

"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_							:			int											= ["gpt2"]
A_							:			List[Any]											= "gpt2"

if is_tf_available():





						class    lowerCamelCase						(tf.Module			):

									def __init__(					self				:						Any    ,				__UpperCAmelCase				:						Tuple       )   ->    Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__								=      tokenizer
												SCREAMING_SNAKE_CASE__								=      AutoConfig.from_pretrained(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      TFGPTaLMHeadModel.from_config(__UpperCAmelCase       )





									@tf.function(input_signature=(tf.TensorSpec((None,)    ,				tf.string    ,				name="""text"""       ),)       )
									def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]       )   ->    Any:
												SCREAMING_SNAKE_CASE__								=      self.tokenizer(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__								=      tf.cast(input_ids_dense > 0    ,				tf.intaa       )
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__								=      self.model(input_ids=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase       )["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						super().setUp()

						SCREAMING_SNAKE_CASE__								=      [GPTaTokenizer.from_pretrained(__UpperCAmelCase       ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__								=      [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase       ) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

						SCREAMING_SNAKE_CASE__								=      [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__								=      list(zip(self.test_sentences    ,				self.test_sentences[::-1]       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Any:
						for tokenizer, tf_tokenizer in zip(self.tokenizers    ,				self.tf_tokenizers       ):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__								=      tokenizer([test_inputs]    ,				return_tensors="""tf"""       )
												SCREAMING_SNAKE_CASE__								=      tf_tokenizer([test_inputs]       )

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__								=      python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__								=      tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape       )       )
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase    ,				tf.intaa       ) == tf_outputs_values       )       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__								=      tf.function(__UpperCAmelCase       )
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__								=      tf.constant(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      compiled_tokenizer(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      tf_tokenizer(__UpperCAmelCase       )

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Optional[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__								=      ModelToSave(tokenizer=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      tf.convert_to_tensor([self.test_sentences[0]]       )
									SCREAMING_SNAKE_CASE__								=      model.serving(__UpperCAmelCase       )  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__								=      Path(__UpperCAmelCase       ) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase    ,				__UpperCAmelCase    ,				signatures={"""serving_default""": model.serving}       )
												SCREAMING_SNAKE_CASE__								=      tf.saved_model.load(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      loaded_model.signatures["""serving_default"""](__UpperCAmelCase       )["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output       )       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__								=      tf.convert_to_tensor([self.test_sentences[0]]       )
									SCREAMING_SNAKE_CASE__								=      tf_tokenizer(__UpperCAmelCase       )  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__								=      tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__								=      TFGPTaTokenizer.from_config(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model_from_config(__UpperCAmelCase       )

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]       )       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Tuple:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__								=      1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__								=      tf.convert_to_tensor([self.test_sentences[0]]       )
												SCREAMING_SNAKE_CASE__								=      tf_tokenizer(__UpperCAmelCase    ,				max_length=__UpperCAmelCase       )

												SCREAMING_SNAKE_CASE__								=      out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length





"""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


A_							:			Union[str, Any]											= logging.get_logger(__name__)

A_							:			Tuple											= "▁"

A_							:			int											= {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

A_							:			Dict											= {
    "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",
    },
}

A_							:			Dict											= {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
A_							:			Any											= {
    "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    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Optional[int]							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		Any							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		Tuple							=							['input_ids', 'attention_mask']

			lowerCamelCase__   :		List[int]							=							[]
			lowerCamelCase__   :		List[int]							=							[]

			def __init__(					self				:						List[str]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						Optional[Any]=None    ,				__UpperCAmelCase				:						int="<s>"    ,				__UpperCAmelCase				:						Optional[int]="</s>"    ,				__UpperCAmelCase				:						Any="</s>"    ,				__UpperCAmelCase				:						Any="<pad>"    ,				__UpperCAmelCase				:						str="<unk>"    ,				__UpperCAmelCase				:						Dict="m2m100"    ,				__UpperCAmelCase				:						Optional[Dict[str, Any]] = None    ,				__UpperCAmelCase				:						str=8    ,				**__UpperCAmelCase				:						str    ,				)   ->    None:
						SCREAMING_SNAKE_CASE__								=      {} if sp_model_kwargs is None else sp_model_kwargs

						SCREAMING_SNAKE_CASE__								=      language_codes
						SCREAMING_SNAKE_CASE__								=      FAIRSEQ_LANGUAGE_CODES[language_codes]
						SCREAMING_SNAKE_CASE__								=      {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

						SCREAMING_SNAKE_CASE__								=      kwargs.get("""additional_special_tokens"""    ,				[]       )
						kwargs["additional_special_tokens"] += [
						    self.get_lang_token(__UpperCAmelCase       )
						    for lang_code in fairseq_language_code
						    if self.get_lang_token(__UpperCAmelCase       ) not in kwargs["additional_special_tokens"]
						]

