0]
    for char in word[1:]:
        pairs.add((prev_char, char) )
        __UpperCAmelCase			   =     char
    return pairs
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    VOCAB_FILES_NAMES
    a__       							=    PRETRAINED_VOCAB_FILES_MAP
    a__       							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       							=    ["input_ids", "attention_mask", "token_type_ids"]


    def __init__(self       , lowercase__       , lowercase__       , lowercase__="replace"       , lowercase__="[CLS]"       , lowercase__="[SEP]"       , lowercase__="[SEP]"       , lowercase__="[CLS]"       , lowercase__="[UNK]"       , lowercase__="[PAD]"       , lowercase__="[MASK]"       , lowercase__=False       , lowercase__=False       , **lowercase__       , )       -> Dict:
        __UpperCAmelCase			   =     AddedToken(lowercase__       , lstrip=lowercase__       , rstrip=lowercase__					) if isinstance(lowercase__       , lowercase__					) else bos_token
        __UpperCAmelCase			   =     AddedToken(lowercase__       , lstrip=lowercase__       , rstrip=lowercase__					) if isinstance(lowercase__       , lowercase__					) else eos_token
        __UpperCAmelCase			   =     AddedToken(lowercase__       , lstrip=lowercase__       , rstrip=lowercase__					) if isinstance(lowercase__       , lowercase__					) else sep_token
        __UpperCAmelCase			   =     AddedToken(lowercase__       , lstrip=lowercase__       , rstrip=lowercase__					) if isinstance(lowercase__       , lowercase__					) else cls_token
        __UpperCAmelCase			   =     AddedToken(lowercase__       , lstrip=lowercase__       , rstrip=lowercase__					) if isinstance(lowercase__       , lowercase__					) else unk_token
        __UpperCAmelCase			   =     AddedToken(lowercase__       , lstrip=lowercase__       , rstrip=lowercase__					) if isinstance(lowercase__       , lowercase__					) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __UpperCAmelCase			   =     AddedToken(lowercase__       , lstrip=lowercase__       , rstrip=lowercase__					) if isinstance(lowercase__       , lowercase__					) else mask_token

        super().__init__(
            errors=lowercase__       , bos_token=lowercase__       , eos_token=lowercase__       , unk_token=lowercase__       , sep_token=lowercase__       , cls_token=lowercase__       , pad_token=lowercase__       , mask_token=lowercase__       , add_prefix_space=lowercase__       , add_bos_token=lowercase__       , **lowercase__       , )
        __UpperCAmelCase			   =     add_bos_token

        with open(lowercase__       , encoding='''utf-8'''					) as vocab_handle:
            __UpperCAmelCase			   =     json.load(lowercase__					)
        __UpperCAmelCase			   =     {v: k for k, v in self.encoder.items()}
        __UpperCAmelCase			   =     errors  # how to handle errors in decoding
        __UpperCAmelCase			   =     bytes_to_unicode()
        __UpperCAmelCase			   =     {v: k for k, v in self.byte_encoder.items()}
        with open(lowercase__       , encoding='''utf-8'''					) as merges_handle:
            __UpperCAmelCase			   =     merges_handle.read().split('''\n'''					)[1:-1]
        __UpperCAmelCase			   =     [tuple(merge.split()					) for merge in bpe_merges]
        __UpperCAmelCase			   =     dict(zip(lowercase__       , range(len(lowercase__					)					)					)					)
        __UpperCAmelCase			   =     {}
        __UpperCAmelCase			   =     add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __UpperCAmelCase			   =     re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''					)


    @property
    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
    def        lowerCAmelCase_  (self					)       -> List[str]:
        return len(self.encoder					)


    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        return dict(self.encoder       , **self.added_tokens_encoder					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Optional[Any]:
        if token in self.cache:
            return self.cache[token]
        __UpperCAmelCase			   =     tuple(lowercase__					)
        __UpperCAmelCase			   =     get_pairs(lowercase__					)

        if not pairs:
            return token

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

                if word[i] == first and i < len(lowercase__					) - 1 and word[i + 1] == second:
                    new_word.append(first + second					)
                    i += 2
                else:
                    new_word.append(word[i]					)
                    i += 1
            __UpperCAmelCase			   =     tuple(lowercase__					)
            __UpperCAmelCase			   =     new_word
            if len(lowercase__					) == 1:
                break
            else:
                __UpperCAmelCase			   =     get_pairs(lowercase__					)
        __UpperCAmelCase			   =     ''' '''.join(lowercase__					)
        __UpperCAmelCase			   =     word
        return word


