






'''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    ):
	_lowerCamelCase              =  VOCAB_FILES_NAMES
	_lowerCamelCase              =  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase              =  MAX_MODEL_INPUT_SIZES
	_lowerCamelCase              =  ['''input_ids''', '''attention_mask''']

	_lowerCamelCase              =  []

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

			super().__init__(
			    bos_token=UpperCAmelCase_  ,					eos_token=UpperCAmelCase_  ,					unk_token=UpperCAmelCase_  ,					pad_token=UpperCAmelCase_  ,					do_upper_case=UpperCAmelCase_  ,					do_lower_case=UpperCAmelCase_  ,					tgt_lang=UpperCAmelCase_  ,					lang_codes=UpperCAmelCase_  ,					sp_model_kwargs=self.sp_model_kwargs  ,					**UpperCAmelCase_  ,					)
			UpperCamelCase__				:					List[str]					=					do_upper_case
			UpperCamelCase__				:					Any					=					do_lower_case

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

			if lang_codes is not None:
					UpperCamelCase__				:					Union[str, Any]					=					lang_codes
					UpperCamelCase__				:					List[Any]					=					LANGUAGES[lang_codes]
					UpperCamelCase__				:					List[Any]					=					[F'<lang:{lang}>' for lang in self.langs]
					UpperCamelCase__				:					Union[str, Any]					=					{lang: self.sp_model.PieceToId(F'<lang:{lang}>') for lang in self.langs}

					UpperCamelCase__				:					int					=					self.lang_tokens
					UpperCamelCase__				:					List[Any]					=					tgt_lang if tgt_lang is not None else self.langs[0]

					self.set_tgt_lang_special_tokens(self._tgt_lang)
			else:
					UpperCamelCase__				:					int					=					{}

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return len(self.encoder)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self._tgt_lang

	@tgt_lang.setter
	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					new_tgt_lang
			self.set_tgt_lang_special_tokens(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					List[Any]					=					self.lang_code_to_id[tgt_lang]
			UpperCamelCase__				:					Optional[int]					=					[lang_code_id]

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

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						Optional[Any]):
			return self.encoder.get(UpperCAmelCase_  ,					self.encoder[self.unk_token])

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						int):
			return self.decoder.get(UpperCAmelCase_  ,					self.unk_token)

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

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						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      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None  ,					UpperCAmelCase_   :						bool = False):

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

			UpperCamelCase__				:					Union[str, Any]					=					[1] * len(self.prefix_tokens)
			UpperCamelCase__				:					str					=					[1]
			if token_ids_a is None:
					return prefix_ones + ([0] * len(UpperCAmelCase_)) + suffix_ones
			return prefix_ones + ([0] * len(UpperCAmelCase_)) + ([0] * len(UpperCAmelCase_)) + suffix_ones

	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					List[Any]					=					self.encoder.copy()
			vocab.update(self.added_tokens_encoder)
			return vocab

	def __getstate__(      self   :						str):
			UpperCamelCase__				:					int					=					self.__dict__.copy()
			UpperCamelCase__				:					Tuple					=					None
			return state

	def __setstate__(      self   :						int  ,					UpperCAmelCase_   :						Dict):
			UpperCamelCase__				:					Optional[int]					=					d

			# for backward compatibility
			if not hasattr(self  ,					'sp_model_kwargs'):
					UpperCamelCase__				:					Union[str, Any]					=					{}

			UpperCamelCase__				:					Any					=					load_spm(self.spm_file  ,					self.sp_model_kwargs)





	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = None):
			UpperCamelCase__				:					List[Any]					=					Path(UpperCAmelCase_)
			assert save_dir.is_dir(), F'{save_directory} should be a directory'
			UpperCamelCase__				:					Dict					=					save_dir / (
			    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
			)
			UpperCamelCase__				:					str					=					save_dir / (
			    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
			)

			save_json(self.encoder  ,					UpperCAmelCase_)

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

			return (str(UpperCAmelCase_), str(UpperCAmelCase_))







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     sentencepiece.SentencePieceProcessor:
		UpperCamelCase__				:					Any					=					sentencepiece.SentencePieceProcessor(**lowerCamelCase_)
		spm.Load(str(lowerCamelCase_))
		return spm







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Union[Dict, List]:
		with open(lowerCamelCase_     ,      'r') as f:
				return json.load(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     None:
		with open(lowerCamelCase_     ,      'w') as f:
				json.dump(lowerCamelCase_     ,      lowerCamelCase_     ,      indent=2)












'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					int					=					tempfile.mkdtemp()

			# fmt: off
			UpperCamelCase__				:					Union[str, Any]					=					['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
			# fmt: on
			UpperCamelCase__				:					Dict					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))
			UpperCamelCase__				:					Optional[Any]					=					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			UpperCamelCase__				:					Union[str, Any]					=					{'unk_token': '<unk>'}

