"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


lowerCAmelCase_:       Optional[int]			     =       random.Random()







def        __a (		A     ,					A=1.0     ,					A=None     ,					A=None   ):






     '''simple docstring'''



     if rng is None:
          lowercase__								=				global_rng

     lowercase__								=				[]
     for batch_idx in range(shape[0]   ):
          values.append([]   )
          for _ in range(shape[1]   ):
               values[-1].append(rng.random() * scale   )

     return values






@require_torch
class  a__     ( unittest.TestCase						):


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=7,					_UpperCAmelCase=400,					_UpperCAmelCase=2000,					_UpperCAmelCase=1,					_UpperCAmelCase=0.0,					_UpperCAmelCase=2_4000,					_UpperCAmelCase=True,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				min_seq_length
        lowercase__								=				max_seq_length
        lowercase__								=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowercase__								=				feature_size
        lowercase__								=				padding_value
        lowercase__								=				sampling_rate
        lowercase__								=				return_attention_mask


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
        }


   def 						snake_case__						(  self,					_UpperCAmelCase=False,					_UpperCAmelCase=False	):
        '''simple docstring'''



        def _flatten(_UpperCAmelCase	):
             return list(itertools.chain(*_UpperCAmelCase	)	)

        if equal_length:
             lowercase__								=				floats_list((self.batch_size, self.max_seq_length)	)
        else:
             # make sure that inputs increase in size
             lowercase__								=				[
                 _flatten(floats_list((x, self.feature_size)	)	)
                 for x in range(self.min_seq_length,					self.max_seq_length,					self.seq_length_diff	)
             ]

        if numpify:
             lowercase__								=				[np.asarray(_UpperCAmelCase	) for x in audio_inputs]

        return audio_inputs






@require_torch
class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   EncodecFeatureExtractor


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				EnCodecFeatureExtractionTester(self	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
        # create three inputs of length 800, 1000, and 1200
        lowercase__								=				[floats_list((1, x)	)[0] for x in range(800,					1400,					200	)]
        lowercase__								=				[np.asarray(_UpperCAmelCase	) for audio_input in audio_inputs]

        # Test not batched input
        lowercase__								=				feat_extract(audio_inputs[0],					return_tensors="np"	).input_values
        lowercase__								=				feat_extract(np_audio_inputs[0],					return_tensors="np"	).input_values
        self.assertTrue(np.allclose(_UpperCAmelCase,					_UpperCAmelCase,					atol=1E-3	)	)

        # Test batched
        lowercase__								=				feat_extract(_UpperCAmelCase,					padding=_UpperCAmelCase,					return_tensors="np"	).input_values
        lowercase__								=				feat_extract(_UpperCAmelCase,					padding=_UpperCAmelCase,					return_tensors="np"	).input_values
        for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase,					_UpperCAmelCase	):
             self.assertTrue(np.allclose(_UpperCAmelCase,					_UpperCAmelCase,					atol=1E-3	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
        lowercase__								=				np.random.rand(100	).astype(np.floataa	)
        lowercase__								=				np_audio_inputs.tolist()

        for inputs in [py_audio_inputs, np_audio_inputs]:
             lowercase__								=				feature_extractor.pad([{"input_values": inputs}],					return_tensors="np"	)
             self.assertTrue(np_processed.input_values.dtype == np.floataa	)
             lowercase__								=				feature_extractor.pad([{"input_values": inputs}],					return_tensors="pt"	)
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        from datasets import load_dataset

        lowercase__								=				load_dataset("hf-internal-testing/librispeech_asr_dummy",					"clean",					split="validation"	)
        # automatic decoding with librispeech
        lowercase__								=				ds.sort("id"	).select(range(_UpperCAmelCase	)	)[:num_samples]["audio"]

        return [x["array"] for x in audio_samples]


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				torch.tensor(
            [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
             3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
             2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
             4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
             7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
             4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
        # fmt: on
        lowercase__								=				self._load_datasamples(1	)
        lowercase__								=				EncodecFeatureExtractor()
        lowercase__								=				feature_extractor(_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(input_values.shape,					(1, 1, 9_3680)	)
        self.assertTrue(torch.allclose(input_values[0, 0, :30],					_UpperCAmelCase,					atol=1E-6	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				torch.tensor(
            [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
             3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
             2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
             4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
             7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
             4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
        # fmt: on
        lowercase__								=				self._load_datasamples(1	)
        lowercase__								=				[np.tile(input_audio[0][None],					reps=(2, 1)	)]
        input_audio[0][1] *= 0.5
        lowercase__								=				EncodecFeatureExtractor(feature_size=2	)
        lowercase__								=				feature_extractor(_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(input_values.shape,					(1, 2, 9_3680)	)
        self.assertTrue(torch.allclose(input_values[0, 0, :30],					_UpperCAmelCase,					atol=1E-6	)	)
        self.assertTrue(torch.allclose(input_values[0, 1, :30],					EXPECTED_INPUT_VALUES * 0.5,					atol=1E-6	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self._load_datasamples(2	)
        # would be easier if the stride was like
        lowercase__								=				EncodecFeatureExtractor(feature_size=1,					chunk_length_s=1,					overlap=0.01	)

