



import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_						=       logging.get_logger(__name__)


lowercase_						=       {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowercase_						=       {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

lowercase_						=       {"""facebook/blenderbot_small-90M""": 512}



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			set()
			lowercase__   					=			word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)					)
						lowercase__   					=			char

			lowercase__   					=			set(SCREAMING_SNAKE_CASE_					)
			return pairs




class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   List[Any]       						=VOCAB_FILES_NAMES
				UpperCamelCase__			:   Union[str, Any]       						=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__			:   Union[str, Any]       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__			:   List[str]       						=["""input_ids""", """attention_mask"""]




				def __init__(  self   :  int,   __lowercase   :  List[Any],   __lowercase   :  Any,   __lowercase   :  Any="__start__",   __lowercase   :  Any="__end__",   __lowercase   :  List[str]="__unk__",   __lowercase   :  List[Any]="__null__",   **__lowercase   :  List[str],   ):
							super().__init__(unk_token=__lowercase,   bos_token=__lowercase,   eos_token=__lowercase,   pad_token=__lowercase,   **__lowercase  )

							with open(__lowercase,   encoding="utf-8"  ) as vocab_handle:
										lowercase__   					=			json.load(__lowercase  )
							lowercase__   					=			{v: k for k, v in self.encoder.items()}
							with open(__lowercase,   encoding="utf-8"  ) as merges_handle:
										lowercase__   					=			merges_handle.read().split("\n"  )[1:-1]
							lowercase__   					=			[tuple(merge.split()  ) for merge in merges]
							lowercase__   					=			dict(zip(__lowercase,   range(len(__lowercase  )  )  )  )
							lowercase__   					=			{}




				@property
				def 							A__ (  self   :  Tuple  ):
							return len(self.encoder  )




				def 							A__ (  self   :  List[Any]  ):
							return dict(self.encoder,   **self.added_tokens_encoder  )




				def 							A__ (  self   :  Optional[int],   __lowercase   :  str  ):
							if token in self.cache:
										return self.cache[token]
							lowercase__   					=			re.sub("([.,!?()])",   R" \1",   __lowercase  )
							lowercase__   					=			re.sub("(')",   R" \1 ",   __lowercase  )
							lowercase__   					=			re.sub(R"\s{2,}",   " ",   __lowercase  )
							if "\n" in token:
										lowercase__   					=			token.replace("\n",   " __newln__"  )

							lowercase__   					=			token.split(" "  )
							lowercase__   					=			[]
							for token in tokens:
										if not len(__lowercase  ):
													continue

										lowercase__   					=			token.lower()
										lowercase__   					=			tuple(__lowercase  )
										lowercase__   					=			tuple(list(word[:-1]  ) + [word[-1] + "</w>"]  )
										lowercase__   					=			get_pairs(__lowercase  )

										if not pairs:
													words.append(__lowercase  )
													continue

										while True:
													lowercase__   					=			min(__lowercase,   key=lambda __lowercase  : self.bpe_ranks.get(__lowercase,   float("inf"  )  )  )
													if bigram not in self.bpe_ranks:
																break
													lowercase__					,    lowercase__   					=			bigram
													lowercase__   					=			[]
													lowercase__   					=			0

													while i < len(__lowercase  ):
																try:
																			lowercase__   					=			word.index(__lowercase,   __lowercase  )
																			new_word.extend(word[i:j]  )
																			lowercase__   					=			j
																except ValueError:
																			new_word.extend(word[i:]  )
																			break

																if word[i] == first and i < len(__lowercase  ) - 1 and word[i + 1] == second:
																			new_word.append(first + second  )
																			i += 2
																else:
																			new_word.append(word[i]  )
																			i += 1
													lowercase__   					=			tuple(__lowercase  )
													lowercase__   					=			new_word
													if len(__lowercase  ) == 1:
																break
													else:
																lowercase__   					=			get_pairs(__lowercase  )
										lowercase__   					=			"@@ ".join(__lowercase  )
										lowercase__   					=			word[:-4]

										lowercase__   					=			word
										words.append(__lowercase  )
							return " ".join(__lowercase  )




				def 							A__ (  self   :  Tuple,   __lowercase   :  str  ):
							lowercase__   					=			[]

							lowercase__   					=			re.findall(R"\S+\n?",   __lowercase  )

							for token in words:
										split_tokens.extend(list(self.bpe(__lowercase  ).split(" "  )  )  )
							return split_tokens




				def 							A__ (  self   :  str,   __lowercase   :  str  ):
							lowercase__   					=			token.lower()
							return self.encoder.get(__lowercase,   self.encoder.get(self.unk_token  )  )




				def 							A__ (  self   :  Tuple,   __lowercase   :  int  ):
							return self.decoder.get(__lowercase,   self.unk_token  )




				def 							A__ (  self   :  Any,   __lowercase   :  List[str]  ):
							lowercase__   					=			" ".join(__lowercase  ).replace("@@ ",   ""  ).strip()
							return out_string




				def 							A__ (  self   :  List[str],   __lowercase   :  str,   __lowercase   :  Optional[str] = None  ):
							if not os.path.isdir(__lowercase  ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
										return
							lowercase__   					=			os.path.join(
							    __lowercase,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
							lowercase__   					=			os.path.join(
							    __lowercase,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

							with open(__lowercase,   "w",   encoding="utf-8"  ) as f:
										f.write(json.dumps(self.encoder,   indent=2,   sort_keys=__lowercase,   ensure_ascii=__lowercase  ) + "\n"  )