			UpperCamelCase__				:					Union[str, Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__				:					Optional[Any]					=					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(UpperCAmelCase_) + '\n')
			with open(self.merges_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write('\n'.join(UpperCAmelCase_))

			UpperCamelCase__				:					Dict					=					{
			    'do_resize': True,
			    'size': 20,
			    'do_center_crop': True,
			    'crop_size': 18,
			    'do_normalize': True,
			    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
			    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
			}
			UpperCamelCase__				:					Any					=					os.path.join(self.tmpdirname  ,					UpperCAmelCase_)
			with open(self.image_processor_file  ,					'w'  ,					encoding='utf-8') as fp:
					json.dump(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPTokenizer.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						str):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			shutil.rmtree(self.tmpdirname)

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					List[str]					=					[np.random.randint(255  ,					size=(3, 30, 400)  ,					dtype=np.uinta)]

			UpperCamelCase__				:					List[str]					=					[Image.fromarray(np.moveaxis(UpperCAmelCase_  ,					0  ,					-1)) for x in image_inputs]

			return image_inputs

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					Optional[Any]					=					self.get_rust_tokenizer()
			UpperCamelCase__				:					Any					=					self.get_image_processor()

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_slow.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Any					=					CLIPProcessor.from_pretrained(self.tmpdirname  ,					use_fast=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_fast.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Optional[int]					=					CLIPProcessor.from_pretrained(self.tmpdirname)

			self.assertEqual(processor_slow.tokenizer.get_vocab()  ,					tokenizer_slow.get_vocab())
			self.assertEqual(processor_fast.tokenizer.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertEqual(tokenizer_slow.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertIsInstance(processor_slow.tokenizer  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.tokenizer  ,					UpperCAmelCase_)

			self.assertEqual(processor_slow.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertEqual(processor_fast.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertIsInstance(processor_slow.image_processor  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.image_processor  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					Union[str, Any]					=					CLIPProcessor(tokenizer=self.get_tokenizer()  ,					image_processor=self.get_image_processor())
			processor.save_pretrained(self.tmpdirname)

			UpperCamelCase__				:					List[str]					=					self.get_tokenizer(bos_token='(BOS)'  ,					eos_token='(EOS)')
			UpperCamelCase__				:					Tuple					=					self.get_image_processor(do_normalize=UpperCAmelCase_  ,					padding_value=1.0)

			UpperCamelCase__				:					Dict					=					CLIPProcessor.from_pretrained(
			    self.tmpdirname  ,					bos_token='(BOS)'  ,					eos_token='(EOS)'  ,					do_normalize=UpperCAmelCase_  ,					padding_value=1.0)

			self.assertEqual(processor.tokenizer.get_vocab()  ,					tokenizer_add_kwargs.get_vocab())
			self.assertIsInstance(processor.tokenizer  ,					UpperCAmelCase_)

			self.assertEqual(processor.image_processor.to_json_string()  ,					image_processor_add_kwargs.to_json_string())
			self.assertIsInstance(processor.image_processor  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					int					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					self.prepare_image_inputs()

			UpperCamelCase__				:					int					=					image_processor(UpperCAmelCase_  ,					return_tensors='np')
			UpperCamelCase__				:					Optional[int]					=					processor(images=UpperCAmelCase_  ,					return_tensors='np')

			for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum()  ,					input_processor[key].sum()  ,					delta=1e-2)

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					Dict					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					'lower newer'

			UpperCamelCase__				:					Union[str, Any]					=					processor(text=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					tokenizer(UpperCAmelCase_)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]  ,					encoded_processor[key])

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Optional[int]					=					self.get_image_processor()
			UpperCamelCase__				:					List[str]					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					'lower newer'
			UpperCamelCase__				:					List[Any]					=					self.prepare_image_inputs()

			UpperCamelCase__				:					str					=					processor(text=UpperCAmelCase_  ,					images=UpperCAmelCase_)

			self.assertListEqual(list(inputs.keys())  ,					['input_ids', 'attention_mask', 'pixel_values'])

			# test if it raises when no input is passed
			with pytest.raises(UpperCAmelCase_):
					processor()

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Any					=					self.get_image_processor()
			UpperCamelCase__				:					Dict					=					self.get_tokenizer()

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			UpperCamelCase__				:					List[Any]					=					processor.batch_decode(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					tokenizer.batch_decode(UpperCAmelCase_)

			self.assertListEqual(UpperCAmelCase_  ,					UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_image_processor()
			UpperCamelCase__				:					List[str]					=					self.get_tokenizer()

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					'lower newer'
			UpperCamelCase__				:					Optional[int]					=					self.prepare_image_inputs()

			UpperCamelCase__				:					List[str]					=					processor(text=UpperCAmelCase_  ,					images=UpperCAmelCase_)

			self.assertListEqual(list(inputs.keys())  ,					processor.model_input_names)




