
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE				:List[str]									=							get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
					from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE				:Any									=							25_0004
SCREAMING_SNAKE_CASE				:Tuple									=							25_0020





@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	MBartTokenizer
	snake_case_          =	MBartTokenizerFast
	snake_case_          =	True
	snake_case_          =	True






	def 							UpperCamelCase_    (			self   :					Dict					):
					super().setUp()

					# We have a SentencePiece fixture for testing
					__A       =					MBartTokenizer(A				,keep_accents=A					)
					tokenizer.save_pretrained(self.tmpdirname					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					MBartTokenizer(A				,keep_accents=A					)

					__A       =					tokenizer.tokenize("This is a test"					)
					self.assertListEqual(A				,["▁This", "▁is", "▁a", "▁t", "est"]					)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(A					)				,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,)

					__A       =					tokenizer.tokenize("I was born in 92000, and this is falsé."					)
					self.assertListEqual(
					    A				,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "9",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "é",
					        ".",
					    ]				,)
					__A       =					tokenizer.convert_tokens_to_ids(A					)
					self.assertListEqual(
					    A				,[
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]				,)

					__A       =					tokenizer.convert_ids_to_tokens(A					)
					self.assertListEqual(
					    A				,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "<unk>",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "<unk>",
					        ".",
					    ]				,)







	def 							UpperCamelCase_    (			self   :					List[str]					):
					if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

					__A       =					(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
													__A       =					self.rust_tokenizer_class.from_pretrained(A				,**A					)
													__A       =					self.tokenizer_class.from_pretrained(A				,**A					)

													__A       =					tempfile.mkdtemp()

													__A       =					tokenizer_r.save_pretrained(A					)
													__A       =					tokenizer_p.save_pretrained(A					)

													# Checks it save with the same files + the tokenizer.json file for the fast one
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files					)					)
													__A       =					tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f					)
													self.assertSequenceEqual(A				,A					)

													# Checks everything loads correctly in the same way
													__A       =					tokenizer_r.from_pretrained(A					)
													__A       =					tokenizer_p.from_pretrained(A					)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A				,A					)					)
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

													shutil.rmtree(A					)

													# Save tokenizer rust, legacy_format=True
													__A       =					tempfile.mkdtemp()

													__A       =					tokenizer_r.save_pretrained(A				,legacy_format=A					)
													__A       =					tokenizer_p.save_pretrained(A					)

													# Checks it save with the same files
													self.assertSequenceEqual(A				,A					)

													# Checks everything loads correctly in the same way
													__A       =					tokenizer_r.from_pretrained(A					)
													__A       =					tokenizer_p.from_pretrained(A					)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A				,A					)					)

													shutil.rmtree(A					)

													# Save tokenizer rust, legacy_format=False
													__A       =					tempfile.mkdtemp()

													__A       =					tokenizer_r.save_pretrained(A				,legacy_format=A					)
													__A       =					tokenizer_p.save_pretrained(A					)

													# Checks it saved the tokenizer.json file
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files					)					)

													# Checks everything loads correctly in the same way
													__A       =					tokenizer_r.from_pretrained(A					)
													__A       =					tokenizer_p.from_pretrained(A					)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A				,A					)					)

													shutil.rmtree(A					)





@require_torch
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	"facebook/mbart-large-en-ro"
	snake_case_          =	[
	    " UN Chief Says There Is No Military Solution in Syria",
	    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
	]
	snake_case_          =	[
	    "Şeful ONU declară că nu există o soluţie militară în Siria",
	    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
	    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
	    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
	]
	snake_case_          =	[8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]






	@classmethod
	def 							UpperCamelCase_    (			cls   :					Optional[int]					):
					__A       =					MBartTokenizer.from_pretrained(
					    cls.checkpoint_name				,src_lang="en_XX"				,tgt_lang="ro_RO"					)
					__A       =					1
					return cls






