
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__				=							{
    """configuration_musicgen""": [
        """MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MusicgenConfig""",
        """MusicgenDecoderConfig""",
    ],
    """processing_musicgen""": ["""MusicgenProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__				=							[
							    """MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MusicgenForConditionalGeneration""",
							    """MusicgenForCausalLM""",
							    """MusicgenModel""",
							    """MusicgenPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_musicgen import (
							    MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MusicgenConfig,
							    MusicgenDecoderConfig,
							)
							from .processing_musicgen import MusicgenProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_musicgen import (
														    MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MusicgenForCausalLM,
														    MusicgenForConditionalGeneration,
														    MusicgenModel,
														    MusicgenPreTrainedModel,
														)

else:
							import sys

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







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor





