
import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCamelCase				=			get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, """r""", encoding="""utf-8""") as f:
   __lowerCamelCase				=			json.load(f)






@require_torch
class     UpperCAmelCase						(		unittest.TestCase					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   Optional[int]					)						->      Any:


          '''simple docstring'''


          return FSMTTokenizer.from_pretrained(A_					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   Union[str, Any]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  int     							= FSMTForConditionalGeneration.from_pretrained(A_					).to(A_					)
          if torch_device == "cuda":
                 model.half()
          return model



   @parameterized.expand(
       [
           ["en-ru", 26.0],
           ["ru-en", 22.0],
           ["en-de", 22.0],
           ["de-en", 29.0],
       ]					)
   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   Dict       ,		snake_case__						:   Any					)						->      int:


          '''simple docstring'''


          snake_case			:  int     							= f"""facebook/wmt19-{pair}"""
          snake_case			:  str     							= self.get_tokenizer(A_					)
          snake_case			:  List[Any]     							= self.get_model(A_					)

          snake_case			:  Union[str, Any]     							= bleu_data[pair]["src"]
          snake_case			:  Dict     							= bleu_data[pair]["tgt"]

          snake_case			:  List[Any]     							= tokenizer(A_       ,		return_tensors="pt"       ,		truncation=A_       ,		padding="longest"					).to(A_					)
          snake_case			:  Optional[Any]     							= model.generate(
              input_ids=batch.input_ids       ,		num_beams=8       ,		)
          snake_case			:  List[str]     							= tokenizer.batch_decode(
              A_       ,		skip_special_tokens=A_       ,		clean_up_tokenization_spaces=A_					)
          snake_case			:  int     							= calculate_bleu(A_       ,		A_					)
          print(A_					)
          self.assertGreaterEqual(scores["bleu"]       ,		A_					)









"""simple docstring"""




import math
from typing import Optional

import numpy as np

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


__magic_name__						: List[str]    =				logging.get_logger(__name__)

__magic_name__						: Tuple    =				{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    int     ,							):
										__lowerCamelCase          =      target_bandwidths
										__lowerCamelCase          =      sampling_rate
										__lowerCamelCase          =      audio_channels
										__lowerCamelCase          =      normalize
										__lowerCamelCase          =      chunk_length_s
										__lowerCamelCase          =      overlap
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_filters
										__lowerCamelCase          =      num_residual_layers
										__lowerCamelCase          =      upsampling_ratios
										__lowerCamelCase          =      norm_type
										__lowerCamelCase          =      kernel_size
										__lowerCamelCase          =      last_kernel_size
										__lowerCamelCase          =      residual_kernel_size
										__lowerCamelCase          =      dilation_growth_rate
										__lowerCamelCase          =      use_causal_conv
										__lowerCamelCase          =      pad_mode
										__lowerCamelCase          =      compress
										__lowerCamelCase          =      num_lstm_layers
										__lowerCamelCase          =      trim_right_ratio
										__lowerCamelCase          =      codebook_size
										__lowerCamelCase          =      codebook_dim if codebook_dim is not None else hidden_size
										__lowerCamelCase          =      use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
													raise ValueError(
													    f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'   )

										super().__init__(**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[Any]   ):
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1     ,							int((1.0 - self.overlap) * self.chunk_length   )   )




							@property
							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




							@property
							def   __a   (     self:    List[Any]   ):
										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )




