'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class     a__      (   UpperCAmelCase__ ):
     lowerCamelCase  :     Dict							="EncodecFeatureExtractor"
     lowerCamelCase  :     List[str]							=("T5Tokenizer", "T5TokenizerFast")






     def __init__( self		:		Dict  ,				a		:		Optional[int]  ,				a		:		Optional[int]    ):





         """simple docstring"""



         super().__init__(a  ,				a    )
         __lowerCamelCase		=  self.feature_extractor
         __lowerCamelCase		=  False






     def    SCREAMING_SNAKE_CASE__   ( self		:		int  ,				a		:		Optional[Any]=None  ,				a		:		List[str]=None  ,				a		:		Tuple=True    ):





         """simple docstring"""



         return self.tokenizer.get_decoder_prompt_ids(task=a  ,				language=a  ,				no_timestamps=a    )






     def __call__( self		:		int  ,				*a		:		List[str]  ,				**a		:		List[Any]    ):





         """simple docstring"""



         if self._in_target_context_manager:
             return self.current_processor(*a  ,				**a    )

         __lowerCamelCase		=  kwargs.pop('''audio'''  ,				a    )
         __lowerCamelCase		=  kwargs.pop('''sampling_rate'''  ,				a    )
         __lowerCamelCase		=  kwargs.pop('''text'''  ,				a    )
         if len(a    ) > 0:
             __lowerCamelCase		=  args[0]
             __lowerCamelCase		=  args[1:]

         if audio is None and text is None:
             raise ValueError('''You need to specify either an `audio` or `text` input to process.'''    )

         if text is not None:
             __lowerCamelCase		=  self.tokenizer(a  ,				**a    )

         if audio is not None:
             __lowerCamelCase		=  self.feature_extractor(a  ,				*a  ,				sampling_rate=a  ,				**a    )

         if audio is None:
             return inputs

         elif text is None:
             return audio_inputs

         else:
             __lowerCamelCase		=  audio_inputs['''input_values''']
             if "padding_mask" in audio_inputs:
                 __lowerCamelCase		=  audio_inputs['''padding_mask''']
             return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				*a		:		Any  ,				**a		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  kwargs.pop('''audio'''  ,				a    )
         __lowerCamelCase		=  kwargs.pop('''padding_mask'''  ,				a    )

         if len(a    ) > 0:
             __lowerCamelCase		=  args[0]
             __lowerCamelCase		=  args[1:]

         if audio_values is not None:
             return self._decode_audio(a  ,				padding_mask=a    )
         else:
             return self.tokenizer.batch_decode(*a  ,				**a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				*a		:		Dict  ,				**a		:		List[Any]    ):





         """simple docstring"""



         return self.tokenizer.decode(*a  ,				**a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict  ,				a		:		Any  ,				a		:		Optional = None    ):





         """simple docstring"""



         __lowerCamelCase		=  to_numpy(a    )
         __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  audio_values.shape

         if padding_mask is None:
             return list(a    )

         __lowerCamelCase		=  to_numpy(a    )

         # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
         # token (so that the generated audio values are **not** treated as padded tokens)
         __lowerCamelCase		=  seq_len - padding_mask.shape[-1]
         __lowerCamelCase		=  1 - self.feature_extractor.padding_value
         __lowerCamelCase		=  np.pad(a  ,				((0, 0), (0, difference))  ,				'''constant'''  ,				constant_values=a    )

         __lowerCamelCase		=  audio_values.tolist()
         for i in range(a    ):
             __lowerCamelCase		=  np.asarray(audio_values[i]    )[
                 padding_mask[i][None, :] != self.feature_extractor.padding_value
             ]
             __lowerCamelCase		=  sliced_audio.reshape(a  ,				-1    )

         return audio_values


"""simple docstring"""



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    '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>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 UpperCAmelCase__          = FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase__          = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru



