

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_ta import TaTokenizer
else:
    _lowerCamelCase    :       List[str]        =							None


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

_lowerCamelCase    :       Tuple        =							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase    :       Optional[int]        =							{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase    :       Optional[Any]        =							{
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}





class  __UpperCAmelCase					(	SCREAMING_SNAKE_CASE__						):
   UpperCamelCase       					=					VOCAB_FILES_NAMES
   UpperCamelCase       					=					PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase       					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase       					=					['input_ids', 'attention_mask']
   UpperCamelCase       					=					TaTokenizer

   UpperCamelCase       					=					[]



   def __init__(							self							:					List[str],		__A							:					List[Any]=None,		__A							:					Union[str, Any]=None,		__A							:					Any="</s>",		__A							:					Union[str, Any]="<unk>",		__A							:					int="<pad>",		__A							:					Optional[int]=1_0_0,		__A							:					Union[str, Any]=None,		**__A							:					List[Any],		):



     if extra_ids > 0 and additional_special_tokens is None:
       UpperCAmelCase	:							str						=			[F'''<extra_id_{i}>''' for i in range(a_						)]
     elif extra_ids > 0 and additional_special_tokens is not None:
       # Check that we have the right number of extra special tokens
       UpperCAmelCase	:							int						=			len(set(filter(lambda __A						: bool('''extra_id_''' in str(a_						)						),		a_						)						)						)
       if extra_tokens != extra_ids:
         raise ValueError(
             F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
             ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
             ''' tokens'''						)

     super().__init__(
         a_,		tokenizer_file=a_,		eos_token=a_,		unk_token=a_,		pad_token=a_,		extra_ids=a_,		additional_special_tokens=a_,		**a_,		)

     UpperCAmelCase	:							List[Any]						=			vocab_file
     UpperCAmelCase	:							Tuple						=			False if not self.vocab_file else True
     UpperCAmelCase	:							List[Any]						=			extra_ids



   @staticmethod
   def 	__magic_name__   (							__A							:					List[str],		__A							:					str,		__A							:					int						):



     if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
       UpperCAmelCase	:							Tuple						=			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
       if init_max_model_length is not None and init_max_model_length != max_model_length:
         return init_max_model_length
       elif init_max_model_length is None:
         warnings.warn(
             '''This tokenizer was incorrectly instantiated with a model max length of'''
             F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
             ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
             ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
             F''' {pretrained_model_name_or_path} automatically truncating your input to'''
             F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
             F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
             ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
             ''' instantiate this tokenizer with `model_max_length` set to your preferred value.''',		a_,		)

     return max_model_length



   def 	__magic_name__   (							self							:					Tuple,		__A							:					str,		__A							:					Optional[str] = None						):



     if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''						)

     if not os.path.isdir(a_						):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
       return
     UpperCAmelCase	:							Tuple						=			os.path.join(
         a_,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

     if os.path.abspath(self.vocab_file						) != os.path.abspath(a_						):
       copyfile(self.vocab_file,		a_						)
       logger.info(F'''Copy vocab file to {out_vocab_file}'''						)

     return (out_vocab_file,)



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     UpperCAmelCase	:							List[str]						=			token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a
     else:
       UpperCAmelCase	:							Optional[int]						=			token_ids_a + [self.eos_token_id]
       return self.prefix_tokens + token_ids_a + token_ids_a



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     UpperCAmelCase	:							List[Any]						=			[self.eos_token_id]

     if token_ids_a is None:
       return len(token_ids_a + eos						) * [0]
     return len(token_ids_a + eos + token_ids_a + eos						) * [0]



   def 	__magic_name__   (							self							:					Any						):
     return list(
         set(filter(lambda __A						: bool(re.search(R'''<extra_id_\d+>''',		a_						)						) is not None,		self.additional_special_tokens						)						)						)





   def 	__magic_name__   (							self							:					Tuple						):
     return [self.convert_tokens_to_ids(a_						) for token in self.get_sentinel_tokens()]








import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _lowerCamelCase    :       Any        =							"0.12"  # assumed parallelism: 8


if is_torch_available():
     import torch

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      List[str]=None      )  ->						List[Any]:
  if rng is None:
    UpperCAmelCase	:							Dict						=			random.Random()

  UpperCAmelCase	:							Optional[Any]						=			1
  for dim in shape:
    total_dims *= dim

  UpperCAmelCase	:							List[str]						=			[]
  for _ in range(UpperCAmelCase      ):
    values.append(rng.randint(0    ,			vocab_size - 1      )      )

  UpperCAmelCase	:							List[str]						=			np.array(UpperCAmelCase    ,			dtype=jnp.intaa      ).reshape(UpperCAmelCase      )

  return output

def       a__					(		UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Optional[int]=None      )  ->						List[str]:
  UpperCAmelCase	:							Optional[int]						=			ids_tensor(UpperCAmelCase    ,			vocab_size=2    ,			rng=UpperCAmelCase      )
  # make sure that at least one token is attended to for each batch
  UpperCAmelCase	:							str						=			1
  return attn_mask





