



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

	UpperCAmelCase							 =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch

def 			A_      (	__a     :				Any ,		__a     :				Dict ,		__a     :				Optional[int]=None		):

				"""simple docstring"""
				if rng is None:
								a__				      =		random.Random()

				a__				      =		1
				for dim in shape:
								total_dims *= dim

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

				a__				      =		np.array(__a ,		dtype=jnp.intaa		).reshape(__a		)

				return output

def 			A_      (	__a     :				Tuple ,		__a     :				List[str]=None		):

				"""simple docstring"""
				a__				      =		ids_tensor(__a ,		vocab_size=2 ,		rng=__a		)
				# make sure that at least one token is attended to for each batch
				a__				      =		1
				return attn_mask

@require_flax
class  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					None
							UpperCamelCase__				:      int         =					()


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											a__				      =		2
											a__				      =		inputs["""input_ids"""].shape[-1] // 2
											a__				      =		inputs["""input_ids"""][:max_batch_size, :sequence_length]

											a__				      =		jnp.ones_like(a_		)
											a__				      =		attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											a__				      =		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()`
															a__				      =		config.eos_token_id
											return config, input_ids, attention_mask, max_length


							@is_pt_flax_cross_test
							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length
											a__				      =		0

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a__				      =		getattr(a_  ,  a_		)
															a__				      =		pt_model_class(a_		).eval()
															a__				      =		load_flax_weights_in_pytorch_model(a_  ,  flax_model.params		)

															a__				      =		flax_model.generate(a_		).sequences
															a__				      =		pt_model.generate(torch.tensor(a_  ,  dtype=torch.long		)		)

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

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		True
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length
											a__				      =		2

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length
											a__				      =		2
											a__				      =		2

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[0]  ,  input_ids.shape[0] * config.num_return_sequences		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		True
											a__				      =		max_length
											a__				      =		0.8
											a__				      =		10
											a__				      =		0.3
											a__				      =		1
											a__				      =		8
											a__				      =		9

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		max_length
											a__				      =		1
											a__				      =		8
											a__				      =		9

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		max_length
											a__				      =		2
											a__				      =		1
											a__				      =		8
											a__				      =		9

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()

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

											a__				      =		False
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_  ,  attention_mask=a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_  ,  attention_mask=a_		).sequences

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


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()

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

											a__				      =		True
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_  ,  attention_mask=a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_  ,  attention_mask=a_		).sequences

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







							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()

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

											a__				      =		2
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_  ,  attention_mask=a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_  ,  attention_mask=a_		).sequences

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




@require_flax
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											a__				      =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""		)
											a__				      =		FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""		)

											a__				      =		"""Hello world"""
											a__				      =		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"""		):
															a__				      =		{"""foo""": """bar"""}
															model.generate(a_  ,  **a_		)






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase							 =					{"""bert_for_seq_generation""": 512}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Any         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[str]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      Optional[int]         =					["""input_ids""", """attention_mask"""]


							def __init__(							self  ,  a_  ,  a_="<s>"  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<::::>"  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Add extra_ids to the special token list
											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  pad_token=a_  ,  sep_token=a_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **a_  ,  )

											a__				      =		vocab_file

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a      (							self		):
											return self.sp_model.get_piece_size()


							def _a      (							self		):
											a__				      =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(							self		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

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

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a      (							self  ,  a_		):
											return self.sp_model.encode(a_  ,  out_type=a_		)


							def _a      (							self  ,  a_		):
											return self.sp_model.piece_to_id(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.sp_model.IdToPiece(a_		)
											return token


							def _a      (							self  ,  a_		):
											a__				      =		[]
											a__				      =		""""""
											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(a_		) + token
																			a__				      =		[]
															else:
																			current_sub_tokens.append(a_		)
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		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_		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file  ,  a_		)
											elif not os.path.isfile(self.vocab_file		):
															with open(a_  ,  """wb"""		) as fi:
																			a__				      =		self.sp_model.serialized_model_proto()
																			fi.write(a_		)

											return (out_vocab_file,)

