from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					'rembert'




 def __init__(			self					:  List[Any]			,    lowercase__					:  int=250_300			,    lowercase__					:  List[str]=1_152			,    lowercase__					:  List[str]=32			,    lowercase__					:  Tuple=18			,    lowercase__					:  Tuple=256			,    lowercase__					:  List[Any]=1_664			,    lowercase__					:  List[str]=4_608			,    lowercase__					:  Optional[Any]="gelu"			,    lowercase__					:  str=0.0			,    lowercase__					:  Any=0.0			,    lowercase__					:  Dict=0.1			,    lowercase__					:  List[str]=512			,    lowercase__					:  Optional[int]=2			,    lowercase__					:  List[Any]=0.02			,    lowercase__					:  Union[str, Any]=1e-12			,    lowercase__					:  str=True			,    lowercase__					:  int=0			,    lowercase__					:  List[Any]=312			,    lowercase__					:  int=313			,    **lowercase__					:  Dict			,    ):







        '''simple docstring'''



        super().__init__(pad_token_id=lowercase__			,    bos_token_id=lowercase__			,    eos_token_id=lowercase__			,    **lowercase__)

        lowerCAmelCase__     	=					vocab_size
        lowerCAmelCase__     	=					input_embedding_size
        lowerCAmelCase__     	=					output_embedding_size
        lowerCAmelCase__     	=					max_position_embeddings
        lowerCAmelCase__     	=					hidden_size
        lowerCAmelCase__     	=					num_hidden_layers
        lowerCAmelCase__     	=					num_attention_heads
        lowerCAmelCase__     	=					intermediate_size
        lowerCAmelCase__     	=					hidden_act
        lowerCAmelCase__     	=					hidden_dropout_prob
        lowerCAmelCase__     	=					attention_probs_dropout_prob
        lowerCAmelCase__     	=					classifier_dropout_prob
        lowerCAmelCase__     	=					initializer_range
        lowerCAmelCase__     	=					type_vocab_size
        lowerCAmelCase__     	=					layer_norm_eps
        lowerCAmelCase__     	=					use_cache
        lowerCAmelCase__     	=					False






class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 @property
 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



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




 @property
 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



        return 1e-4








import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def 					__A       (     self:      Optional[Any]						,   __A:      bool = False      )			-> Union[str, Any]:
												_A									=       10

												if only_cross_attention:
																	_A									=       12
												else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_A									=       query_dim

												return {
												    "query_dim": query_dim,
												    "cross_attention_dim": cross_attention_dim,
												    "heads": 2,
												    "dim_head": 4,
												    "added_kv_proj_dim": 6,
												    "norm_num_groups": 1,
												    "only_cross_attention": only_cross_attention,
												    "processor": AttnAddedKVProcessor(),
												}

							def 					__A       (     self:      List[Any]						,   __A:      str						,   __A:      Union[str, Any]      )			-> Union[str, Any]:
												_A									=       2

												_A									=       torch.rand(__A						,   __A						,   3						,   2      )
												_A									=       torch.rand(__A						,   4						,   __A      )
												_A									=       None

												return {
												    "hidden_states": hidden_states,
												    "encoder_hidden_states": encoder_hidden_states,
												    "attention_mask": attention_mask,
												}



							def 					__A       (     self:      Optional[int]      )			-> Any:
												# self and cross attention

												torch.manual_seed(0      )

												_A									=       self.get_constructor_arguments(only_cross_attention=__A      )
												_A									=       Attention(**__A      )

												self.assertTrue(attn.to_k is not None      )
												self.assertTrue(attn.to_v is not None      )

												_A									=       self.get_forward_arguments(
												    query_dim=constructor_args['''query_dim''']						,   added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']      )

												_A									=       attn(**__A      )

												# only self attention

												torch.manual_seed(0      )

												_A									=       self.get_constructor_arguments(only_cross_attention=__A      )
												_A									=       Attention(**__A      )

												self.assertTrue(attn.to_k is None      )
												self.assertTrue(attn.to_v is None      )

												_A									=       self.get_forward_arguments(
												    query_dim=constructor_args['''query_dim''']						,   added_kv_proj_dim=constructor_args['''added_kv_proj_dim''']      )

												_A									=       attn(**__A      )

												self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							def 					__A       (     self:      Any      )			-> Dict:
												_A									=       DiffusionPipeline.from_pretrained('''hf-internal-testing/tiny-stable-diffusion-pipe'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												# the initial conversion succeeds
												_A									=       DiffusionPipeline.from_pretrained(
												    '''hf-internal-testing/tiny-stable-diffusion-pipe'''						,   device_map='''sequential'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												with tempfile.TemporaryDirectory() as tmpdir:
																	# save the converted model
																	pipe.save_pretrained(__A      )

																	# can also load the converted weights
																	_A									=       DiffusionPipeline.from_pretrained(__A						,   device_map='''sequential'''						,   safety_checker=__A      )

												_A									=       pipe(
												    '''foo'''						,   num_inference_steps=2						,   generator=torch.Generator('''cpu'''      ).manual_seed(0      )						,   output_type='''np'''						,   ).images

												self.assertTrue(np.allclose(__A						,   __A						,   atol=1e-5      )      )
												self.assertTrue(np.allclose(__A						,   __A						,   atol=1e-5      )      )