@require_flax
class  __UpperCAmelCase					:
   UpperCamelCase       					=					None
   UpperCamelCase       					=					()



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

     # cut to half length & take max batch_size 3
     UpperCAmelCase	:							Optional[int]						=			2
     UpperCAmelCase	:							Dict						=			inputs['''input_ids'''].shape[-1] // 2
     UpperCAmelCase	:							Dict						=			inputs['''input_ids'''][:max_batch_size, :sequence_length]

     UpperCAmelCase	:							Optional[int]						=			jnp.ones_like(__A						)
     UpperCAmelCase	:							Optional[int]						=			attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     UpperCAmelCase	:							Optional[Any]						=			input_ids.shape[-1] + 5
     if config.eos_token_id is not None and config.pad_token_id is None:
       # hack to allow generate for models such as GPT2 as is done in `generate()`
       UpperCAmelCase	:							Optional[Any]						=			config.eos_token_id
     return config, input_ids, attention_mask, max_length



   @is_pt_flax_cross_test
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Any						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Optional[Any]						=			False
     UpperCAmelCase	:							Any						=			max_length
     UpperCAmelCase	:							List[Any]						=			0

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							List[str]						=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
       UpperCAmelCase	:							List[Any]						=			getattr(__A,		__A						)
       UpperCAmelCase	:							Union[str, Any]						=			pt_model_class(__A						).eval()
       UpperCAmelCase	:							Tuple						=			load_flax_weights_in_pytorch_model(__A,		flax_model.params						)

       UpperCAmelCase	:							Dict						=			flax_model.generate(__A						).sequences
       UpperCAmelCase	:							str						=			pt_model.generate(torch.tensor(__A,		dtype=torch.long						)						)

       if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
         UpperCAmelCase	:							Any						=			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

       self.assertListEqual(pt_generation_outputs.numpy().tolist(),		flax_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			False
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							Optional[int]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			True
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							str						=			jit(model.generate						)
       UpperCAmelCase	:							List[Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							int						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Dict						=			False
     UpperCAmelCase	:							Union[str, Any]						=			max_length
     UpperCAmelCase	:							List[Any]						=			2

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							str						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							int						=			jit(model.generate						)
       UpperCAmelCase	:							Union[str, Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Any						=			False
     UpperCAmelCase	:							Optional[int]						=			max_length
     UpperCAmelCase	:							Optional[int]						=			2
     UpperCAmelCase	:							str						=			2

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[0],		input_ids.shape[0] * config.num_return_sequences						)



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							str						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			True
     UpperCAmelCase	:							Union[str, Any]						=			max_length
     UpperCAmelCase	:							Union[str, Any]						=			0.8
     UpperCAmelCase	:							str						=			1_0
     UpperCAmelCase	:							Any						=			0.3
     UpperCAmelCase	:							str						=			1
     UpperCAmelCase	:							Union[str, Any]						=			8
     UpperCAmelCase	:							Optional[Any]						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							List[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[int]						=			jit(model.generate						)
       UpperCAmelCase	:							Any						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Optional[Any]						=			max_length
     UpperCAmelCase	:							Tuple						=			1
     UpperCAmelCase	:							Optional[Any]						=			8
     UpperCAmelCase	:							Optional[int]						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							List[str]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Dict						=			jit(model.generate						)
       UpperCAmelCase	:							List[str]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							List[str]						=			max_length
     UpperCAmelCase	:							Union[str, Any]						=			2
     UpperCAmelCase	:							List[Any]						=			1
     UpperCAmelCase	:							List[str]						=			8
     UpperCAmelCase	:							int						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Optional[Any]						=			model_class(__A						)

       UpperCAmelCase	:							Union[str, Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[str]						=			jit(model.generate						)
       UpperCAmelCase	:							Tuple						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Union[str, Any]						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Tuple						=			False
     UpperCAmelCase	:							str						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Any						=			model_class(__A						)

       UpperCAmelCase	:							Union[str, Any]						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Union[str, Any]						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Union[str, Any]						=			True
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Any						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)





   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Dict						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Union[str, Any]						=			2
     UpperCAmelCase	:							str						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							str						=			model_class(__A						)

       UpperCAmelCase	:							int						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Dict						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)






@require_flax
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Union[str, Any]						=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''						)
     UpperCAmelCase	:							List[str]						=			FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''						)

     UpperCAmelCase	:							int						=			'''Hello world'''
     UpperCAmelCase	:							Optional[int]						=			tokenizer(__A,		return_tensors='''np'''						).input_ids

     # typos are quickly detected (the correct argument is `do_sample`)
     with self.assertRaisesRegex(__A,		'''do_samples'''						):
       model.generate(__A,		do_samples=__A						)

     # arbitrary arguments that will not be used anywhere are also not accepted
     with self.assertRaisesRegex(__A,		'''foo'''						):
       UpperCAmelCase	:							Any						=			{'''foo''': '''bar'''}
       model.generate(__A,		**__A						)





