




import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_UpperCamelCase      (				lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		LxmertTokenizer
      UpperCAmelCase_        =		LxmertTokenizerFast
      UpperCAmelCase_        =		True
      UpperCAmelCase_        =		True
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Any:
       super().setUp()

       UpperCAmelCase__							 =						[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :Tuple      )     ->			Optional[int]:
       UpperCAmelCase__							 =						"UNwant\u00E9d,running"
       UpperCAmelCase__							 =						"unwanted, running"
       return input_text, output_text
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Any:
       UpperCAmelCase__							 =						self.tokenizer_class(self.vocab_file      )

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







      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Optional[int]:
       if not self.test_rust_tokenizer:
        return

       UpperCAmelCase__							 =						self.get_tokenizer()
       UpperCAmelCase__							 =						self.get_rust_tokenizer()

       UpperCAmelCase__							 =						"I was born in 92000, and this is falsé."

       UpperCAmelCase__							 =						tokenizer.tokenize(lowerCamelCase      )
       UpperCAmelCase__							 =						rust_tokenizer.tokenize(lowerCamelCase      )
       self.assertListEqual(lowerCamelCase ,      lowerCamelCase      )

       UpperCAmelCase__							 =						tokenizer.encode(lowerCamelCase ,      add_special_tokens=lowerCamelCase      )
       UpperCAmelCase__							 =						rust_tokenizer.encode(lowerCamelCase ,      add_special_tokens=lowerCamelCase      )
       self.assertListEqual(lowerCamelCase ,      lowerCamelCase      )

       UpperCAmelCase__							 =						self.get_rust_tokenizer()
       UpperCAmelCase__							 =						tokenizer.encode(lowerCamelCase      )
       UpperCAmelCase__							 =						rust_tokenizer.encode(lowerCamelCase      )
       self.assertListEqual(lowerCamelCase ,      lowerCamelCase      )











# 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						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   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						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

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

# Save
_lowerCAmelCase						:   Optional[Any]		=   "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





