

'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    						=       {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""RemBertTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""RemBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RemBertForCausalLM""",
							    """RemBertForMaskedLM""",
							    """RemBertForMultipleChoice""",
							    """RemBertForQuestionAnswering""",
							    """RemBertForSequenceClassification""",
							    """RemBertForTokenClassification""",
							    """RemBertLayer""",
							    """RemBertModel""",
							    """RemBertPreTrainedModel""",
							    """load_tf_weights_in_rembert""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRemBertForCausalLM""",
							    """TFRemBertForMaskedLM""",
							    """TFRemBertForMultipleChoice""",
							    """TFRemBertForQuestionAnswering""",
							    """TFRemBertForSequenceClassification""",
							    """TFRemBertForTokenClassification""",
							    """TFRemBertLayer""",
							    """TFRemBertModel""",
							    """TFRemBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_rembert import RemBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_rembert_fast import RemBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rembert import (
														    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RemBertForCausalLM,
														    RemBertForMaskedLM,
														    RemBertForMultipleChoice,
														    RemBertForQuestionAnswering,
														    RemBertForSequenceClassification,
														    RemBertForTokenClassification,
														    RemBertLayer,
														    RemBertModel,
														    RemBertPreTrainedModel,
														    load_tf_weights_in_rembert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rembert import (
														    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRemBertForCausalLM,
														    TFRemBertForMaskedLM,
														    TFRemBertForMultipleChoice,
														    TFRemBertForQuestionAnswering,
														    TFRemBertForSequenceClassification,
														    TFRemBertForTokenClassification,
														    TFRemBertLayer,
														    TFRemBertModel,
														    TFRemBertPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase		       =      subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
lowerCamelCase		       =      subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()

lowerCamelCase		       =      """|""".join(sys.argv[1:])
lowerCamelCase		       =      re.compile(RF'^({joined_dirs}).*?\.py$')

lowerCamelCase		       =      [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


