






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__UpperCamelCase					:		Optional[int]	  =						"""
Human: <<task>>

Assistant: """


__UpperCamelCase					:		List[str]	  =						"""huggingface-tools/default-prompts"""
__UpperCamelCase					:		str	  =						{"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def      a_ (      _A      ,   _A      ,   _A="run"  )			->			str:







				"""simple docstring"""
				if prompt_or_repo_id is None:
								snake_case__									=     DEFAULT_PROMPTS_REPO

				# prompt is considered a repo ID when it does not contain any kind of space
				if re.search('\\s'      ,   _A  ) is not None:
								return prompt_or_repo_id

				snake_case__									=     cached_file(
				    _A      ,   PROMPT_FILES[mode]      ,   repo_type='dataset'      ,   user_agent={'agent': agent_name}  )
				with open(_A      ,   'r'      ,   encoding='utf-8'  ) as f:
								return f.read()












from typing import TYPE_CHECKING

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


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




