




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


snake_case_ : Any    		=       {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}







class __snake_case			(			nn.Module		):







   def __init__(      self		:						List[Any]					,						_snake_case		:						Any):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							torchvision.models.resnetaaa(pretrained=_snake_case)
        UpperCAmelCase_          =							list(model.children())[:-2]
        UpperCAmelCase_          =							nn.Sequential(*_snake_case)
        UpperCAmelCase_          =							nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.pool(self.model(_snake_case))
        UpperCAmelCase_          =							torch.flatten(_snake_case					,						start_dim=2)
        UpperCAmelCase_          =							out.transpose(1					,						2).contiguous()
        return out  # BxNx2048







class __snake_case			(			a		):







   def __init__(      self		:						int					,						_snake_case		:						List[Any]					,						_snake_case		:						List[str]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							[json.loads(_snake_case) for l in open(_snake_case)]
        UpperCAmelCase_          =							os.path.dirname(_snake_case)
        UpperCAmelCase_          =							tokenizer
        UpperCAmelCase_          =							labels
        UpperCAmelCase_          =							len(_snake_case)
        UpperCAmelCase_          =							max_seq_length

        UpperCAmelCase_          =							transforms







   def __len__(      self		:						Union[str, Any]):

        """simple docstring"""


        return len(self.data)







   def __getitem__(      self		:						Dict					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']					,						add_special_tokens=_snake_case))
        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							sentence[0], sentence[1:-1], sentence[-1]
        UpperCAmelCase_          =							sentence[: self.max_seq_length]

        UpperCAmelCase_          =							torch.zeros(self.n_classes)
        UpperCAmelCase_          =							1

        UpperCAmelCase_          =							Image.open(os.path.join(self.data_dir					,						self.data[index]['''img'''])).convert('''RGB''')
        UpperCAmelCase_          =							self.transforms(_snake_case)

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							Counter()
        for row in self.data:
             label_freqs.update(row['''label'''])
        return label_freqs







def    A						(__A   :    List[str]       )   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							[len(row['''sentence''']       ) for row in batch]
     UpperCAmelCase_     ,       UpperCAmelCase_          =							len(__A       ), max(__A       )

     UpperCAmelCase_          =							torch.zeros(__A   ,				__A   ,				dtype=torch.long       )
     UpperCAmelCase_          =							torch.zeros(__A   ,				__A   ,				dtype=torch.long       )

     for i_batch, (input_row, length) in enumerate(zip(__A   ,				__A       )       ):
          UpperCAmelCase_          =							input_row['''sentence''']
          UpperCAmelCase_          =							1

     UpperCAmelCase_          =							torch.stack([row['''image'''] for row in batch]       )
     UpperCAmelCase_          =							torch.stack([row['''label'''] for row in batch]       )
     UpperCAmelCase_          =							torch.stack([row['''image_start_token'''] for row in batch]       )
     UpperCAmelCase_          =							torch.stack([row['''image_end_token'''] for row in batch]       )

     return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor


def    A						()   ->      Dict:







     """simple docstring"""


     return [
         "Crime",
         "Drama",
         "Thriller",
         "Action",
         "Comedy",
         "Romance",
         "Documentary",
         "Short",
         "Mystery",
         "History",
         "Family",
         "Adventure",
         "Fantasy",
         "Sci-Fi",
         "Western",
         "Horror",
         "Sport",
         "War",
         "Music",
         "Musical",
         "Animation",
         "Biography",
         "Film-Noir",
     ]


def    A						()   ->      Optional[Any]:







     """simple docstring"""


     return transforms.Compose(
         [
             transforms.Resize(256       ),
             transforms.CenterCrop(224       ),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]   ,				std=[0.12_221_994, 0.12_145_835, 0.14_380_469]   ,				),
         ]       )












import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:







    class __snake_case			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}					,						_snake_case)
        import datasets

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							depth_estimator(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ])
        self.assertEqual(
            [
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())					,						2_9.3_0_4)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())					,						2.6_6_2)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')






