






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__						:Optional[int]          =			logging.get_logger(__name__)

UpperCAmelCase__						:Any          =			{
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}







class 	SCREAMING_SNAKE_CASE	(      lowercase__  ):
       snake_case__    :     str   		=			'xlm'
       snake_case__    :     Optional[Any]   		=			{
           'hidden_size': 'emb_dim',
           'num_attention_heads': 'n_heads',
           'num_hidden_layers': 'n_layers',
           'n_words': 'vocab_size',  # For backward compatibility
       }




       def __init__(					self      : Any	,    A__      : Optional[int]=30145	,    A__      : Any=2048	,    A__      : Dict=12	,    A__      : Optional[Any]=16	,    A__      : int=0.1	,    A__      : Optional[int]=0.1	,    A__      : Union[str, Any]=True	,    A__      : Optional[int]=False	,    A__      : Any=False	,    A__      : Optional[Any]=False	,    A__      : int=1	,    A__      : str=True	,    A__      : int=512	,    A__      : Optional[Any]=2048**-0.5	,    A__      : Tuple=1e-1_2	,    A__      : Optional[Any]=0.02	,    A__      : Tuple=0	,    A__      : Optional[Any]=1	,    A__      : Any=2	,    A__      : Dict=3	,    A__      : Any=5	,    A__      : List[Any]=True	,    A__      : List[Any]="first"	,    A__      : Dict=True	,    A__      : Any=None	,    A__      : int=True	,    A__      : Any=0.1	,    A__      : Tuple=5	,    A__      : Any=5	,    A__      : Union[str, Any]=0	,    A__      : int=0	,    A__      : List[Any]=2	,    A__      : Optional[Any]=0	,    **A__      : List[Any]	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				vocab_size
            __lowerCamelCase						:       Optional[Any]				   =				emb_dim
            __lowerCamelCase						:       Dict				   =				n_layers
            __lowerCamelCase						:       List[Any]				   =				n_heads
            __lowerCamelCase						:       Tuple				   =				dropout
            __lowerCamelCase						:       List[str]				   =				attention_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				gelu_activation
            __lowerCamelCase						:       Optional[int]				   =				sinusoidal_embeddings
            __lowerCamelCase						:       Any				   =				causal
            __lowerCamelCase						:       Tuple				   =				asm
            __lowerCamelCase						:       Union[str, Any]				   =				n_langs
            __lowerCamelCase						:       str				   =				use_lang_emb
            __lowerCamelCase						:       int				   =				layer_norm_eps
            __lowerCamelCase						:       str				   =				bos_index
            __lowerCamelCase						:       int				   =				eos_index
            __lowerCamelCase						:       Dict				   =				pad_index
            __lowerCamelCase						:       Tuple				   =				unk_index
            __lowerCamelCase						:       List[str]				   =				mask_index
            __lowerCamelCase						:       str				   =				is_encoder
            __lowerCamelCase						:       int				   =				max_position_embeddings
            __lowerCamelCase						:       str				   =				embed_init_std
            __lowerCamelCase						:       Tuple				   =				init_std
            __lowerCamelCase						:       Tuple				   =				summary_type
            __lowerCamelCase						:       Tuple				   =				summary_use_proj
            __lowerCamelCase						:       Optional[Any]				   =				summary_activation
            __lowerCamelCase						:       Any				   =				summary_proj_to_labels
            __lowerCamelCase						:       str				   =				summary_first_dropout
            __lowerCamelCase						:       Optional[int]				   =				start_n_top
            __lowerCamelCase						:       Union[str, Any]				   =				end_n_top
            __lowerCamelCase						:       int				   =				mask_token_id
            __lowerCamelCase						:       Any				   =				lang_id

            if "n_words" in kwargs:
                 __lowerCamelCase						:       Tuple				   =				kwargs["n_words"]

            super().__init__(pad_token_id=A__	,    bos_token_id=A__	,    **A__					)







class 	SCREAMING_SNAKE_CASE	(      lowercase__  ):




       @property
       def 					a_					(					self      : Any					):


            """simple docstring"""







            if self.task == "multiple-choice":
                 __lowerCamelCase						:       str				   =				{0: "batch", 1: "choice", 2: "sequence"}
            else:
                 __lowerCamelCase						:       Optional[int]				   =				{0: "batch", 1: "sequence"}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""token_type_ids""", dynamic_axis),
                ]					)











'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				"""laion/clap-htsat-unfused"""
            __lowerCamelCase						:       List[Any]				   =				tempfile.mkdtemp()




       def 					a_					(					self      : Optional[int]	,    **A__      : Union[str, Any]					):


            """simple docstring"""







            return RobertaTokenizer.from_pretrained(self.checkpoint	,    **A__					)




       def 					a_					(					self      : str	,    **A__      : Any					):


            """simple docstring"""







            return ClapFeatureExtractor.from_pretrained(self.checkpoint	,    **A__					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            shutil.rmtree(self.tmpdirname					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.get_tokenizer()
            __lowerCamelCase						:       Union[str, Any]				   =				self.get_feature_extractor()

            __lowerCamelCase						:       Dict				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            processor.save_pretrained(self.tmpdirname					)
            __lowerCamelCase						:       Optional[Any]				   =				ClapProcessor.from_pretrained(self.tmpdirname					)

            self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()					)
            self.assertIsInstance(processor.tokenizer	,    A__					)

            self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()					)
            self.assertIsInstance(processor.feature_extractor	,    A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				ClapProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()					)
            processor.save_pretrained(self.tmpdirname					)

            __lowerCamelCase						:       Tuple				   =				self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""					)
            __lowerCamelCase						:       Any				   =				self.get_feature_extractor(do_normalize=A__	,    padding_value=1.0					)

            __lowerCamelCase						:       List[Any]				   =				ClapProcessor.from_pretrained(
                self.tmpdirname	,    bos_token="""(BOS)"""	,    eos_token="""(EOS)"""	,    do_normalize=A__	,    padding_value=1.0					)

            self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()					)
            self.assertIsInstance(processor.tokenizer	,    A__					)

            self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()					)
            self.assertIsInstance(processor.feature_extractor	,    A__					)




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.get_feature_extractor()
            __lowerCamelCase						:       Tuple				   =				self.get_tokenizer()

            __lowerCamelCase						:       Optional[int]				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       Dict				   =				floats_list((3, 1000)					)

            __lowerCamelCase						:       str				   =				feature_extractor(A__	,    return_tensors="""np"""					)
            __lowerCamelCase						:       Any				   =				processor(audios=A__	,    return_tensors="""np"""					)

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1e-2					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_feature_extractor()
            __lowerCamelCase						:       int				   =				self.get_tokenizer()

            __lowerCamelCase						:       str				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       int				   =				"""This is a test string"""

            __lowerCamelCase						:       Any				   =				processor(text=A__					)

            __lowerCamelCase						:       Any				   =				tokenizer(A__					)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]					)




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.get_feature_extractor()
            __lowerCamelCase						:       List[str]				   =				self.get_tokenizer()

            __lowerCamelCase						:       int				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       Union[str, Any]				   =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __lowerCamelCase						:       int				   =				processor.batch_decode(A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.batch_decode(A__					)

            self.assertListEqual(A__	,    A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.get_feature_extractor()
            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()

            __lowerCamelCase						:       Any				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            self.assertListEqual(
                processor.model_input_names[2:]	,    feature_extractor.model_input_names	,    msg="""`processor` and `feature_extractor` model input names do not match"""	,    )