        # pad and trunc raise an error ?
        with self.assertRaisesRegex(
            _UpperCAmelCase,					"^Both padding and truncation were set. Make sure you only set one.$",					):
             lowercase__								=				feature_extractor(
                 _UpperCAmelCase,					padding="max_length",					truncation=_UpperCAmelCase,					return_tensors="pt"	).input_values

        # truncate to chunk
        lowercase__								=				feature_extractor(_UpperCAmelCase,					truncation=_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(truncated_outputs.shape,					(2, 1, 7_1520)	)  # 2 chunks

        # force truncate to max_length
        lowercase__								=				feature_extractor(
            _UpperCAmelCase,					truncation=_UpperCAmelCase,					max_length=4_8000,					return_tensors="pt"	).input_values
        self.assertEquals(truncated_outputs.shape,					(2, 1, 4_8000)	)

        # pad to chunk
        lowercase__								=				feature_extractor(_UpperCAmelCase,					padding=_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(padded_outputs.shape,					(2, 1, 9_5280)	)

        # pad to chunk
        lowercase__								=				feature_extractor(_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(truncated_outputs.shape,					(2, 1, 9_5280)	)

        # force pad to max length
        lowercase__								=				feature_extractor(
            _UpperCAmelCase,					padding="max_length",					max_length=10_0000,					return_tensors="pt"	).input_values
        self.assertEquals(truncated_outputs.shape,					(2, 1, 10_0000)	)

        # force no pad
        with self.assertRaisesRegex(
            _UpperCAmelCase,					"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",					):
             lowercase__								=				feature_extractor(_UpperCAmelCase,					padding=_UpperCAmelCase,					return_tensors="pt"	).input_values

        lowercase__								=				feature_extractor(input_audio[0],					padding=_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(truncated_outputs.shape,					(1, 1, 9_3680)	)

        # no pad if no chunk_length_s
        lowercase__								=				None
        with self.assertRaisesRegex(
            _UpperCAmelCase,					"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",					):
             lowercase__								=				feature_extractor(_UpperCAmelCase,					padding=_UpperCAmelCase,					return_tensors="pt"	).input_values

        lowercase__								=				feature_extractor(input_audio[0],					padding=_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(truncated_outputs.shape,					(1, 1, 9_3680)	)

        # no pad if no overlap
        lowercase__								=				2
        lowercase__								=				None
        with self.assertRaisesRegex(
            _UpperCAmelCase,					"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",					):
             lowercase__								=				feature_extractor(_UpperCAmelCase,					padding=_UpperCAmelCase,					return_tensors="pt"	).input_values

        lowercase__								=				feature_extractor(input_audio[0],					padding=_UpperCAmelCase,					return_tensors="pt"	).input_values
        self.assertEquals(truncated_outputs.shape,					(1, 1, 9_3680)	)





"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

lowerCAmelCase_:       Optional[Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Optional[int]			     =       {"vocab_file": "tokenizer.model"}

lowerCAmelCase_:       Optional[int]			     =       {
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
lowerCAmelCase_:       Optional[int]			     =       {
    "hf-internal-testing/llama-tokenizer": 2_0_4_8,
}
lowerCAmelCase_:       str			     =       "▁"

lowerCAmelCase_     ,				lowerCAmelCase_:       str			     =       "[INST]", "[/INST]"
lowerCAmelCase_     ,				lowerCAmelCase_:       Dict			     =       "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowerCAmelCase_:       List[Any]			     =       "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."






class  a__     ( _a						):
   snake_case_				=   VOCAB_FILES_NAMES
   snake_case_				=   PRETRAINED_VOCAB_FILES_MAP
   snake_case_				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_				=   ["input_ids", "attention_mask"]


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase="<unk>",					_UpperCAmelCase="<s>",					_UpperCAmelCase="</s>",					_UpperCAmelCase=None,					_UpperCAmelCase = None,					_UpperCAmelCase=True,					_UpperCAmelCase=False,					_UpperCAmelCase=False,					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				{} if sp_model_kwargs is None else sp_model_kwargs
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else bos_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else eos_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else unk_token
        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else pad_token
        super().__init__(
            bos_token=_UpperCAmelCase,					eos_token=_UpperCAmelCase,					unk_token=_UpperCAmelCase,					pad_token=_UpperCAmelCase,					add_bos_token=_UpperCAmelCase,					add_eos_token=_UpperCAmelCase,					sp_model_kwargs=self.sp_model_kwargs,					clean_up_tokenization_spaces=_UpperCAmelCase,					legacy=_UpperCAmelCase,					**_UpperCAmelCase,					)
        if legacy:
             logger.warning_once(
                 F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                 " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"	)
        lowercase__								=				legacy
        lowercase__								=				vocab_file
        lowercase__								=				add_bos_token
        lowercase__								=				add_eos_token
        lowercase__								=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(_UpperCAmelCase	)


