
'''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 		snake_case_       (						unittest.TestCase						):



   """simple docstring"""





   @patch("sys.argv"			,   ["fakeprogrampath", "env"])
   def     __UpperCAmelCase      ( 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     __UpperCAmelCase      ( self):
          import transformers.commands.transformers_cli

          shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"			,   ignore_errors=UpperCamelCase)  # 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     __UpperCAmelCase      ( 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=UpperCamelCase)

          # 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     __UpperCAmelCase      ( 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=UpperCamelCase)

          # 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 json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		snake_case_       (						A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizer
   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizerFast
   __lowerCAmelCase	:							int								=True







   def     __UpperCAmelCase      ( self):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(f"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"""			,   encoding="utf-8") as f_data:
                 lowerCamelCase__				   =      f_data.read().replace("\n\n"			,   "\n").strip()

          lowerCamelCase__				   =      [
              "<s>",
              "</s>",
              "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>",
              ",</w>",
              "<unk>",
          ]
          lowerCamelCase__				   =      dict(zip(UpperCamelCase			,   range(len(UpperCamelCase))))
          lowerCamelCase__				   =      ["l o 123", "lo w 1456", "e r</w> 1789", ""]

          lowerCamelCase__				   =      os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES["vocab_file"])
          lowerCamelCase__				   =      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     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      "lower newer"
          lowerCamelCase__				   =      "lower newer"
          return input_text, output_text







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class(vocab_file=self.vocab_file			,   merges_file=self.merges_file)

          lowerCamelCase__				   =      "lower"
          lowerCamelCase__				   =      ["low", "er</w>"]
          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokens + ["<unk>"]
          lowerCamelCase__				   =      [16, 17, 23]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase)			,   UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          if not self.test_rust_tokenizer:
                 return

          lowerCamelCase__				   =      self.get_tokenizer()
          lowerCamelCase__				   =      self.get_rust_tokenizer()

          lowerCamelCase__				   =      "lower,newer"

          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      self.get_rust_tokenizer()
          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)







   @slow
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class.from_pretrained("allegro/herbert-base-cased")

          lowerCamelCase__				   =      tokenizer.encode("konstruowanie sekwencji"			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.encode("konstruowanie wielu sekwencji"			,   add_special_tokens=UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase			,   UpperCamelCase)

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]

