__lowerCAmelCase	:       Any						       =	frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__lowerCAmelCase	:       Optional[int]						       =	frozenset(["prompt", "negative_prompt"])

__lowerCAmelCase	:       Tuple						       =	frozenset([])

__lowerCAmelCase	:       str						       =	frozenset(["image"])

__lowerCAmelCase	:       int						       =	frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__lowerCAmelCase	:       Optional[Any]						       =	frozenset(["image"])

__lowerCAmelCase	:       int						       =	frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__lowerCAmelCase	:       str						       =	frozenset(["prompt", "image", "negative_prompt"])

__lowerCAmelCase	:       Union[str, Any]						       =	frozenset(
    [
        # Text guided image variation with an image mask
        "prompt",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__lowerCAmelCase	:       int						       =	frozenset(["prompt", "image", "mask_image", "negative_prompt"])

__lowerCAmelCase	:       Any						       =	frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__lowerCAmelCase	:       Union[str, Any]						       =	frozenset(["image", "mask_image"])

__lowerCAmelCase	:       str						       =	frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__lowerCAmelCase	:       int						       =	frozenset(["example_image", "image", "mask_image"])

__lowerCAmelCase	:       int						       =	frozenset(["class_labels"])

__lowerCAmelCase	:       List[str]						       =	frozenset(["class_labels"])

__lowerCAmelCase	:       Optional[Any]						       =	frozenset(["batch_size"])

__lowerCAmelCase	:       Union[str, Any]						       =	frozenset([])

__lowerCAmelCase	:       Union[str, Any]						       =	frozenset(["batch_size"])

__lowerCAmelCase	:       str						       =	frozenset([])

__lowerCAmelCase	:       Optional[Any]						       =	frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__lowerCAmelCase	:       List[str]						       =	frozenset(["prompt", "negative_prompt"])
__lowerCAmelCase	:       int						       =	frozenset(["input_tokens"])

__lowerCAmelCase	:       Union[str, Any]						       =	frozenset(["input_tokens"])

'''simple docstring'''


from typing import TYPE_CHECKING

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


__lowerCAmelCase	:       Optional[Any]						       =	{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	[
					    "LlamaForCausalLM",
					    "LlamaModel",
					    "LlamaPreTrainedModel",
					    "LlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

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