


'''simple docstring'''






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

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


__UpperCAmelCase       =							logging.get_logger(__name__)


__UpperCAmelCase       =							{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

__UpperCAmelCase       =							{
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

__UpperCAmelCase       =							'''</w>'''
__UpperCAmelCase       =							'''@@ '''


def  _snake_case       (					A	)						->   List[str]:
							lowerCAmelCase__	       =     set()
							lowerCAmelCase__	       =     word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)	)
														lowerCAmelCase__	       =     char
							return pairs


# Speech2Text2 has no max input length
__UpperCAmelCase       =							{'''facebook/s2t-wav2vec2-large-en-de''': 1_024}






class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       List[str]     						=				VOCAB_FILES_NAMES
							lowercase__      :       Optional[int]     						=				PRETRAINED_VOCAB_FILES_MAP
							lowercase__      :       Tuple     						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__      :       str     						=				["input_ids", "attention_mask"]







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_="<s>" ,   lowerCamelCase_="<pad>" ,   lowerCamelCase_="</s>" ,   lowerCamelCase_="<unk>" ,   lowerCamelCase_=False ,   lowerCamelCase_=None ,   **lowerCamelCase_ ,   )	->							Tuple:
														super().__init__(
														    unk_token=lowerCamelCase_ ,   bos_token=lowerCamelCase_ ,   eos_token=lowerCamelCase_ ,   pad_token=lowerCamelCase_ ,   do_lower_case=lowerCamelCase_ ,   **lowerCamelCase_ ,   )

														lowerCAmelCase__	       =     do_lower_case

														with open(lowerCamelCase_ ,   encoding='''utf-8'''       ) as vocab_handle:
																					lowerCAmelCase__	       =     json.load(lowerCamelCase_       )
														lowerCAmelCase__	       =     {v: k for k, v in self.encoder.items()}

														if merges_file is None:
																					logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""       )

																					lowerCAmelCase__	       =     None
																					lowerCAmelCase__	       =     None
														else:
																					with open(lowerCamelCase_ ,   encoding='''utf-8'''       ) as merges_handle:
																												lowerCAmelCase__	       =     merges_handle.read().split('''\n'''       )[:-1]

																					lowerCAmelCase__	       =     [tuple(merge.split()[:2]       ) for merge in merges]
																					lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )
																					lowerCAmelCase__	       =     {}







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return len(self.decoder       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return dict(self.encoder ,   **self.added_tokens_encoder       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[str]:
														lowerCAmelCase__	       =     tuple(token[:-1]       ) + (token[-1] + BPE_TOKEN_MERGES,)
														if token in self.cache:
																					return self.cache[token]
														lowerCAmelCase__	       =     get_pairs(lowerCamelCase_       )

														if not pairs:
																					return token

														while True:
																					lowerCAmelCase__	       =     min(lowerCamelCase_ ,   key=lambda lowerCamelCase_       : self.bpe_ranks.get(lowerCamelCase_ ,   float('''inf'''       )       )       )
																					if bigram not in self.bpe_ranks:
																												break
																					lowerCAmelCase__       , lowerCAmelCase__	       =     bigram
																					lowerCAmelCase__	       =     []
																					lowerCAmelCase__	       =     0
																					while i < len(lowerCamelCase_       ):
																												try:
																																			lowerCAmelCase__	       =     word.index(lowerCamelCase_ ,   lowerCamelCase_       )
																												except ValueError:
																																			new_word.extend(word[i:]       )
																																			break
																												else:
																																			new_word.extend(word[i:j]       )
																																			lowerCAmelCase__	       =     j

																												if word[i] == first and i < len(lowerCamelCase_       ) - 1 and word[i + 1] == second:
																																			new_word.append(first + second       )
																																			i += 2
																												else:
																																			new_word.append(word[i]       )
																																			i += 1
																					lowerCAmelCase__	       =     tuple(lowerCamelCase_       )
																					lowerCAmelCase__	       =     new_word
																					if len(lowerCamelCase_       ) == 1:
																												break
																					else:
																												lowerCAmelCase__	       =     get_pairs(lowerCamelCase_       )
														lowerCAmelCase__	       =     ''' '''.join(lowerCamelCase_       )
														if word == "\n  " + BPE_TOKEN_MERGES:
																					lowerCAmelCase__	       =     '''\n''' + BPE_TOKEN_MERGES

														if word.endswith(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     word.replace(lowerCamelCase_ ,   ''''''       )

														lowerCAmelCase__	       =     word.replace(''' ''' ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     word
														return word







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Dict:

														if self.bpe_ranks is None:
																					raise ValueError(
																					    '''This tokenizer was instantiated without a `merges.txt` file, so'''
																					    ''' that it can only be used for decoding, not for encoding.'''
																					    '''Make sure to provide `merges.txt` file at instantiation to enable '''
																					    '''encoding.'''       )

														if self.do_lower_case:
																					lowerCAmelCase__	       =     text.lower()

														lowerCAmelCase__	       =     text.split()

														lowerCAmelCase__	       =     []
														for token in text:
																					if token:
																												split_tokens.extend(list(self.bpe(lowerCamelCase_       ).split(''' '''       )       )       )

														return split_tokens







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							int:
														return self.encoder.get(lowerCamelCase_ ,   self.encoder.get(self.unk_token       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     self.decoder.get(lowerCamelCase_ ,   self.unk_token       )
														return result







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     ''' '''.join(lowerCamelCase_       )

														# make sure @@ tokens are concatenated
														lowerCAmelCase__	       =     ''''''.join(string.split(lowerCamelCase_       )       )

														return string







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Tuple[str]:
														if not os.path.isdir(lowerCamelCase_       ):
																					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
																					return
														lowerCAmelCase__	       =     os.path.join(
														    lowerCamelCase_ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
														lowerCAmelCase__	       =     os.path.join(
														    lowerCamelCase_ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

														lowerCAmelCase__	       =     0
														if self.bpe_ranks is None:
																					return (vocab_file,)

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

														return (vocab_file, merges_file)



'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       torch.FloatTensor




class 		a__    (							a__    ,    a__  ):





							'''simple docstring'''






							@register_to_config
							def __init__(		self ,   lowerCamelCase_ = 6_55_36 ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 0 ,   lowerCamelCase_ = "fourier" ,   lowerCamelCase_ = True ,   lowerCamelCase_ = False ,   lowerCamelCase_ = 0.0 ,   lowerCamelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,   lowerCamelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,   lowerCamelCase_ = "UNetMidBlock1D" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = (32, 32, 64) ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 8 ,   lowerCamelCase_ = 1 ,   lowerCamelCase_ = False ,   )	->							Optional[int]:
														super().__init__()
														lowerCAmelCase__	       =     sample_size

														# time
														if time_embedding_type == "fourier":
																					lowerCAmelCase__	       =     GaussianFourierProjection(
																					    embedding_size=8 ,   set_W_to_weight=lowerCamelCase_ ,   log=lowerCamelCase_ ,   flip_sin_to_cos=lowerCamelCase_       )
																					lowerCAmelCase__	       =     2 * block_out_channels[0]
														elif time_embedding_type == "positional":
																					lowerCAmelCase__	       =     Timesteps(
																					    block_out_channels[0] ,   flip_sin_to_cos=lowerCamelCase_ ,   downscale_freq_shift=lowerCamelCase_       )
																					lowerCAmelCase__	       =     block_out_channels[0]

														if use_timestep_embedding:
																					lowerCAmelCase__	       =     block_out_channels[0] * 4
																					lowerCAmelCase__	       =     TimestepEmbedding(
																					    in_channels=lowerCamelCase_ ,   time_embed_dim=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   out_dim=block_out_channels[0] ,   )

														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None

														# down
														lowerCAmelCase__	       =     in_channels
														for i, down_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     block_out_channels[i]

																					if i == 0:
																												input_channel += extra_in_channels

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_down_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_downsample=not is_final_block or downsample_each_block ,   )
																					self.down_blocks.append(lowerCamelCase_       )

														# mid
														lowerCAmelCase__	       =     get_mid_block(
														    lowerCamelCase_ ,   in_channels=block_out_channels[-1] ,   mid_channels=block_out_channels[-1] ,   out_channels=block_out_channels[-1] ,   embed_dim=block_out_channels[0] ,   num_layers=lowerCamelCase_ ,   add_downsample=lowerCamelCase_ ,   )

														# up
														lowerCAmelCase__	       =     list(reversed(lowerCamelCase_       )       )
														lowerCAmelCase__	       =     reversed_block_out_channels[0]
														if out_block_type is None:
																					lowerCAmelCase__	       =     out_channels
														else:
																					lowerCAmelCase__	       =     block_out_channels[0]

														for i, up_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     (
																					    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase_       ) - 1 else final_upsample_channels
																					)

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_up_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_upsample=not is_final_block ,   )
																					self.up_blocks.append(lowerCamelCase_       )
																					lowerCAmelCase__	       =     output_channel

														# out
														lowerCAmelCase__	       =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,   32       )
														lowerCAmelCase__	       =     get_out_block(
														    out_block_type=lowerCamelCase_ ,   num_groups_out=lowerCamelCase_ ,   embed_dim=block_out_channels[0] ,   out_channels=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   fc_dim=block_out_channels[-1] // 4 ,   )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = True ,   )	->							Union[UNetaDOutput, Tuple]:
														lowerCAmelCase__	       =     timestep
														if not torch.is_tensor(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     torch.tensor([timesteps] ,   dtype=torch.long ,   device=sample.device       )
														elif torch.is_tensor(lowerCamelCase_       ) and len(timesteps.shape       ) == 0:
																					lowerCAmelCase__	       =     timesteps[None].to(sample.device       )

														lowerCAmelCase__	       =     self.time_proj(lowerCamelCase_       )
														if self.config.use_timestep_embedding:
																					lowerCAmelCase__	       =     self.time_mlp(lowerCamelCase_       )
														else:
																					lowerCAmelCase__	       =     timestep_embed[..., None]
																					lowerCAmelCase__	       =     timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
																					lowerCAmelCase__	       =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

														# 2. down
														lowerCAmelCase__	       =     ()
														for downsample_block in self.down_blocks:
																					lowerCAmelCase__       , lowerCAmelCase__	       =     downsample_block(hidden_states=lowerCamelCase_ ,   temb=lowerCamelCase_       )
																					down_block_res_samples += res_samples

														# 3. mid
														if self.mid_block:
																					lowerCAmelCase__	       =     self.mid_block(lowerCamelCase_ ,   lowerCamelCase_       )

														# 4. up
														for i, upsample_block in enumerate(self.up_blocks       ):
																					lowerCAmelCase__	       =     down_block_res_samples[-1:]
																					lowerCAmelCase__	       =     down_block_res_samples[:-1]
																					lowerCAmelCase__	       =     upsample_block(lowerCamelCase_ ,   res_hidden_states_tuple=lowerCamelCase_ ,   temb=lowerCamelCase_       )

														# 5. post-process
														if self.out_block:
																					lowerCAmelCase__	       =     self.out_block(lowerCamelCase_ ,   lowerCamelCase_       )

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=lowerCamelCase_       )