





'''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 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		@patch("""sys.argv"""   ,   ["""fakeprogrampath""", """env"""]   )
		def 							A  (self    :   List[str]   ):
			# 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 							A  (self    :   Optional[int]   ):
			import transformers.commands.transformers_cli

			shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""   ,   ignore_errors=_lowerCAmelCase   )  # 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 							A  (self    :   Optional[int]   ):
			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=_lowerCAmelCase   )

			# 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 							A  (self    :   List[Any]   ):
			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=_lowerCAmelCase   )

			# 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]

