

'''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 PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			'''▁'''

lowerCAmelCase__      		=			{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

lowerCAmelCase__      		=			{
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

lowerCAmelCase__      		=			{
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

lowerCAmelCase__      		=			['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

lowerCAmelCase__      		=			{'''mustc''': MUSTC_LANGS}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Dict           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						int           =    MAX_MODEL_INPUT_SIZES
  SCREAMING_SNAKE_CASE					:						int           =    ['input_ids', 'attention_mask']

  SCREAMING_SNAKE_CASE					:						List[int]           =    []






  def __init__(   self     :  Optional[int]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  Tuple="<s>"      ,lowercase__     :  List[str]="</s>"      ,lowercase__     :  str="<pad>"      ,lowercase__     :  Optional[Any]="<unk>"      ,lowercase__     :  Dict=False      ,lowercase__     :  List[Any]=False      ,lowercase__     :  int=None      ,lowercase__     :  Dict=None      ,lowercase__     :  Optional[Dict[str, Any]] = None      ,**lowercase__     :  Optional[Any]      ,):
   __lowercase													=				{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=lowercase__      ,eos_token=lowercase__      ,unk_token=lowercase__      ,pad_token=lowercase__      ,do_upper_case=lowercase__      ,do_lower_case=lowercase__      ,tgt_lang=lowercase__      ,lang_codes=lowercase__      ,sp_model_kwargs=self.sp_model_kwargs      ,**lowercase__      ,)
   __lowercase													=				do_upper_case
   __lowercase													=				do_lower_case

   __lowercase													=				load_json(lowercase__    )
   __lowercase													=				{v: k for k, v in self.encoder.items()}
   __lowercase													=				spm_file
   __lowercase													=				load_spm(lowercase__      ,self.sp_model_kwargs    )

   if lang_codes is not None:
    __lowercase													=				lang_codes
    __lowercase													=				LANGUAGES[lang_codes]
    __lowercase													=				[F"<lang:{lang}>" for lang in self.langs]
    __lowercase													=				{lang: self.sp_model.PieceToId(F"<lang:{lang}>"    ) for lang in self.langs}

    __lowercase													=				self.lang_tokens
    __lowercase													=				tgt_lang if tgt_lang is not None else self.langs[0]

    self.set_tgt_lang_special_tokens(self._tgt_lang    )
   else:
    __lowercase													=				{}






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return len(self.encoder    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   return self._tgt_lang






  @tgt_lang.setter
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  str    ):
   __lowercase													=				new_tgt_lang
   self.set_tgt_lang_special_tokens(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  str    ):
   __lowercase													=				self.lang_code_to_id[tgt_lang]
   __lowercase													=				[lang_code_id]






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  str    ):
   return self.sp_model.encode(lowercase__      ,out_type=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Optional[Any]    ):
   return self.encoder.get(lowercase__      ,self.encoder[self.unk_token]    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  int    ):
   return self.decoder.get(lowercase__      ,self.unk_token    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[str]    ):
   __lowercase													=				[]
   __lowercase													=				''''''
   for token in tokens:
    # make sure that special tokens are not decoded using sentencepiece model
    if token in self.all_special_tokens:
     __lowercase													=				self.sp_model.decode(lowercase__    )
     out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
     __lowercase													=				[]
    else:
     current_sub_tokens.append(lowercase__    )
   __lowercase													=				self.sp_model.decode(lowercase__    )
   out_string += decoded.upper() if self.do_upper_case else decoded
   return out_string.strip()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Any      ,lowercase__     :  Optional[int]=None    ):
   if token_ids_a is None:
    return self.prefix_tokens + token_ids_a + [self.eos_token_id]
   # 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.eos_token_id]






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None      ,lowercase__     :  bool = False    ):

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

   __lowercase													=				[1] * len(self.prefix_tokens    )
   __lowercase													=				[1]
   if token_ids_a is None:
    return prefix_ones + ([0] * len(lowercase__    )) + suffix_ones
   return prefix_ones + ([0] * len(lowercase__    )) + ([0] * len(lowercase__    )) + suffix_ones






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				self.encoder.copy()
   vocab.update(self.added_tokens_encoder    )
   return vocab






  def __getstate__(   self     :  Optional[int]    ):
   __lowercase													=				self.__dict__.copy()
   __lowercase													=				None
   return state






  def __setstate__(   self     :  Tuple      ,lowercase__     :  Dict    ):
   __lowercase													=				d

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

   __lowercase													=				load_spm(self.spm_file      ,self.sp_model_kwargs    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  str      ,lowercase__     :  Optional[str] = None    ):
   __lowercase													=				Path(lowercase__    )
   assert save_dir.is_dir(), F"{save_directory} should be a directory"
   __lowercase													=				save_dir / (
       (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
   )
   __lowercase													=				save_dir / (
       (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
   )

   save_json(self.encoder      ,lowercase__    )

   if os.path.abspath(self.spm_file    ) != os.path.abspath(lowercase__    ) and os.path.isfile(self.spm_file    ):
    copyfile(self.spm_file      ,lowercase__    )
   elif not os.path.isfile(self.spm_file    ):
    with open(lowercase__      ,'''wb'''    ) as fi:
     __lowercase													=				self.sp_model.serialized_model_proto()
     fi.write(lowercase__    )

   return (str(lowercase__    ), str(lowercase__    ))


