





"""simple docstring"""


import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
a__   :       Tuple   						=			"""stas/tiny-wmt19-en-ru"""



class        lowercase_			(       _UpperCAmelCase			,unittest.TestCase							):
       snake_case__			:							str      							=       FSMTTokenizer
       snake_case__			:							Optional[int]      							=       False

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        snake_case__      							=			[
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "w</w>",
            "r</w>",
            "t</w>",
            "lo",
            "low",
            "er</w>",
            "low</w>",
            "lowest</w>",
            "newer</w>",
            "wider</w>",
            "<unk>",
        ]
        snake_case__      							=			dict(zip(lowerCAmelCase__				,   range(len(lowerCAmelCase__       )       )       )       )
        snake_case__      							=			["l o 123", "lo w 1456", "e r</w> 1789", ""]

        snake_case__      							=			["en", "ru"]
        snake_case__      							=			{
            "langs": self.langs,
            "src_vocab_size": 10,
            "tgt_vocab_size": 20,
        }

        snake_case__      							=			os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["src_vocab_file"]       )
        snake_case__      							=			os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["tgt_vocab_file"]       )
        snake_case__      							=			os.path.join(self.tmpdirname				,   "tokenizer_config.json"       )
        snake_case__      							=			os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["merges_file"]       )
        with open(self.src_vocab_file				,   "w"       ) as fp:
         fp.write(json.dumps(lowerCAmelCase__       )       )
        with open(self.tgt_vocab_file				,   "w"       ) as fp:
         fp.write(json.dumps(lowerCAmelCase__       )       )
        with open(self.merges_file				,   "w"       ) as fp:
         fp.write("\n".join(lowerCAmelCase__       )       )
        with open(lowerCAmelCase__				,   "w"       ) as fp:
         fp.write(json.dumps(lowerCAmelCase__       )       )

       @cached_property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"       )

       @cached_property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			FSMTTokenizer.from_pretrained(lowerCAmelCase__       )
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]				,   ["en", "ru"]       )
        self.assertEqual(tokenizer.src_vocab_size				,   21       )
        self.assertEqual(tokenizer.tgt_vocab_size				,   21       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			FSMTTokenizer(self.langs				,   self.src_vocab_file				,   self.tgt_vocab_file				,   self.merges_file       )

        snake_case__      							=			"lower"
        snake_case__      							=			["low", "er</w>"]
        snake_case__      							=			tokenizer.tokenize(lowerCAmelCase__       )
        self.assertListEqual(lowerCAmelCase__				,   lowerCAmelCase__       )

        snake_case__      							=			tokens + ["<unk>"]
        snake_case__      							=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )				,   lowerCAmelCase__       )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer_ru_en

        snake_case__      							=			tokenizer.encode("sequence builders"				,   add_special_tokens=lowerCAmelCase__       )
        snake_case__      							=			tokenizer.encode("multi-sequence build"				,   add_special_tokens=lowerCAmelCase__       )

        snake_case__      							=			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__       )
        snake_case__      							=			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__				,   lowerCAmelCase__       )

        assert encoded_sentence == text + [2]
        assert encoded_pair == text + [2] + text_a + [2]

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer_en_ru
        snake_case__      							=			self.tokenizer_ru_en

        snake_case__      							=			[
            [
                "Here\'s a little song I wrote. Don\'t worry, be happy.",
                [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
            ],
            ["This is it. No more. I\'m done!", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
        ]

        # if data needs to be recreated or added, run:
        # import torch
        # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
        # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

        for src_text, tgt_input_ids in targets:
         snake_case__      							=			tokenizer_enc.encode(lowerCAmelCase__				,   return_tensors=lowerCAmelCase__       )
         self.assertListEqual(lowerCAmelCase__				,   lowerCAmelCase__       )

         # and decode backward, using the reversed languages model
         snake_case__      							=			tokenizer_dec.decode(lowerCAmelCase__				,   skip_special_tokens=lowerCAmelCase__       )
         self.assertEqual(lowerCAmelCase__				,   lowerCAmelCase__       )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"				,   do_lower_case=lowerCAmelCase__       )
        snake_case__      							=			tokenizer.tokenize("USA is United States of America"       )
        snake_case__      							=			["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
        self.assertListEqual(lowerCAmelCase__				,   lowerCAmelCase__       )

       @unittest.skip("FSMTConfig.__init__  requires non-optional args"       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        pass

       @unittest.skip("FSMTConfig.__init__  requires non-optional args"       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        pass








"""simple docstring"""


import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


a__   :       str   						=			logging.get_logger(__name__)



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Dict										=       """semantic"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=768				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_0048				,   __SCREAMING_SNAKE_CASE=12_9595				,   __SCREAMING_SNAKE_CASE=12_9599				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE=49.9				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   max_new_tokens=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			text_encoding_offset
        snake_case__      							=			text_pad_token
        snake_case__      							=			eos_token_id
        snake_case__      							=			semantic_infer_token
        snake_case__      							=			semantic_vocab_size
        snake_case__      							=			max_input_semantic_length
        snake_case__      							=			semantic_rate_hz



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """coarse_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_2048				,   __SCREAMING_SNAKE_CASE=75				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=1_2050				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE = 630				,   __SCREAMING_SNAKE_CASE = 60				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			coarse_semantic_pad_token
        snake_case__      							=			coarse_rate_hz
        snake_case__      							=			n_coarse_codebooks
        snake_case__      							=			coarse_infer_token
        snake_case__      							=			max_coarse_input_length
        snake_case__      							=			max_coarse_history
        snake_case__      							=			sliding_window_len



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """fine_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=0.5				,   __SCREAMING_SNAKE_CASE=512				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=8				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(temperature=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			max_fine_history_length
        snake_case__      							=			max_fine_input_length
        snake_case__      							=			n_fine_codebooks



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """bark"""
       snake_case__			:							Dict										=       True

       def __init__(     self				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE=2_4000				,   __SCREAMING_SNAKE_CASE=1024				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        if semantic_config is None:
         snake_case__      							=			{}
         logger.info("semantic_config is None. initializing the semantic model with default values."       )

        if coarse_acoustics_config is None:
         snake_case__      							=			{}
         logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."       )

        if fine_acoustics_config is None:
         snake_case__      							=			{}
         logger.info("fine_acoustics_config is None. initializing the fine model with default values."       )

        snake_case__      							=			BarkSemanticGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			sample_rate
        snake_case__      							=			codebook_size

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(     cls				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        return cls(
            semantic_config=semantic_config.to_dict()				,   coarse_acoustics_config=coarse_acoustics_config.to_dict()				,   fine_acoustics_config=fine_acoustics_config.to_dict()				,   **__SCREAMING_SNAKE_CASE				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )

        snake_case__      							=			self.semantic_config.to_dict()
        snake_case__      							=			self.coarse_acoustics_config.to_dict()
        snake_case__      							=			self.fine_acoustics_config.to_dict()

        snake_case__      							=			self.__class__.model_type
        return output