							lowercase__   					=			0
							with open(__lowercase,   "w",   encoding="utf-8"  ) as writer:
										writer.write("#version: 0.2\n"  )
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),   key=lambda __lowercase  : kv[1]  ):
													if index != token_index:
																logger.warning(
																    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																    " Please check that the tokenizer is not corrupted!"  )
																lowercase__   					=			token_index
													writer.write(" ".join(__lowercase  ) + "\n"  )
													index += 1

							return vocab_file, merge_file









from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
							from ...feature_extraction_utils import FeatureExtractionMixin
							from ...tokenization_utils_base import PreTrainedTokenizerBase
							from ...utils import TensorType

lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
lowercase_						=       [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
lowercase_						=       [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[int]       						="""whisper"""
				UpperCamelCase__			:   Optional[int]       						=["""past_key_values"""]
				UpperCamelCase__			:   Optional[Any]       						={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self   :  List[Any],   __lowercase   :  List[str]=5_1865,   __lowercase   :  Dict=80,   __lowercase   :  List[Any]=6,   __lowercase   :  Union[str, Any]=4,   __lowercase   :  Tuple=6,   __lowercase   :  Dict=4,   __lowercase   :  List[Any]=1536,   __lowercase   :  Tuple=1536,   __lowercase   :  Any=0.0,   __lowercase   :  List[str]=0.0,   __lowercase   :  List[Any]=5_0257,   __lowercase   :  List[str]=True,   __lowercase   :  str=True,   __lowercase   :  int="gelu",   __lowercase   :  Tuple=256,   __lowercase   :  Tuple=0.0,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=0.0,   __lowercase   :  List[Any]=0.02,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  str=1500,   __lowercase   :  Optional[int]=448,   __lowercase   :  Optional[Any]=5_0256,   __lowercase   :  Tuple=5_0256,   __lowercase   :  Any=5_0256,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Any=[220, 5_0256],   __lowercase   :  List[Any]=False,   __lowercase   :  int=256,   __lowercase   :  int=False,   __lowercase   :  Tuple=0.05,   __lowercase   :  int=10,   __lowercase   :  Dict=2,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=10,   __lowercase   :  Union[str, Any]=0,   __lowercase   :  Tuple=7,   **__lowercase   :  Union[str, Any],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			num_mel_bins
							lowercase__   					=			d_model
							lowercase__   					=			encoder_layers
							lowercase__   					=			encoder_attention_heads
							lowercase__   					=			decoder_layers
							lowercase__   					=			decoder_attention_heads
							lowercase__   					=			decoder_ffn_dim
							lowercase__   					=			encoder_ffn_dim
							lowercase__   					=			dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			activation_dropout
							lowercase__   					=			activation_function
							lowercase__   					=			init_std
							lowercase__   					=			encoder_layerdrop
							lowercase__   					=			decoder_layerdrop
							lowercase__   					=			use_cache
							lowercase__   					=			encoder_layers
							lowercase__   					=			scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__   					=			max_source_positions
							lowercase__   					=			max_target_positions

							# Audio Classification-specific parameters. Feel free to ignore for other classes.
							lowercase__   					=			classifier_proj_size
							lowercase__   					=			use_weighted_layer_sum

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowercase__   					=			apply_spec_augment
							lowercase__   					=			mask_time_prob
							lowercase__   					=			mask_time_length
							lowercase__   					=			mask_time_min_masks
							lowercase__   					=			mask_feature_prob
							lowercase__   					=			mask_feature_length
							lowercase__   					=			mask_feature_min_masks

							lowercase__   					=			median_filter_width

							super().__init__(
							    pad_token_id=__lowercase,   bos_token_id=__lowercase,   eos_token_id=__lowercase,   is_encoder_decoder=__lowercase,   decoder_start_token_id=__lowercase,   suppress_tokens=__lowercase,   begin_suppress_tokens=__lowercase,   **__lowercase,   )
class 			_snake_case     (   lowercase__):




				@property
				def 							A__ (  self   :  str  ):
							lowercase__   					=			OrderedDict(
							    [
							        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
							    ]  )
							if self.use_past:
										lowercase__   					=			{0: "batch"}
							else:
										lowercase__   					=			{0: "batch", 1: "decoder_sequence"}

							if self.use_past:
										self.fill_with_past_key_values_(__lowercase,   direction="inputs"  )

							return common_inputs




				def 							A__ (  self   :  int,   __lowercase   :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional["TensorType"] = None,   __lowercase   :  int = 2_2050,   __lowercase   :  float = 5.0,   __lowercase   :  int = 220,   ):
							lowercase__   					=			OrderedDict()
							lowercase__   					=			OnnxConfig.generate_dummy_inputs(
							    self,   preprocessor=preprocessor.feature_extractor,   batch_size=__lowercase,   framework=__lowercase,   sampling_rate=__lowercase,   time_duration=__lowercase,   frequency=__lowercase,   )
							lowercase__   					=			encoder_inputs["input_features"].shape[2]
							lowercase__   					=			encoder_sequence_length // 2 if self.use_past else seq_length

							lowercase__   					=			super().generate_dummy_inputs(
							    preprocessor.tokenizer,   __lowercase,   __lowercase,   __lowercase,   __lowercase  )

							lowercase__   					=			encoder_inputs.pop("input_features"  )
							lowercase__   					=			decoder_inputs.pop("decoder_input_ids"  )

							if "past_key_values" in decoder_inputs:
										lowercase__   					=			decoder_inputs.pop("past_key_values"  )

							return dummy_inputs




				@property
				def 							A__ (  self   :  int  ):
							return 1e-3




