



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


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



@require_torch
class 			__lowerCAmelCase  (						unittest.TestCase ):




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return FSMTTokenizer.from_pretrained(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     FSMTForConditionalGeneration.from_pretrained(__UpperCAmelCase					).to(__UpperCAmelCase					)
    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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    # note: this test is not testing the best performance since it only evals a small batch
    # but it should be enough to detect a regression in the output quality
    __lowerCamelCase    				=     F"""facebook/wmt19-{pair}"""
    __lowerCamelCase    				=     self.get_tokenizer(__UpperCAmelCase					)
    __lowerCamelCase    				=     self.get_model(__UpperCAmelCase					)

    __lowerCamelCase    				=     bleu_data[pair]['''src''']
    __lowerCamelCase    				=     bleu_data[pair]['''tgt''']

    __lowerCamelCase    				=     tokenizer(__UpperCAmelCase						,					return_tensors='''pt'''						,					truncation=__UpperCAmelCase						,					padding='''longest'''					).to(__UpperCAmelCase					)
    __lowerCamelCase    				=     model.generate(
        input_ids=batch.input_ids						,					num_beams=8						,					)
    __lowerCamelCase    				=     tokenizer.batch_decode(
        __UpperCAmelCase						,					skip_special_tokens=__UpperCAmelCase						,					clean_up_tokenization_spaces=__UpperCAmelCase					)
    __lowerCamelCase    				=     calculate_bleu(__UpperCAmelCase						,					__UpperCAmelCase					)
    print(__UpperCAmelCase					)
    self.assertGreaterEqual(scores['''bleu''']						,					__UpperCAmelCase					)











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


a_								=       logging.get_logger(__name__)

a_								=       {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""canine"""




 def __init__(    self						,					__UpperCAmelCase=768						,					__UpperCAmelCase=12						,					__UpperCAmelCase=12						,					__UpperCAmelCase=3072						,					__UpperCAmelCase="gelu"						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=16384						,					__UpperCAmelCase=16						,					__UpperCAmelCase=0.02						,					__UpperCAmelCase=1E-1_2						,					__UpperCAmelCase=0						,					__UpperCAmelCase=0Xe_000						,					__UpperCAmelCase=0Xe_001						,					__UpperCAmelCase=4						,					__UpperCAmelCase=4						,					__UpperCAmelCase=8						,					__UpperCAmelCase=16384						,					__UpperCAmelCase=128						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(pad_token_id=__UpperCAmelCase						,					bos_token_id=__UpperCAmelCase						,					eos_token_id=__UpperCAmelCase						,					**__UpperCAmelCase					)

    __lowerCamelCase    				=     max_position_embeddings
    __lowerCamelCase    				=     hidden_size
    __lowerCamelCase    				=     num_hidden_layers
    __lowerCamelCase    				=     num_attention_heads
    __lowerCamelCase    				=     intermediate_size
    __lowerCamelCase    				=     hidden_act
    __lowerCamelCase    				=     hidden_dropout_prob
    __lowerCamelCase    				=     attention_probs_dropout_prob
    __lowerCamelCase    				=     initializer_range
    __lowerCamelCase    				=     type_vocab_size
    __lowerCamelCase    				=     layer_norm_eps

    # Character config:
    __lowerCamelCase    				=     downsampling_rate
    __lowerCamelCase    				=     upsampling_kernel_size
    __lowerCamelCase    				=     num_hash_functions
    __lowerCamelCase    				=     num_hash_buckets
    __lowerCamelCase    				=     local_transformer_stride