	def 							UpperCamelCase_    (			self   :					Any					):
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]				,25_00_01					)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]				,25_00_04					)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]				,25_00_20					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
					self.assertListEqual(self.expected_src_tokens				,A					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					self.assertIn(A				,self.tokenizer.all_special_ids					)
					__A       =					[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
					__A       =					self.tokenizer.decode(A				,skip_special_tokens=A					)
					__A       =					self.tokenizer.decode(generated_ids[1:]				,skip_special_tokens=A					)
					self.assertEqual(A				,A					)
					self.assertNotIn(self.tokenizer.eos_token				,A					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					["this is gunna be a long sentence " * 20]
					assert isinstance(src_text[0]				,A					)
					__A       =					10
					__A       =					self.tokenizer(A				,max_length=A				,truncation=A					).input_ids[0]
					self.assertEqual(ids[-2]				,2					)
					self.assertEqual(ids[-1]				,A					)
					self.assertEqual(len(A					)				,A					)






	def 							UpperCamelCase_    (			self   :					Any					):
					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]					)				,[25_00_26, 25_00_01]					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					tempfile.mkdtemp()
					__A       =					self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(A					)
					__A       =					MBartTokenizer.from_pretrained(A					)
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,A					)






	@require_torch
	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.tokenizer(self.src_text				,text_target=self.tgt_text				,padding=A				,return_tensors="pt"					)
					__A       =					shift_tokens_right(batch["labels"]				,self.tokenizer.pad_token_id					)

					# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
					assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
					assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
					assert batch.decoder_input_ids[1][-1] == 2
					assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






	@require_torch
	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.tokenizer(
					    self.src_text				,text_target=self.tgt_text				,padding=A				,truncation=A				,max_length=len(self.expected_src_tokens					)				,return_tensors="pt"				,)

					__A       =					shift_tokens_right(batch["labels"]				,self.tokenizer.pad_token_id					)

					self.assertIsInstance(A				,A					)

					self.assertEqual((2, 14)				,batch.input_ids.shape					)
					self.assertEqual((2, 14)				,batch.attention_mask.shape					)
					__A       =					batch.input_ids.tolist()[0]
					self.assertListEqual(self.expected_src_tokens				,A					)
					self.assertEqual(2				,batch.decoder_input_ids[0, -1]					)  # EOS
					# Test that special tokens are reset
					self.assertEqual(self.tokenizer.prefix_tokens				,[]					)
					self.assertEqual(self.tokenizer.suffix_tokens				,[self.tokenizer.eos_token_id, EN_CODE]					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self.tokenizer(self.src_text				,padding=A				,truncation=A				,max_length=3				,return_tensors="pt"					)
					__A       =					self.tokenizer(
					    text_target=self.tgt_text				,padding=A				,truncation=A				,max_length=10				,return_tensors="pt"					)
					__A       =					targets["input_ids"]
					__A       =					shift_tokens_right(A				,self.tokenizer.pad_token_id					)

					self.assertEqual(batch.input_ids.shape[1]				,3					)
					self.assertEqual(batch.decoder_input_ids.shape[1]				,10					)







	@require_torch
	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					self.tokenizer._build_translation_inputs(
					    "A test"				,return_tensors="pt"				,src_lang="en_XX"				,tgt_lang="ar_AR"					)

					self.assertEqual(
					    nested_simplify(A					)				,{
					        # A, test, EOS, en_XX
					        "input_ids": [[62, 30_34, 2, 25_00_04]],
					        "attention_mask": [[1, 1, 1, 1]],
					        # ar_AR
					        "forced_bos_token_id": 25_00_01,
					    }				,)




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
      import torch


snake_case__				:	int					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				int		,				__a				:				Optional[Any]=False		,				__a				:				Tuple=False		,				__a				:				Tuple=6.0		,				__a				:				str=None		,				__a				:				List[str]=False		,				__a				:				str=False		,				__a				:				Optional[Any]=None		,				__a				:				Dict="fp4"		,				__a				:				Optional[int]=False		,				**__a				:				Dict		,				)					->str:
          lowerCamelCase_						:   str		= load_in_abit
          lowerCamelCase_						:   Union[str, Any]		= load_in_abit
          lowerCamelCase_						:   Tuple		= llm_inta_threshold
          lowerCamelCase_						:   int		= llm_inta_skip_modules
          lowerCamelCase_						:   List[str]		= llm_inta_enable_fpaa_cpu_offload
          lowerCamelCase_						:   Optional[Any]		= llm_inta_has_fpaa_weight
          lowerCamelCase_						:   Dict		= bnb_abit_quant_type
          lowerCamelCase_						:   int		= bnb_abit_use_double_quant

          if bnb_abit_compute_dtype is None:
             lowerCamelCase_						:   int		= torch.floataa
          elif isinstance(__a		,				__a							):
             lowerCamelCase_						:   Tuple		= getattr(__a		,				__a							)
          elif isinstance(__a		,				torch.dtype							):
             lowerCamelCase_						:   List[str]		= bnb_abit_compute_dtype
          else:
             raise ValueError("""bnb_4bit_compute_dtype must be a string or a torch.dtype"""							)

          self.post_init()



       def 				_lowerCAmelCase							(						self				:				Any							)					->Union[str, Any]:
          if not isinstance(self.llm_inta_threshold		,				__a							):
             raise ValueError("""llm_int8_threshold must be a float"""							)

          if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules		,				__a							):
             raise ValueError("""llm_int8_skip_modules must be a list of strings"""							)
          if not isinstance(self.llm_inta_enable_fpaa_cpu_offload		,				__a							):
             raise ValueError("""llm_int8_enable_fp32_cpu_offload must be a boolean"""							)

          if not isinstance(self.llm_inta_has_fpaa_weight		,				__a							):
             raise ValueError("""llm_int8_has_fp16_weight must be a boolean"""							)

          if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype		,				torch.dtype							):
             raise ValueError("""bnb_4bit_compute_dtype must be torch.dtype"""							)

          if not isinstance(self.bnb_abit_quant_type		,				__a							):
             raise ValueError("""bnb_4bit_quant_type must be a string"""							)

          if not isinstance(self.bnb_abit_use_double_quant		,				__a							):
             raise ValueError("""bnb_4bit_use_double_quant must be a boolean"""							)

          if self.load_in_abit and not version.parse(importlib.metadata.version("""bitsandbytes"""							)							) >= version.parse(
              """0.39.0"""							):
             raise ValueError(
                 """4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"""							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->int:
          return self.load_in_abit or self.load_in_abit



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->List[Any]:
          if self.load_in_abit:
             return "llm_int8"
          elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
             return "fp4"
          elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
             return "nf4"
          else:
             return None



       @classmethod
       def 				_lowerCAmelCase							(						cls				:				int		,				__a				:				Dict		,				__a				:				str		,				**__a				:				Any							)					->Optional[Any]:
          lowerCamelCase_						:   Optional[Any]		= cls(**__a							)

          lowerCamelCase_						:   List[Any]		= []
          for key, value in kwargs.items():
             if hasattr(__a		,				__a							):
                setattr(__a		,				__a		,				__a							)
                to_remove.append(__a							)
          for key in to_remove:
             kwargs.pop(__a		,				__a							)

          if return_unused_kwargs:
             return config, kwargs
          else:
             return config



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Union[str, os.PathLike]							)					->Any:
          with open(__a		,				"""w"""		,				encoding="""utf-8"""							) as writer:
             lowerCamelCase_						:   List[Any]		= self.to_dict()
             lowerCamelCase_						:   Dict		= json.dumps(__a		,				indent=2		,				sort_keys=__a							) + """\n"""

             writer.write(__a							)



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Dict[str, Any]:
          lowerCamelCase_						:   Dict		= copy.deepcopy(self.__dict__							)
          lowerCamelCase_						:   Tuple		= str(output["""bnb_4bit_compute_dtype"""]							).split("""."""							)[1]

          return output



       def __repr__(						self				:				List[str]							)					->Any:
          return F'''{self.__class__.__name__} {self.to_json_string()}'''



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				bool = True							)					->str:
          if use_diff is True:
             lowerCamelCase_						:   Dict		= self.to_diff_dict()
          else:
             lowerCamelCase_						:   Any		= self.to_dict()
          return json.dumps(__a		,				indent=2		,				sort_keys=__a							) + "\n"




       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Dict[str, Any]:
          lowerCamelCase_						:   Any		= self.to_dict()

          # get the default config dict
          lowerCamelCase_						:   List[str]		= BitsAndBytesConfig().to_dict()

          lowerCamelCase_						:   str		= {}

          # only serialize values that differ from the default config
          for key, value in config_dict.items():
             if value != default_config_dict[key]:
                lowerCamelCase_						:   List[Any]		= value

          return serializable_config_dict





