



import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Any =       FunnelTokenizer
							A				:Optional[int] =       FunnelTokenizerFast
							A				:List[Any] =       True
							A				:Tuple =       True



							def 		_A	(							self				):







											"""simple docstring"""



											super().setUp()

											a__	:							Optional[int]      						= [
											    "<unk>",
											    "<cls>",
											    "<sep>",
											    "want",
											    "##want",
											    "##ed",
											    "wa",
											    "un",
											    "runn",
											    "##ing",
											    ",",
											    "low",
											    "lowest",
											]
											a__	:							Union[str, Any]      						= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]				)
											with open(self.vocab_file ,					"w" ,					encoding="utf-8"				) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return FunnelTokenizer.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[str]      						= "UNwant\u00E9d,running"
											a__	:							Dict      						= "unwanted, running"
											return input_text, output_text



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.tokenizer_class(self.vocab_file				)

											a__	:							Any      						= tokenizer.tokenize("UNwant\u00E9d,running"				)
											self.assertListEqual(__UpperCAmelCase ,					["un", "##want", "##ed", ",", "runn", "##ing"]				)
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				) ,					[7, 4, 5, 10, 8, 9]				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.get_tokenizers(do_lower_case=__UpperCAmelCase				)
											for tokenizer in tokenizers:
															a__	:							int      						= tokenizer("UNwant\u00E9d,running"				)
															a__	:							Optional[int]      						= len(inputs["input_ids"]				) - 1
															self.assertListEqual(inputs["token_type_ids"] ,					[2] + [0] * sentence_len				)

															a__	:							str      						= tokenizer("UNwant\u00E9d,running" ,					"UNwant\u00E9d,running"				)
															self.assertListEqual(inputs["token_type_ids"] ,					[2] + [0] * sentence_len + [1] * sentence_len				)







# 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