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

						SCREAMING_SNAKE_CASE__								=      vocab_file
						SCREAMING_SNAKE_CASE__								=      load_json(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.encoder.items()}
						SCREAMING_SNAKE_CASE__								=      spm_file
						SCREAMING_SNAKE_CASE__								=      load_spm(__UpperCAmelCase    ,				self.sp_model_kwargs       )

						SCREAMING_SNAKE_CASE__								=      len(self.encoder       )

						SCREAMING_SNAKE_CASE__								=      {
						    self.get_lang_token(__UpperCAmelCase       ): self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase       )
						}
						SCREAMING_SNAKE_CASE__								=      {lang_code: self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase       )}
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.lang_token_to_id.items()}

						SCREAMING_SNAKE_CASE__								=      src_lang if src_lang is not None else """en"""
						SCREAMING_SNAKE_CASE__								=      tgt_lang
						SCREAMING_SNAKE_CASE__								=      self.get_lang_id(self._src_lang       )
						self.set_src_lang_special_tokens(self._src_lang       )

						SCREAMING_SNAKE_CASE__								=      num_madeup_words

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    int:
						return len(self.encoder       ) + len(self.lang_token_to_id       )

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						return self._src_lang

			@src_lang.setter
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      new_src_lang
						self.set_src_lang_special_tokens(self._src_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						str       )   ->    List[str]:
						return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						Tuple       )   ->    Tuple:
						if token in self.lang_token_to_id:
									return self.lang_token_to_id[token]
						return self.encoder.get(__UpperCAmelCase    ,				self.encoder[self.unk_token]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    str:
						if index in self.id_to_lang_token:
									return self.id_to_lang_token[index]
						return self.decoder.get(__UpperCAmelCase    ,				self.unk_token       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      """"""
						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(__UpperCAmelCase       ) + token
												SCREAMING_SNAKE_CASE__								=      []
									else:
												current_sub_tokens.append(__UpperCAmelCase       )
						out_string += self.sp_model.decode(__UpperCAmelCase       )
						return out_string.strip()

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None    ,				__UpperCAmelCase				:						bool = False       )   ->    List[int]:

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

						SCREAMING_SNAKE_CASE__								=      [1] * len(self.prefix_tokens       )
						SCREAMING_SNAKE_CASE__								=      [1] * len(self.suffix_tokens       )
						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 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						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 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      {self.convert_ids_to_tokens(__UpperCAmelCase       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab

			def __getstate__(					self				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.__dict__.copy()
						SCREAMING_SNAKE_CASE__								=      None
						return state

			def __setstate__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      d

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						SCREAMING_SNAKE_CASE__								=      Path(__UpperCAmelCase       )
						if not save_dir.is_dir():
									raise OSError(F"""{save_directory} should be a directory"""       )
						SCREAMING_SNAKE_CASE__								=      save_dir / (
						    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
						)
						SCREAMING_SNAKE_CASE__								=      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:
												SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
												fi.write(__UpperCAmelCase       )

						return (str(__UpperCAmelCase       ), str(__UpperCAmelCase       ))

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						str = "en"    ,				__UpperCAmelCase				:						Optional[List[str]] = None    ,				__UpperCAmelCase				:						str = "ro"    ,				**__UpperCAmelCase				:						str    ,				)   ->    BatchEncoding:
						SCREAMING_SNAKE_CASE__								=      src_lang
						SCREAMING_SNAKE_CASE__								=      tgt_lang
						self.set_src_lang_special_tokens(self.src_lang       )
						return super().prepare_seqaseq_batch(__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Optional[str]    ,				__UpperCAmelCase				:						Optional[str]    ,				**__UpperCAmelCase				:						Tuple       )   ->    str:
						if src_lang is None or tgt_lang is None:
									raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
						SCREAMING_SNAKE_CASE__								=      src_lang
						SCREAMING_SNAKE_CASE__								=      self(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase    ,				**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.get_lang_id(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      tgt_lang_id
						return inputs

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    int:
						self.set_src_lang_special_tokens(self.src_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    str:
						self.set_tgt_lang_special_tokens(self.tgt_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						str       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      self.get_lang_token(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.lang_token_to_id[lang_token]
						SCREAMING_SNAKE_CASE__								=      [self.cur_lang_id]
						SCREAMING_SNAKE_CASE__								=      [self.eos_token_id]

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						str       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      self.get_lang_token(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.lang_token_to_id[lang_token]
						SCREAMING_SNAKE_CASE__								=      [self.cur_lang_id]
						SCREAMING_SNAKE_CASE__								=      [self.eos_token_id]

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						str       )   ->    str:
						return self.lang_code_to_token[lang]





			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						str       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.get_lang_token(__UpperCAmelCase       )
						return self.lang_token_to_id[lang_token]

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      sentencepiece.SentencePieceProcessor(**snake_case__    )
			spm.Load(str(snake_case__    )    )
			return spm

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				"""r"""    ) as f:
						return json.load(snake_case__    )




def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				"""w"""    ) as f:
						json.dump(snake_case__		,				snake_case__		,				indent=2    )