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> List[int]:
        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __UpperCAmelCase			   =     [self.cls_token_id]
        __UpperCAmelCase			   =     [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None       , lowercase__ = False					)       -> List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowercase__       , token_ids_a=lowercase__       , already_has_special_tokens=lowercase__					)

        if token_ids_a is None:
            return [1] + ([0] * len(lowercase__					)) + [1]
        return [1] + ([0] * len(lowercase__					)) + [1] + ([0] * len(lowercase__					)) + [1]


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> List[int]:
        __UpperCAmelCase			   =     [self.sep_token_id]
        __UpperCAmelCase			   =     [self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep					) * [0]
        return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Optional[int]:
        __UpperCAmelCase			   =     []
        for token in re.findall(self.pat       , lowercase__					):
            __UpperCAmelCase			   =     ''''''.join(
                self.byte_encoder[b] for b in token.encode('''utf-8'''					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__					).split(''' '''					)					)
        return bpe_tokens


    def        lowerCAmelCase_  (self       , lowercase__					)       -> str:
        return self.encoder.get(lowercase__       , self.encoder.get(self.unk_token					)					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Dict:
        return self.decoder.get(lowercase__					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[Any]:
        __UpperCAmelCase			   =     ''''''.join(lowercase__					)
        __UpperCAmelCase			   =     bytearray([self.byte_decoder[c] for c in text]					).decode('''utf-8'''       , errors=self.errors					)
        return text


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> Tuple[str]:
        if not os.path.isdir(lowercase__					):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
            return
        __UpperCAmelCase			   =     os.path.join(
            lowercase__       , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
        __UpperCAmelCase			   =     os.path.join(
            lowercase__       , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']					)

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

        __UpperCAmelCase			   =     0
        with open(lowercase__       , '''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 lowercase__					: 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!'''					)
                    __UpperCAmelCase			   =     token_index
                writer.write(''' '''.join(lowercase__					) + '''\n'''					)
                index += 1

        return vocab_file, merge_file


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__=False       , **lowercase__					)       -> Any:
        __UpperCAmelCase			   =     kwargs.pop('''add_prefix_space'''       , self.add_prefix_space					)
        if (is_split_into_words or add_prefix_space) and (len(lowercase__					) > 0 and not text[0].isspace()):
            __UpperCAmelCase			   =     ''' ''' + text
        return (text, kwargs)






    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[int]:
        __UpperCAmelCase			   =     []
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(lowercase__       , add_special_tokens=lowercase__					) + [self.eos_token_id]					)
        if len(lowercase__					) > self.model_max_length:
            __UpperCAmelCase			   =     input_ids[-self.model_max_length :]
        return input_ids







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    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__=2       , lowercase__=32       , lowercase__=16       , lowercase__=3       , lowercase__=True       , lowercase__=True       , lowercase__=32       , lowercase__=4       , lowercase__=[0, 1, 2, 3]       , lowercase__=4       , lowercase__=37       , lowercase__="gelu"       , lowercase__=0.1       , lowercase__=0.1       , lowercase__=0.02       , lowercase__=3       , lowercase__=False       , lowercase__=None       , )       -> Any:
        __UpperCAmelCase			   =     parent
        __UpperCAmelCase			   =     batch_size
        __UpperCAmelCase			   =     image_size
        __UpperCAmelCase			   =     patch_size
        __UpperCAmelCase			   =     num_channels
        __UpperCAmelCase			   =     is_training
        __UpperCAmelCase			   =     use_labels
        __UpperCAmelCase			   =     hidden_size
        __UpperCAmelCase			   =     num_hidden_layers
        __UpperCAmelCase			   =     backbone_out_indices
        __UpperCAmelCase			   =     num_attention_heads
        __UpperCAmelCase			   =     intermediate_size
        __UpperCAmelCase			   =     hidden_act
        __UpperCAmelCase			   =     hidden_dropout_prob
        __UpperCAmelCase			   =     attention_probs_dropout_prob
        __UpperCAmelCase			   =     initializer_range
        __UpperCAmelCase			   =     num_labels
        __UpperCAmelCase			   =     scope
        __UpperCAmelCase			   =     is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        __UpperCAmelCase			   =     (image_size // patch_size) ** 2
        __UpperCAmelCase			   =     num_patches + 1


    def        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        __UpperCAmelCase			   =     self.get_config()

        return config, pixel_values, labels


    def        lowerCAmelCase_  (self					)       -> str:
        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=lowercase__       , initializer_range=self.initializer_range       , is_hybrid=self.is_hybrid       , )


