import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	lowerCamelCase_     (   UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						List[Any]						=      None
    a__	:						int						=      BloomTokenizerFast
    a__	:						Any						=      BloomTokenizerFast
    a__	:						Optional[Any]						=      True
    a__	:						Optional[int]						=      False
    a__	:						Union[str, Any]						=      """tokenizer_file"""
    a__	:						Dict						=      {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        super().setUp()
        __UpperCamelCase     :int			  =		BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''')
        tokenizer.save_pretrained(self.tmpdirname)







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	Union[str, Any]:
        kwargs.update(self.special_tokens_map)
        return BloomTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Union[str, Any]			  =		self.get_rust_tokenizer()

        __UpperCamelCase     :str			  =		['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
        __UpperCamelCase     :Optional[int]			  =		[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

        __UpperCamelCase     :Dict			  =		tokenizer.batch_encode_plus(__lowercase)['''input_ids''']
        self.assertListEqual(__lowercase , __lowercase)

        __UpperCamelCase     :Dict			  =		tokenizer.batch_decode(__lowercase)
        self.assertListEqual(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self , __lowercase=6)       ->	Any:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                __UpperCamelCase     :int			  =		self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase)
                # tokenizer_r.pad_token = None # Hotfixing padding = None
                # Simple input
                __UpperCamelCase     :str			  =		'''This is a simple input'''
                __UpperCamelCase     :Optional[int]			  =		['''This is a simple input 1''', '''This is a simple input 2''']
                __UpperCamelCase     :Any			  =		('''This is a simple input''', '''This is a pair''')
                __UpperCamelCase     :Optional[Any]			  =		[
                    ('''This is a simple input 1''', '''This is a simple input 2'''),
                    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                ]

                # Simple input tests
                try:
                    tokenizer_r.encode(__lowercase , max_length=__lowercase)
                    tokenizer_r.encode_plus(__lowercase , max_length=__lowercase)

                    tokenizer_r.batch_encode_plus(__lowercase , max_length=__lowercase)
                    tokenizer_r.encode(__lowercase , max_length=__lowercase)
                    tokenizer_r.batch_encode_plus(__lowercase , max_length=__lowercase)
                except ValueError:
                    self.fail('''Bloom Tokenizer should be able to deal with padding''')

                __UpperCamelCase     :str			  =		None  # Hotfixing padding = None
                self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding='''max_length''')

                # Simple input
                self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''')

                # Simple input
                self.assertRaises(
                    __lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''' , )

                # Pair input
                self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding='''max_length''')

                # Pair input
                self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''')

                # Pair input
                self.assertRaises(
                    __lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :int			  =		self.get_rust_tokenizer()
        __UpperCamelCase     :List[str]			  =		load_dataset('''xnli''' , '''all_languages''' , split='''test''' , streaming=__lowercase)

        __UpperCamelCase     :Optional[int]			  =		next(iter(__lowercase))['''premise''']  # pick up one data
        __UpperCamelCase     :Union[str, Any]			  =		list(sample_data.values())

        __UpperCamelCase     :Optional[int]			  =		list(map(tokenizer.encode , __lowercase))
        __UpperCamelCase     :Union[str, Any]			  =		[tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase) for x in output_tokens]
        self.assertListEqual(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
        # any sequence length constraints. This test of the parent class will fail since it relies on the
        # maximum sequence length of the positoonal embeddings.
        self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map) , 1)
        self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]) , 1)






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Union[str, Any]						=      """wav2vec2"""







