



from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase__   =       logging.get_logger(__name__)







@add_end_docstrings(a__						)
class 			__lowerCAmelCase    (			a__						):



			def __init__(						self   :  Any ,		*A   :  Union[str, Any] ,		**A   :  Union[str, Any])   ->    List[Any]:



						"""simple docstring"""






						super().__init__(*__lowerCAmelCase ,		**__lowerCAmelCase)
						requires_backends(self ,		'vision')
						self.check_model_type(__lowerCAmelCase)



			def __call__(						self   :  List[Any] ,		A   :  Any ,		**A   :  Union[str, Any])   ->    int:



						"""simple docstring"""






						return super().__call__(__lowerCAmelCase ,		**__lowerCAmelCase)



			def     _lowerCamelCase       (						self   :  Optional[int] ,		**A   :  str)   ->    Optional[Any]:



						"""simple docstring"""






						return {}, {}, {}



			def     _lowerCamelCase       (						self   :  Optional[Any] ,		A   :  Tuple)   ->    Tuple:



						"""simple docstring"""






						_UpperCAmelCase		       =   load_image(__lowerCAmelCase)
						_UpperCAmelCase		       =   image.size
						_UpperCAmelCase		       =   self.image_processor(images=__lowerCAmelCase ,		return_tensors=self.framework)
						return model_inputs



			def     _lowerCamelCase       (						self   :  str ,		A   :  Optional[Any])   ->    int:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.model(**__lowerCAmelCase)
						return model_outputs



			def     _lowerCamelCase       (						self   :  List[str] ,		A   :  Tuple)   ->    Dict:



						"""simple docstring"""






						_UpperCAmelCase		       =   model_outputs.predicted_depth
						_UpperCAmelCase		       =   torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1) ,		size=self.image_size[::-1] ,		mode='bicubic' ,		align_corners=__lowerCAmelCase)
						_UpperCAmelCase		       =   prediction.squeeze().cpu().numpy()
						_UpperCAmelCase		       =   (output * 2_55 / np.max(__lowerCAmelCase)).astype('uint8')
						_UpperCAmelCase		       =   Image.fromarray(__lowerCAmelCase)
						_UpperCAmelCase		       =   {}
						_UpperCAmelCase		       =   predicted_depth
						_UpperCAmelCase		       =   depth
						return output_dict

'''simple docstring'''

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    CTRLTokenizer
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__				:    Optional[Any]  =					['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
        lowercase__				:    str  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )
        lowercase__				:    Tuple  =					['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
        lowercase__				:    Optional[Any]  =					{'''unk_token''': '''<unk>'''}

        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  List[str]:
        kwargs.update(self.special_tokens_map )
        return CTRLTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    List[str]  =					'''adapt react readapt apt'''
        lowercase__				:    Union[str, Any]  =					'''adapt react readapt apt'''
        return input_text, output_text


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					CTRLTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Optional[Any]  =					'''adapt react readapt apt'''
        lowercase__				:    Dict  =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    int  =					tokens + [tokenizer.unk_token]

        lowercase__				:    List[str]  =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