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> Any:
        __UpperCAmelCase			   =     DPTModel(config=lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__					)
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> List[Any]:
        __UpperCAmelCase			   =     self.num_labels
        __UpperCAmelCase			   =     DPTForDepthEstimation(lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__					)
        self.parent.assertEqual(result.predicted_depth.shape       , (self.batch_size, self.image_size, self.image_size)					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> str:
        __UpperCAmelCase			   =     self.num_labels
        __UpperCAmelCase			   =     DPTForSemanticSegmentation(lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__       , labels=lowercase__					)
        self.parent.assertEqual(
            result.logits.shape       , (self.batch_size, self.num_labels, self.image_size, self.image_size)					)






    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase			   =     self.prepare_config_and_inputs()
        __UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase			   =     config_and_inputs
        __UpperCAmelCase			   =     {'''pixel_values''': pixel_values}
        return config, inputs_dict






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


    '''simple docstring'''






    a__       							=    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    a__       							=    (
        {
            "depth-estimation": DPTForDepthEstimation,
            "feature-extraction": DPTModel,
            "image-segmentation": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    a__       							=    False
    a__       							=    False
    a__       							=    False


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     DPTModelTester(self					)
        __UpperCAmelCase			   =     ConfigTester(self       , config_class=lowercase__       , has_text_modality=lowercase__       , hidden_size=37					)


    def        lowerCAmelCase_  (self					)       -> Dict:
        self.config_tester.run_common_tests()


    @unittest.skip(reason='''DPT does not use inputs_embeds'''					)
    def        lowerCAmelCase_  (self					)       -> int:
        pass


    def        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

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


    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCAmelCase			   =     ['''pixel_values''']
            self.assertListEqual(arg_names[:1]       , lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Any:
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

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

            if model_class in get_values(lowercase__					):
                continue

            __UpperCAmelCase			   =     model_class(lowercase__					)
            model.to(lowercase__					)
            model.train()
            __UpperCAmelCase			   =     self._prepare_for_class(lowercase__       , lowercase__       , return_labels=lowercase__					)
            __UpperCAmelCase			   =     model(**lowercase__					).loss
            loss.backward()


    def        lowerCAmelCase_  (self					)       -> Tuple:
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

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

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


    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase			   =     _config_zero_init(lowercase__					)
        for model_class in self.all_model_classes:
            __UpperCAmelCase			   =     model_class(config=lowercase__					)
            # Skip the check for the backbone
            __UpperCAmelCase			   =     []
            for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                    __UpperCAmelCase			   =     [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        lowerCAmelCase_  (self					)       -> int:
        pass






    @slow
    def        lowerCAmelCase_  (self					)       -> List[str]:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCAmelCase			   =     DPTModel.from_pretrained(lowercase__					)
            self.assertIsNotNone(lowercase__					)






def  __a    (				) ->       Any:






    '''simple docstring'''
    __UpperCAmelCase			   =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    return image
@require_torch
@require_vision
@slow
class    A_				( unittest.TestCase  ):


    '''simple docstring'''






    def        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large'''					)
        __UpperCAmelCase			   =     DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)
            __UpperCAmelCase			   =     outputs.predicted_depth

        # verify the predicted depth
        __UpperCAmelCase			   =     torch.Size((1, 384, 384)					)
        self.assertEqual(predicted_depth.shape       , lowercase__					)

        __UpperCAmelCase			   =     torch.tensor(
            [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(lowercase__					)

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


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
        __UpperCAmelCase			   =     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)

        # verify the logits
        __UpperCAmelCase			   =     torch.Size((1, 150, 480, 480)					)
        self.assertEqual(outputs.logits.shape       , lowercase__					)

        __UpperCAmelCase			   =     torch.tensor(
            [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(lowercase__					)

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






    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
        __UpperCAmelCase			   =     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)

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

        __UpperCAmelCase			   =     image_processor.post_process_semantic_segmentation(outputs=lowercase__       , target_sizes=[(500, 300)]					)
        __UpperCAmelCase			   =     torch.Size((500, 300)					)
        self.assertEqual(segmentation[0].shape       , lowercase__					)

        __UpperCAmelCase			   =     image_processor.post_process_semantic_segmentation(outputs=lowercase__					)
        __UpperCAmelCase			   =     torch.Size((480, 480)					)
        self.assertEqual(segmentation[0].shape       , lowercase__					)