    def __init__(	self , __lowercase=32 , __lowercase=768 , __lowercase=12 , __lowercase=12 , __lowercase=3_072 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.02 , __lowercase=1E-5 , __lowercase="group" , __lowercase="gelu" , __lowercase=(512, 512, 512, 512, 512, 512, 512) , __lowercase=(5, 2, 2, 2, 2, 2, 2) , __lowercase=(10, 3, 3, 3, 3, 2, 2) , __lowercase=False , __lowercase=128 , __lowercase=16 , __lowercase=False , __lowercase=True , __lowercase=0.05 , __lowercase=10 , __lowercase=2 , __lowercase=0.0 , __lowercase=10 , __lowercase=0 , __lowercase=320 , __lowercase=2 , __lowercase=0.1 , __lowercase=100 , __lowercase=256 , __lowercase=256 , __lowercase=0.1 , __lowercase="sum" , __lowercase=False , __lowercase=False , __lowercase=256 , __lowercase=(512, 512, 512, 512, 1_500) , __lowercase=(5, 3, 3, 1, 1) , __lowercase=(1, 2, 3, 1, 1) , __lowercase=512 , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=3 , __lowercase=2 , __lowercase=3 , __lowercase=None , __lowercase=None , **__lowercase , )       ->	int:
        super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase)
        __UpperCamelCase     :Any			  =		hidden_size
        __UpperCamelCase     :int			  =		feat_extract_norm
        __UpperCamelCase     :Tuple			  =		feat_extract_activation
        __UpperCamelCase     :Union[str, Any]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :int			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		conv_bias
        __UpperCamelCase     :Optional[int]			  =		num_conv_pos_embeddings
        __UpperCamelCase     :Dict			  =		num_conv_pos_embedding_groups
        __UpperCamelCase     :Any			  =		len(self.conv_dim)
        __UpperCamelCase     :List[str]			  =		num_hidden_layers
        __UpperCamelCase     :int			  =		intermediate_size
        __UpperCamelCase     :str			  =		hidden_act
        __UpperCamelCase     :Any			  =		num_attention_heads
        __UpperCamelCase     :int			  =		hidden_dropout
        __UpperCamelCase     :Tuple			  =		attention_dropout
        __UpperCamelCase     :List[str]			  =		activation_dropout
        __UpperCamelCase     :Optional[Any]			  =		feat_proj_dropout
        __UpperCamelCase     :Any			  =		final_dropout
        __UpperCamelCase     :Any			  =		layerdrop
        __UpperCamelCase     :str			  =		layer_norm_eps
        __UpperCamelCase     :Optional[Any]			  =		initializer_range
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :str			  =		do_stable_layer_norm
        __UpperCamelCase     :Union[str, Any]			  =		use_weighted_layer_sum

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase     :List[Any]			  =		apply_spec_augment
        __UpperCamelCase     :Tuple			  =		mask_time_prob
        __UpperCamelCase     :int			  =		mask_time_length
        __UpperCamelCase     :Dict			  =		mask_time_min_masks
        __UpperCamelCase     :str			  =		mask_feature_prob
        __UpperCamelCase     :List[str]			  =		mask_feature_length
        __UpperCamelCase     :Union[str, Any]			  =		mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase     :Optional[Any]			  =		num_codevectors_per_group
        __UpperCamelCase     :List[Any]			  =		num_codevector_groups
        __UpperCamelCase     :Tuple			  =		contrastive_logits_temperature
        __UpperCamelCase     :Optional[int]			  =		feat_quantizer_dropout
        __UpperCamelCase     :Optional[int]			  =		num_negatives
        __UpperCamelCase     :List[Any]			  =		codevector_dim
        __UpperCamelCase     :str			  =		proj_codevector_dim
        __UpperCamelCase     :List[str]			  =		diversity_loss_weight

        # ctc loss
        __UpperCamelCase     :Tuple			  =		ctc_loss_reduction
        __UpperCamelCase     :Tuple			  =		ctc_zero_infinity

        # adapter
        __UpperCamelCase     :List[str]			  =		add_adapter
        __UpperCamelCase     :Tuple			  =		adapter_kernel_size
        __UpperCamelCase     :str			  =		adapter_stride
        __UpperCamelCase     :Tuple			  =		num_adapter_layers
        __UpperCamelCase     :Tuple			  =		output_hidden_size or hidden_size
        __UpperCamelCase     :Optional[Any]			  =		adapter_attn_dim

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase     :Optional[Any]			  =		classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase     :Optional[int]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :str			  =		xvector_output_dim







    @property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        return functools.reduce(operator.mul , self.conv_stride , 1)





