






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase     :     Union[str, Any]            =	logging.get_logger(__name__)

_lowerCAmelCase     :     List[str]            =	{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   SCREAMING_SNAKE_CASE_					      ='''mobilebert'''
   def __init__(						self	:     List[str]				,       snake_case__	:     List[Any]=3_0_5_2_2				,       snake_case__	:     Optional[int]=5_1_2				,       snake_case__	:     List[str]=2_4				,       snake_case__	:     Optional[Any]=4				,       snake_case__	:     Optional[int]=5_1_2				,       snake_case__	:     List[Any]="relu"				,       snake_case__	:     int=0.0				,       snake_case__	:     Union[str, Any]=0.1				,       snake_case__	:     List[Any]=5_1_2				,       snake_case__	:     Union[str, Any]=2				,       snake_case__	:     str=0.02				,       snake_case__	:     Optional[Any]=1e-12				,       snake_case__	:     int=0				,       snake_case__	:     List[str]=1_2_8				,       snake_case__	:     str=True				,       snake_case__	:     int=True				,       snake_case__	:     Dict=1_2_8				,       snake_case__	:     Union[str, Any]=False				,       snake_case__	:     int=True				,       snake_case__	:     Tuple=4				,       snake_case__	:     Optional[Any]="no_norm"				,       snake_case__	:     List[str]=True				,       snake_case__	:     str=None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__				,       **snake_case__				)

        UpperCAmelCase__		:  int				=				vocab_size
        UpperCAmelCase__		:  Union[str, Any]				=				hidden_size
        UpperCAmelCase__		:  Tuple				=				num_hidden_layers
        UpperCAmelCase__		:  str				=				num_attention_heads
        UpperCAmelCase__		:  Any				=				hidden_act
        UpperCAmelCase__		:  List[Any]				=				intermediate_size
        UpperCAmelCase__		:  Any				=				hidden_dropout_prob
        UpperCAmelCase__		:  Optional[Any]				=				attention_probs_dropout_prob
        UpperCAmelCase__		:  Optional[int]				=				max_position_embeddings
        UpperCAmelCase__		:  Union[str, Any]				=				type_vocab_size
        UpperCAmelCase__		:  str				=				initializer_range
        UpperCAmelCase__		:  Optional[Any]				=				layer_norm_eps
        UpperCAmelCase__		:  Any				=				embedding_size
        UpperCAmelCase__		:  Dict				=				trigram_input
        UpperCAmelCase__		:  Dict				=				use_bottleneck
        UpperCAmelCase__		:  List[str]				=				intra_bottleneck_size
        UpperCAmelCase__		:  Union[str, Any]				=				use_bottleneck_attention
        UpperCAmelCase__		:  List[Any]				=				key_query_shared_bottleneck
        UpperCAmelCase__		:  Any				=				num_feedforward_networks
        UpperCAmelCase__		:  Optional[Any]				=				normalization_type
        UpperCAmelCase__		:  List[str]				=				classifier_activation

        if self.use_bottleneck:
             UpperCAmelCase__		:  int				=				intra_bottleneck_size
        else:
             UpperCAmelCase__		:  List[str]				=				hidden_size

        UpperCAmelCase__		:  List[str]				=				classifier_dropout




class        lowerCAmelCase__       (  __magic_name__					):
   @property
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        if self.task == "multiple-choice":
             UpperCAmelCase__		:  Optional[int]				=				{0: "batch", 1: "choice", 2: "sequence"}
        else:
             UpperCAmelCase__		:  Tuple				=				{0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]				)










"""simple docstring"""



import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				"runwayml/stable-diffusion-v1-5"
        UpperCAmelCase__		:  Optional[Any]				=				TextToVideoZeroPipeline.from_pretrained(snake_case__				,       torch_dtype=torch.floataa				).to("cuda"				)
        UpperCAmelCase__		:  Any				=				DDIMScheduler.from_config(pipe.scheduler.config				)
        UpperCAmelCase__		:  Optional[Any]				=				torch.Generator(device="cuda"				).manual_seed(0				)

        UpperCAmelCase__		:  Any				=				"A bear is playing a guitar on Times Square"
        UpperCAmelCase__		:  Dict				=				pipe(prompt=snake_case__				,       generator=snake_case__				).images

        UpperCAmelCase__		:  List[Any]				=				load_pt(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"				)

        assert_mean_pixel_difference(snake_case__				,       snake_case__				)


