
"""simple docstring"""




import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch

class 		a__						(				unittest.TestCase						):
   @patch("sys.argv"     ,   ["fakeprogrampath", "env"]    )
   def       __magic_name__      (      self    ):
          # test transformers-cli env
          import transformers.commands.transformers_cli

          with CaptureStd() as cs:
                 transformers.commands.transformers_cli.main()
          self.assertIn("Python version"     ,   cs.out    )
          self.assertIn("Platform"     ,   cs.out    )
          self.assertIn("Using distributed or parallel set-up in script?"     ,   cs.out    )
   @is_pt_tf_cross_test
   @patch(
       "sys.argv"     ,   ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]    )
   def       __magic_name__      (      self    ):
          import transformers.commands.transformers_cli

          shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"     ,   ignore_errors=_a    )  # cleans potential past runs
          transformers.commands.transformers_cli.main()

          # The original repo has no TF weights -- if they exist, they were created by the CLI
          self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"    )    )
   @require_torch
   @patch("sys.argv"     ,   ["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]    )
   def       __magic_name__      (      self    ):
          import transformers.commands.transformers_cli

          # # remove any previously downloaded model to start clean
          shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"     ,   ignore_errors=_a    )

          # run the command
          transformers.commands.transformers_cli.main()

          # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"    )    )
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"    )    )
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"    )    )

   @require_torch
   @patch(
       "sys.argv"     ,   [
           "fakeprogrampath",
           "download",
           "hf-internal-testing/test_dynamic_model_with_tokenizer",
           "--trust-remote-code",
           "--cache-dir",
           "/tmp",
       ]     ,   )
   def       __magic_name__      (      self    ):
          import transformers.commands.transformers_cli

          # # remove any previously downloaded model to start clean
          shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"     ,   ignore_errors=_a    )

          # run the command
          transformers.commands.transformers_cli.main()

          # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"    )    )
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"    )    )
          self.assertTrue(
              os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"    )    )




"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 		a__						(				a_,      unittest.TestCase						):
   __lowerCAmelCase						=    BertweetTokenizer
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase   :    Tuple			=							["I", "m", "V@@", "R@@", "r", "e@@"]
          lowercase   :    List[Any]			=							dict(zip(_a     ,   range(len(_a    )    )    )    )
          lowercase   :    Union[str, Any]			=							["#version: 0.2", "a m</w>"]
          lowercase   :    List[Any]			=							{"unk_token": "<unk>"}

          lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )
          lowercase   :    Any			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["merges_file"]    )
          with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 for token in vocab_tokens:
                        fp.write(f"""{token} {vocab_tokens[token]}\n"""    )
          with open(self.merges_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 fp.write("\n".join(_a    )    )
   def       __magic_name__      (      self     ,   **_a    ):
          kwargs.update(self.special_tokens_map    )
          return BertweetTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							"I am VinAI Research"
          lowercase   :    str			=							"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
          return input_text, output_text

   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							BertweetTokenizer(self.vocab_file     ,   self.merges_file     ,   **self.special_tokens_map    )
          lowercase   :    int			=							"I am VinAI Research"
          lowercase   :    Dict			=							"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
          lowercase   :    Tuple			=							tokenizer.tokenize(_a    )
          self.assertListEqual(_a     ,   _a    )

          lowercase   :    Any			=							tokens + [tokenizer.unk_token]

          lowercase   :    List[Any]			=							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a    )     ,   _a    )


