


"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase      		=			{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase      		=			['''PerceiverFeatureExtractor''']
	UpperCAmelCase      		=			['''PerceiverImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase      		=			[
	    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PerceiverForImageClassificationConvProcessing''',
	    '''PerceiverForImageClassificationFourier''',
	    '''PerceiverForImageClassificationLearned''',
	    '''PerceiverForMaskedLM''',
	    '''PerceiverForMultimodalAutoencoding''',
	    '''PerceiverForOpticalFlow''',
	    '''PerceiverForSequenceClassification''',
	    '''PerceiverLayer''',
	    '''PerceiverModel''',
	    '''PerceiverPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
	from .tokenization_perceiver import PerceiverTokenizer

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_perceiver import PerceiverFeatureExtractor
		from .image_processing_perceiver import PerceiverImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_perceiver import (
		    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PerceiverForImageClassificationConvProcessing,
		    PerceiverForImageClassificationFourier,
		    PerceiverForImageClassificationLearned,
		    PerceiverForMaskedLM,
		    PerceiverForMultimodalAutoencoding,
		    PerceiverForOpticalFlow,
		    PerceiverForSequenceClassification,
		    PerceiverLayer,
		    PerceiverModel,
		    PerceiverPreTrainedModel,
		)

else:
	import sys

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





"""simple docstring"""



# 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      		=			'''
Human: <<task>>

Assistant: '''


UpperCAmelCase      		=			'''huggingface-tools/default-prompts'''
UpperCAmelCase      		=			{'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def       lowerCamelCase					(a_						:int				,				a_						:str				,				a_						:Dict="run")     ->							Optional[Any]:
       if prompt_or_repo_id is None:
              lowercase		:Tuple       							=				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

       lowercase		:List[str]       							=				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()

