"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase     					=     logging.get_logger(__name__)

UpperCamelCase     					=     {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}
class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					List[Any]						      =							"""ernie"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=3_0_5_2_2       ,     _SCREAMING_SNAKE_CASE=7_6_8       ,     _SCREAMING_SNAKE_CASE=1_2       ,     _SCREAMING_SNAKE_CASE=1_2       ,     _SCREAMING_SNAKE_CASE=3_0_7_2       ,     _SCREAMING_SNAKE_CASE="gelu"       ,     _SCREAMING_SNAKE_CASE=0.1       ,     _SCREAMING_SNAKE_CASE=0.1       ,     _SCREAMING_SNAKE_CASE=5_1_2       ,     _SCREAMING_SNAKE_CASE=2       ,     _SCREAMING_SNAKE_CASE=3       ,     _SCREAMING_SNAKE_CASE=False       ,     _SCREAMING_SNAKE_CASE=0.0_2       ,     _SCREAMING_SNAKE_CASE=1E-12       ,     _SCREAMING_SNAKE_CASE=0       ,     _SCREAMING_SNAKE_CASE="absolute"       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Dict:
							super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )

							a_							:			List[str]     		=					vocab_size
							a_							:			Any     		=					hidden_size
							a_							:			List[str]     		=					num_hidden_layers
							a_							:			Tuple     		=					num_attention_heads
							a_							:			Optional[int]     		=					hidden_act
							a_							:			List[str]     		=					intermediate_size
							a_							:			List[str]     		=					hidden_dropout_prob
							a_							:			Dict     		=					attention_probs_dropout_prob
							a_							:			Union[str, Any]     		=					max_position_embeddings
							a_							:			int     		=					type_vocab_size
							a_							:			List[str]     		=					task_type_vocab_size
							a_							:			Optional[Any]     		=					use_task_id
							a_							:			Optional[Any]     		=					initializer_range
							a_							:			Tuple     		=					layer_norm_eps
							a_							:			List[str]     		=					position_embedding_type
							a_							:			List[str]     		=					use_cache
							a_							:			Union[str, Any]     		=					classifier_dropout




class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""







	@property
	def       A    (			self )    ->	Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
													a_							:			Optional[int]     		=					{0: "batch", 1: "choice", 2: "sequence"}
							else:
													a_							:			str     		=					{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							        ("task_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   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					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(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




