from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A   :     Union[str, Any]       =				{"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     Any       =				["GPTNeoXTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     Union[str, Any]       =				[
          "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GPTNeoXForCausalLM",
          "GPTNeoXForQuestionAnswering",
          "GPTNeoXForSequenceClassification",
          "GPTNeoXForTokenClassification",
          "GPTNeoXLayer",
          "GPTNeoXModel",
          "GPTNeoXPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_neox import (
                GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTNeoXForCausalLM,
                GPTNeoXForQuestionAnswering,
                GPTNeoXForSequenceClassification,
                GPTNeoXForTokenClassification,
                GPTNeoXLayer,
                GPTNeoXModel,
                GPTNeoXPreTrainedModel,
            )


else:
      import sys

      A   :     Optional[Any]       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


A   :     Tuple       =				get_tests_dir("fixtures")


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		Union[str, Any]:
        # A mock response for an HTTP head request to emulate server down
        SCREAMING_SNAKE_CASE_      =     mock.Mock()
        SCREAMING_SNAKE_CASE_      =     500
        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     HTTPError
        SCREAMING_SNAKE_CASE_      =     {}

        # Download this model to make sure it's in the cache.
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
        # Under the mock environment we get a 500 error when trying to reach the model.
        with mock.patch("requests.Session.request"     ,   return_value=__magic_name__      ) as mock_head:
              SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
              # This check we did call the fake head request
              mock_head.assert_called()







  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        # This test is for deprecated behavior and can be removed in v5
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(
            "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"      )



@is_staging_test
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  @classmethod
  def       __A   (							cls       :				Any      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     TOKEN
        HfFolder.save_token(__magic_name__      )







  @classmethod
  def       __A   (							cls       :				int      )     ->		Optional[Any]:
        try:
              delete_repo(token=cls._token     ,   repo_id="test-feature-extractor"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="valid_org/test-feature-extractor-org"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="test-dynamic-feature-extractor"      )
        except HTTPError:
              pass







  def       __A   (							self       :				str      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="test-feature-extractor"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				Dict      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("valid_org/test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="valid_org/test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="valid_org/test-feature-extractor-org"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        CustomFeatureExtractor.register_for_auto_class()
        SCREAMING_SNAKE_CASE_      =     CustomFeatureExtractor.from_pretrained(__magic_name__      )

        feature_extractor.push_to_hub("test-dynamic-feature-extractor"     ,   use_auth_token=self._token      )

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            feature_extractor.auto_map     ,   {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}     ,   )

        SCREAMING_SNAKE_CASE_      =     AutoFeatureExtractor.from_pretrained(
            F'''{USER}/test-dynamic-feature-extractor'''     ,   trust_remote_code=__magic_name__      )
        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
        self.assertEqual(new_feature_extractor.__class__.__name__     ,   "CustomFeatureExtractor"      )


