



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:int =       XLMTokenizer
							A				:List[str] =       False



							def 		_A	(							self				):







											"""simple docstring"""



											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											a__	:							Union[str, Any]      						= [
											    "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>",
											]
											a__	:							Optional[int]      						= dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase				)				)				)				)
											a__	:							Tuple      						= ["l o 123", "lo w 1456", "e r</w> 1789", ""]

											a__	:							List[str]      						= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]				)
											a__	:							Tuple      						= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["merges_file"]				)
											with open(self.vocab_file ,					"w"				) as fp:
															fp.write(json.dumps(__UpperCAmelCase				)				)
											with open(self.merges_file ,					"w"				) as fp:
															fp.write("\n".join(__UpperCAmelCase				)				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							int      						= "lower newer"
											a__	:							Dict      						= "lower newer"
											return input_text, output_text



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= XLMTokenizer(self.vocab_file ,					self.merges_file				)

											a__	:							Dict      						= "lower"
											a__	:							int      						= ["low", "er</w>"]
											a__	:							List[Any]      						= tokenizer.tokenize(__UpperCAmelCase				)
											self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase				)

											a__	:							str      						= tokens + ["<unk>"]
											a__	:							List[str]      						= [14, 15, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				) ,					__UpperCAmelCase				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= XLMTokenizer.from_pretrained("xlm-mlm-en-2048"				)

											a__	:							Tuple      						= tokenizer.encode("sequence builders" ,					add_special_tokens=__UpperCAmelCase				)
											a__	:							Optional[int]      						= tokenizer.encode("multi-sequence build" ,					add_special_tokens=__UpperCAmelCase				)

											a__	:							Union[str, Any]      						= tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase				)
											a__	:							str      						= tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase ,					__UpperCAmelCase				)

											assert encoded_sentence == [0] + text + [1]
											assert encoded_pair == [0] + text + [1] + text_a + [1]







# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

lowerCamelCase		       =      """facebook/wmt19-en-de"""
lowerCamelCase		       =      FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCamelCase		       =      FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

lowerCamelCase		       =      FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

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

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

# Save
lowerCamelCase		       =      """tiny-wmt19-en-de"""
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-de


