'''simple docstring'''


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


snake_case__					:      Any				      =  logging.get_logger(__name__)

snake_case__					:      Tuple				      =  '''▁'''

snake_case__					:      Dict				      =  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

snake_case__					:      Any				      =  {
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

snake_case__					:      Tuple				      =  {
    '''facebook/m2m100_418M''': 1024,
}

# fmt: off
snake_case__					:      Tuple				      =  {
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		VOCAB_FILES_NAMES
  lowerCamelCase_		:List[str]						   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase_		:Any						   =		PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase_		:str						   =		['''input_ids''', '''attention_mask''']

  lowerCamelCase_		:List[int]						   =		[]
  lowerCamelCase_		:List[int]						   =		[]


  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_=None				,		snake_case_=None				,		snake_case_="<s>"				,		snake_case_="</s>"				,		snake_case_="</s>"				,		snake_case_="<pad>"				,		snake_case_="<unk>"				,		snake_case_="m2m100"				,		snake_case_ = None				,		snake_case_=8				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				{} if sp_model_kwargs is None else sp_model_kwargs

       UpperCAmelCase_							:       Union[str, Any]       =				language_codes
       UpperCAmelCase_							:       Optional[int]       =				FAIRSEQ_LANGUAGE_CODES[language_codes]
       UpperCAmelCase_							:       Tuple       =				{lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

       UpperCAmelCase_							:       List[Any]       =				kwargs.get('additional_special_tokens'				,		[]						)
       kwargs["additional_special_tokens"] += [
           self.get_lang_token(snake_case_						)
           for lang_code in fairseq_language_code
           if self.get_lang_token(snake_case_						) not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           src_lang=snake_case_				,		tgt_lang=snake_case_				,		bos_token=snake_case_				,		eos_token=snake_case_				,		sep_token=snake_case_				,		unk_token=snake_case_				,		pad_token=snake_case_				,		language_codes=snake_case_				,		sp_model_kwargs=self.sp_model_kwargs				,		num_madeup_words=snake_case_				,		**snake_case_				,		)

       UpperCAmelCase_							:       Optional[int]       =				vocab_file
       UpperCAmelCase_							:       int       =				load_json(snake_case_						)
       UpperCAmelCase_							:       List[Any]       =				{v: k for k, v in self.encoder.items()}
       UpperCAmelCase_							:       str       =				spm_file
       UpperCAmelCase_							:       Any       =				load_spm(snake_case_				,		self.sp_model_kwargs						)

       UpperCAmelCase_							:       Any       =				len(self.encoder						)

       UpperCAmelCase_							:       Any       =				{
           self.get_lang_token(snake_case_						): self.encoder_size + i for i, lang_code in enumerate(snake_case_						)
       }
       UpperCAmelCase_							:       Union[str, Any]       =				{lang_code: self.encoder_size + i for i, lang_code in enumerate(snake_case_						)}
       UpperCAmelCase_							:       Union[str, Any]       =				{v: k for k, v in self.lang_token_to_id.items()}

       UpperCAmelCase_							:       List[str]       =				src_lang if src_lang is not None else 'en'
       UpperCAmelCase_							:       Optional[Any]       =				tgt_lang
       UpperCAmelCase_							:       Dict       =				self.get_lang_id(self._src_lang						)
       self.set_src_lang_special_tokens(self._src_lang						)

       UpperCAmelCase_							:       str       =				num_madeup_words


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return len(self.encoder						) + len(self.lang_token_to_id						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._src_lang


  @src_lang.setter
  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       return self.sp_model.encode(snake_case_				,		out_type=snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
       return self.encoder.get(snake_case_				,		self.encoder[self.unk_token]						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
       return self.decoder.get(snake_case_				,		self.unk_token						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				[]
       UpperCAmelCase_							:       List[Any]       =				''
       for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(snake_case_						) + token
                 UpperCAmelCase_							:       Dict       =				[]
            else:
                 current_sub_tokens.append(snake_case_						)
       out_string += self.sp_model.decode(snake_case_						)
       return out_string.strip()


