
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__magic_name__           =					{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     __magic_name__           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




@require_tokenizers
class    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      _A  :						Optional[Any]      		=       EsmTokenizer
      def A_     (     self	):
          super().setUp()
          snake_case__  =			tempfile.mkdtemp()
          # fmt: off
          snake_case__  =			["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
          # fmt: on
          snake_case__  =			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						,		**lowerCamelCase	):
          return [self.get_tokenizer(**lowerCamelCase	)]
      def A_     (     self						,		**lowerCamelCase	):
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,		**lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			tokenizer.tokenize("LAGVS"	)
          self.assertListEqual(lowerCamelCase						,		["L", "A", "G", "V", "S"]	)
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,		[4, 5, 6, 7, 8]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			"LAGVS"
          self.assertListEqual(tokenizer.encode(lowerCamelCase	)						,		[0, 4, 5, 6, 7, 8, 2]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			["LAGVS", "WCB"]
          snake_case__  =			tokenizer(lowerCamelCase						,		padding=lowerCamelCase	)["input_ids"]

          self.assertListEqual(lowerCamelCase						,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			["LAGVS", "WCB"]
          snake_case__  =			tokenizer(lowerCamelCase						,		padding=lowerCamelCase	)["input_ids"]

          self.assertListEqual(lowerCamelCase						,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]	)



      def A_     (     self	):
          snake_case__  =			self.get_tokenizers(fast=lowerCamelCase	)
          for tokenizer in tokenizers:
              with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
                  snake_case__  =			"<unk>"
                  snake_case__  =			"<mask>"

                  snake_case__  =			tokenizer.tokenize(lowerCamelCase	)
                  snake_case__  =			tokenizer.tokenize(lowerCamelCase	)

                  self.assertEqual(len(lowerCamelCase	)						,		1	)
                  self.assertEqual(len(lowerCamelCase	)						,		1	)
                  self.assertEqual(token_a[0]						,		lowerCamelCase	)
                  self.assertEqual(token_a[0]						,		lowerCamelCase	)



