






from collections import OrderedDict
from typing import Mapping

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


snake_case_   :	Dict							      =logging.get_logger(__name__)

snake_case_   :	Optional[int]							      ={
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Any 					= SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		UpperCAmelCase_							:       Tuple 					= 'squeezebert'






		def __init__(      self							, lowercase__=30522							, lowercase__=768							, lowercase__=12							, lowercase__=12							, lowercase__=3072							, lowercase__="gelu"							, lowercase__=0.1							, lowercase__=0.1							, lowercase__=512							, lowercase__=2							, lowercase__=0.02							, lowercase__=1e-12							, lowercase__=0							, lowercase__=768							, lowercase__=4							, lowercase__=4							, lowercase__=4							, lowercase__=1							, lowercase__=4							, lowercase__=4							, **lowercase__							, )				->				str:
				super().__init__(pad_token_id=lowercase__							, **lowercase__						)

				__A    =							vocab_size
				__A    =							hidden_size
				__A    =							num_hidden_layers
				__A    =							num_attention_heads
				__A    =							hidden_act
				__A    =							intermediate_size
				__A    =							hidden_dropout_prob
				__A    =							attention_probs_dropout_prob
				__A    =							max_position_embeddings
				__A    =							type_vocab_size
				__A    =							initializer_range
				__A    =							layer_norm_eps
				__A    =							embedding_size
				__A    =							q_groups
				__A    =							k_groups
				__A    =							v_groups
				__A    =							post_attention_groups
				__A    =							intermediate_groups
				__A    =							output_groups





class        a__      (	lowerCAmelCase__		):






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


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 __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """runwayml/stable-diffusion-v1-5"""
     SCREAMING_SNAKE_CASE__     				= TextToVideoZeroPipeline.from_pretrained(_lowercase			,      torch_dtype=torch.floataa ).to("""cuda""" )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_config(pipe.scheduler.config )
     SCREAMING_SNAKE_CASE__     				= torch.Generator(device="""cuda""" ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__     				= """A bear is playing a guitar on Times Square"""
     SCREAMING_SNAKE_CASE__     				= pipe(prompt=_lowercase			,      generator=_lowercase ).images

     SCREAMING_SNAKE_CASE__     				= 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(_lowercase			,      _lowercase )