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None				,		snake_case_ = False						):




       '''simple docstring'''

       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=snake_case_				,		token_ids_a=snake_case_				,		already_has_special_tokens=snake_case_						)

       UpperCAmelCase_							:       Any       =				[1] * len(self.prefix_tokens						)
       UpperCAmelCase_							:       Optional[Any]       =				[1] * len(self.suffix_tokens						)
       if token_ids_a is None:
            return prefix_ones + ([0] * len(snake_case_						)) + suffix_ones
       return prefix_ones + ([0] * len(snake_case_						)) + ([0] * len(snake_case_						)) + suffix_ones


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				{self.convert_ids_to_tokens(snake_case_						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab


  def __getstate__(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.__dict__.copy()
       UpperCAmelCase_							:       str       =				None
       return state


  def __setstate__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				d

       # for backward compatibility
       if not hasattr(self				,		'sp_model_kwargs'						):
            UpperCAmelCase_							:       str       =				{}

       UpperCAmelCase_							:       Dict       =				load_spm(self.spm_file				,		self.sp_model_kwargs						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				Path(snake_case_						)
       if not save_dir.is_dir():
            raise OSError(F'''{save_directory} should be a directory'''						)
       UpperCAmelCase_							:       Dict       =				save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
       )
       UpperCAmelCase_							:       int       =				save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
       )

       save_json(self.encoder				,		snake_case_						)

       if os.path.abspath(self.spm_file						) != os.path.abspath(snake_case_						) and os.path.isfile(self.spm_file						):
            copyfile(self.spm_file				,		snake_case_						)
       elif not os.path.isfile(self.spm_file						):
            with open(snake_case_				,		'wb'						) as fi:
                 UpperCAmelCase_							:       Optional[int]       =				self.sp_model.serialized_model_proto()
                 fi.write(snake_case_						)

       return (str(snake_case_						), str(snake_case_						))


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = "en"				,		snake_case_ = None				,		snake_case_ = "ro"				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				src_lang
       UpperCAmelCase_							:       Dict       =				tgt_lang
       self.set_src_lang_special_tokens(self.src_lang						)
       return super().prepare_seqaseq_batch(snake_case_				,		snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						):




       '''simple docstring'''

       if src_lang is None or tgt_lang is None:
            raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'						)
       UpperCAmelCase_							:       Tuple       =				src_lang
       UpperCAmelCase_							:       Tuple       =				self(snake_case_				,		add_special_tokens=snake_case_				,		**snake_case_						)
       UpperCAmelCase_							:       Dict       =				self.get_lang_id(snake_case_						)
       UpperCAmelCase_							:       Any       =				tgt_lang_id
       return inputs


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.set_src_lang_special_tokens(self.src_lang						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.set_tgt_lang_special_tokens(self.tgt_lang						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.get_lang_token(snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				self.lang_token_to_id[lang_token]
       UpperCAmelCase_							:       Optional[int]       =				[self.cur_lang_id]
       UpperCAmelCase_							:       List[str]       =				[self.eos_token_id]


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				self.get_lang_token(snake_case_						)
       UpperCAmelCase_							:       str       =				self.lang_token_to_id[lang_token]
       UpperCAmelCase_							:       List[str]       =				[self.cur_lang_id]
       UpperCAmelCase_							:       List[Any]       =				[self.eos_token_id]


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       return self.lang_code_to_token[lang]






  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				self.get_lang_token(snake_case_						)
       return self.lang_token_to_id[lang_token]







def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Dict[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				sentencepiece.SentencePieceProcessor(**lowerCamelCase_       )
     spm.Load(str(lowerCamelCase_       )       )
     return spm


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     with open(lowerCamelCase_     ,					'r'       ) as f:
          return json.load(lowerCamelCase_       )





def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							str       ):



     """simple docstring"""





     with open(lowerCamelCase_     ,					'w'       ) as f:
          json.dump(lowerCamelCase_     ,					lowerCamelCase_     ,					indent=2       )







'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





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






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				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_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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