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				sentencepiece.SentencePieceProcessor(**A__   )
 spm.Load(str(A__   )   )
 return spm


def 	_A  (					A__   ):






 """simple docstring"""



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






def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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




'''simple docstring'''






import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import CvtForImageClassification, CvtModel
 from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )
   self.parent.assertTrue(hasattr(lowercase__      ,'''embed_dim'''    )    )
   self.parent.assertTrue(hasattr(lowercase__      ,'''num_heads'''    )    )






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Optional[int]=1_3      ,lowercase__     :  List[Any]=6_4      ,lowercase__     :  Optional[int]=3      ,lowercase__     :  Dict=[1_6, 4_8, 9_6]      ,lowercase__     :  Optional[Any]=[1, 3, 6]      ,lowercase__     :  Tuple=[1, 2, 1_0]      ,lowercase__     :  Optional[int]=[7, 3, 3]      ,lowercase__     :  str=[4, 2, 2]      ,lowercase__     :  Dict=[2, 1, 1]      ,lowercase__     :  Tuple=[2, 2, 2]      ,lowercase__     :  Tuple=[False, False, True]      ,lowercase__     :  int=[0.0, 0.0, 0.0]      ,lowercase__     :  str=0.0_2      ,lowercase__     :  Union[str, Any]=1e-1_2      ,lowercase__     :  Optional[int]=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Optional[Any]=2      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				image_size
   __lowercase													=				patch_sizes
   __lowercase													=				patch_stride
   __lowercase													=				patch_padding
   __lowercase													=				is_training
   __lowercase													=				use_labels
   __lowercase													=				num_labels
   __lowercase													=				num_channels
   __lowercase													=				embed_dim
   __lowercase													=				num_heads
   __lowercase													=				stride_kv
   __lowercase													=				depth
   __lowercase													=				cls_token
   __lowercase													=				attention_drop_rate
   __lowercase													=				initializer_range
   __lowercase													=				layer_norm_eps






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

   __lowercase													=				self.get_config()
   return config, pixel_values, labels






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return CvtConfig(
       image_size=self.image_size      ,num_labels=self.num_labels      ,num_channels=self.num_channels      ,embed_dim=self.embed_dim      ,num_heads=self.num_heads      ,patch_sizes=self.patch_sizes      ,patch_padding=self.patch_padding      ,patch_stride=self.patch_stride      ,stride_kv=self.stride_kv      ,depth=self.depth      ,cls_token=self.cls_token      ,attention_drop_rate=self.attention_drop_rate      ,initializer_range=self.initializer_range      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  Any      ,lowercase__     :  List[str]    ):
   __lowercase													=				CvtModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__    )
   __lowercase													=				(self.image_size, self.image_size)
   __lowercase						,			__lowercase													=				image_size[0], image_size[1]
   for i in range(len(self.depth    )    ):
    __lowercase													=				floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
    __lowercase													=				floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.embed_dim[-1], height, width)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Dict    ):
   __lowercase													=				self.num_labels
   __lowercase													=				CvtForImageClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase						,			__lowercase													=				config_and_inputs
   __lowercase													=				{'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Any           =    (CvtModel, CvtForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    (
      {'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE					:						Optional[int]           =    False
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    False
  SCREAMING_SNAKE_CASE					:						int           =    False
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False
  SCREAMING_SNAKE_CASE					:						str           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				CvtModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,has_text_modality=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return






  @unittest.skip(reason='''Cvt does not output attentions'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   pass






  @unittest.skip(reason='''Cvt does not use inputs_embeds'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   pass






  @unittest.skip(reason='''Cvt does not support input and output embeddings'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   pass






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

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

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   def check_hidden_states_output(lowercase__     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  str    ):
    __lowercase													=				model_class(lowercase__    )
    model.to(lowercase__    )
    model.eval()

    with torch.no_grad():
     __lowercase													=				model(**self._prepare_for_class(lowercase__      ,lowercase__    )    )

    __lowercase													=				outputs.hidden_states

    __lowercase													=				len(self.model_tester.depth    )
    self.assertEqual(len(lowercase__    )      ,lowercase__    )

    # verify the first hidden states (first block)
    self.assertListEqual(
        list(hidden_states[0].shape[-3:]    )      ,[
            self.model_tester.embed_dim[0],
            self.model_tester.image_size // 4,
            self.model_tester.image_size // 4,
        ]      ,)

   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				True
    check_hidden_states_output(lowercase__      ,lowercase__      ,lowercase__    )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    __lowercase													=				True

    check_hidden_states_output(lowercase__      ,lowercase__      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__    )






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   pass






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase													=				CvtModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )


def 	_A  (					):






 """simple docstring"""



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






@require_torch
@require_vision
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  @cached_property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(lowercase__    )

   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )

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

   # verify the logits
   __lowercase													=				torch.Size((1, 1_0_0_0)    )
   self.assertEqual(outputs.logits.shape      ,lowercase__    )

   __lowercase													=				torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]    ).to(lowercase__    )

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