   def __getstate__(  self	):
        '''simple docstring'''



        lowercase__								=				self.__dict__.copy()
        lowercase__								=				None
        lowercase__								=				self.sp_model.serialized_model_proto()
        return state


   def __setstate__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				d
        lowercase__								=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.sp_model.get_piece_size()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				{self.convert_ids_to_tokens(_UpperCAmelCase	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab


   def 						snake_case__						(  self,					_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



        if not self.legacy:
             lowercase__								=				SPIECE_UNDERLINE + text.replace(_UpperCAmelCase,					" "	)
        return super().tokenize(_UpperCAmelCase,					**_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if not self.legacy:
             lowercase__								=				text.startswith(_UpperCAmelCase	)
             if is_first:
                  lowercase__								=				text[1:]

        lowercase__								=				self.sp_model.encode(_UpperCAmelCase,					out_type=_UpperCAmelCase	)

        if not self.legacy and not is_first and not text.startswith(" "	) and tokens[0].startswith(_UpperCAmelCase	):
             lowercase__								=				([tokens[0][1:]] if len(tokens[0]	) > 1 else []) + tokens[1:]
        return tokens


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        return self.sp_model.piece_to_id(_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.sp_model.IdToPiece(_UpperCAmelCase	)
        return token


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				[]
        lowercase__								=				""
        lowercase__								=				False
        for i, token in enumerate(_UpperCAmelCase	):
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  if not prev_is_special and i != 0:
                       out_string += " "
                  out_string += self.sp_model.decode(_UpperCAmelCase	) + token
                  lowercase__								=				True
                  lowercase__								=				[]
             else:
                  current_sub_tokens.append(_UpperCAmelCase	)
                  lowercase__								=				False
        out_string += self.sp_model.decode(_UpperCAmelCase	)
        return out_string


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



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

        if os.path.abspath(self.vocab_file	) != os.path.abspath(_UpperCAmelCase	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file,					_UpperCAmelCase	)
        elif not os.path.isfile(self.vocab_file	):
             with open(_UpperCAmelCase,					"wb"	) as fi:
                  lowercase__								=				self.sp_model.serialized_model_proto()
                  fi.write(_UpperCAmelCase	)

        return (out_vocab_file,)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase=None	):
        '''simple docstring'''



        lowercase__								=				[self.bos_token_id] if self.add_bos_token else []
        lowercase__								=				[self.eos_token_id] if self.add_eos_token else []

        lowercase__								=				bos_token_id + token_ids_a + eos_token_id

        if token_ids_a is not None:
             lowercase__								=				output + bos_token_id + token_ids_a + eos_token_id

        return output


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None,					_UpperCAmelCase = False	):
        '''simple docstring'''



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

        lowercase__								=				[1] if self.add_bos_token else []
        lowercase__								=				[1] if self.add_eos_token else []

        if token_ids_a is None:
             return bos_token_id + ([0] * len(_UpperCAmelCase	)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(_UpperCAmelCase	))
            + eos_token_id
            + bos_token_id
            + ([0] * len(_UpperCAmelCase	))
            + eos_token_id
        )


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



        lowercase__								=				[self.bos_token_id] if self.add_bos_token else []
        lowercase__								=				[self.eos_token_id] if self.add_eos_token else []

        lowercase__								=				[0] * len(bos_token_id + token_ids_a + eos_token_id	)

        if token_ids_a is not None:
             output += [1] * len(bos_token_id + token_ids_a + eos_token_id	)

        return output


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				list(conversation.iter_texts()	)
        if not all([is_user for is_user, msg in dialogue[::2]]	) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]	):
             raise ValueError(
                 "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"	)

        lowercase__								=				[]
        if len(conversation.past_user_inputs	) > 0:
             if not conversation.past_user_inputs[0].startswith(_UpperCAmelCase	) or E_SYS not in conversation.past_user_inputs[0]:
                  lowercase__								=				(
                      B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                  )
        elif not dialogue[0][1].startswith(_UpperCAmelCase	) or E_SYS not in dialogue[0][1]:
             lowercase__								=				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ''',					add_special_tokens=_UpperCAmelCase	)
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2],					dialogue[1::2]	)
            ],					[],					)
        if not (dialogue[-1][0]):
             raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}'''	)
        dialog_tokens += [self.bos_token_id] + self.encode(
            F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}''',					add_special_tokens=_UpperCAmelCase	)
        return dialog_tokens




