




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class lowerCAmelCase__			(  _lowercase		):






   '''simple docstring'''



   lowerCamelCase__   =    '''mobilenet_v1'''
   def __init__(						self						,   lowercase=3						,   lowercase=224						,   lowercase=1.0						,   lowercase=8						,   lowercase="relu6"						,   lowercase=True						,   lowercase=0.9_99						,   lowercase=0.02						,   lowercase=0.0_01						,   **lowercase						,   ):
       super().__init__(**A_						)

       if depth_multiplier <= 0:
           raise ValueError('depth_multiplier must be greater than zero.'						)

       _lowerCamelCase  :    List[Any]					=					num_channels
       _lowerCamelCase  :    Dict					=					image_size
       _lowerCamelCase  :    List[str]					=					depth_multiplier
       _lowerCamelCase  :    Optional[Any]					=					min_depth
       _lowerCamelCase  :    Optional[Any]					=					hidden_act
       _lowerCamelCase  :    Optional[Any]					=					tf_padding
       _lowerCamelCase  :    List[Any]					=					classifier_dropout_prob
       _lowerCamelCase  :    Union[str, Any]					=					initializer_range
       _lowerCamelCase  :    Any					=					layer_norm_eps






class lowerCAmelCase__			(  _lowercase		):






   '''simple docstring'''



   lowerCamelCase__   =    version.parse("""1.11"""		)
   @property
   def       A_					(						self						):
       return OrderedDict([('pixel_values', {0: 'batch'})]						)
   @property
   def       A_					(						self						):
       if self.task == "image-classification":
           return OrderedDict([('logits', {0: 'batch'})]						)
       else:
           return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]						)






   @property
   def       A_					(						self						):
       return 1E-4





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available






@dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    42


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipeline_text_to_video_synth import TextToVideoSDPipeline
			from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
			from .pipeline_text_to_video_zero import TextToVideoZeroPipeline