






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 		UpperCAmelCase_						(	a__					,  a__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   List[str]						=					VQModel
						A     :   List[str]						=					"""sample"""






						@property
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=(32, 32)				) ->				Any:
								snake_case_				:							Dict						 =       4
								snake_case_				:							str						 =       3

								snake_case_				:							str						 =       floats_tensor((batch_size, num_channels) + sizes				).to(lowerCAmelCase__				)

								return {"sample": image}






						@property
						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								return (3, 32, 32)






						@property
						def       _lowerCAmelCase       (	self				) ->				int:
								return (3, 32, 32)






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							str						 =       {
								    "block_out_channels": [32, 64],
								    "in_channels": 3,
								    "out_channels": 3,
								    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
								    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "latent_channels": 3,
								}
								snake_case_				:							Union[str, Any]						 =       self.dummy_input
								return init_dict, inputs_dict






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								pass






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								pass






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Optional[int]						 =       VQModel.from_pretrained("fusing/vqgan-dummy"		,				output_loading_info=lowerCAmelCase__				)
								self.assertIsNotNone(lowerCAmelCase__				)
								self.assertEqual(len(loading_info["missing_keys"]				)		,				0				)

								model.to(lowerCAmelCase__				)
								snake_case_				:							Optional[int]						 =       model(**self.dummy_input				)

								assert image is not None, "Make sure output is not None"






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							List[str]						 =       VQModel.from_pretrained("fusing/vqgan-dummy"				)
								model.to(lowerCAmelCase__				).eval()

								torch.manual_seed(0				)
								if torch.cuda.is_available():
										torch.cuda.manual_seed_all(0				)

								snake_case_				:							int						 =       torch.randn(1		,				model.config.in_channels		,				model.config.sample_size		,				model.config.sample_size				)
								snake_case_				:							Union[str, Any]						 =       image.to(lowerCAmelCase__				)
								with torch.no_grad():
										snake_case_				:							Dict						 =       model(lowerCAmelCase__				).sample

								snake_case_				:							Tuple						 =       output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								snake_case_				:							Tuple						 =       torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]				)
								# fmt: on
								self.assertTrue(torch.allclose(lowerCAmelCase__		,				lowerCAmelCase__		,				atol=1e-3				)				)














import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							List[Any]			  =    logging.get_logger(__name__)

lowercase  :							Any			  =    {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Optional[int]						=					'sew-d'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=("p2c", "c2p")		,				_SCREAMING_SNAKE_CASE="layer_norm"		,				_SCREAMING_SNAKE_CASE="gelu_python"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-7		,				_SCREAMING_SNAKE_CASE=1e-5		,				_SCREAMING_SNAKE_CASE="group"		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,				_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=16		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.05		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE="mean"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=2		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[Any]:
								super().__init__(**_SCREAMING_SNAKE_CASE		,				pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       hidden_size
								snake_case_				:							Any						 =       feat_extract_norm
								snake_case_				:							Union[str, Any]						 =       feat_extract_activation
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[Any]						 =       conv_bias
								snake_case_				:							Union[str, Any]						 =       num_conv_pos_embeddings
								snake_case_				:							Tuple						 =       num_conv_pos_embedding_groups
								snake_case_				:							Optional[int]						 =       len(self.conv_dim				)
								snake_case_				:							str						 =       num_hidden_layers
								snake_case_				:							List[Any]						 =       intermediate_size
								snake_case_				:							List[Any]						 =       squeeze_factor
								snake_case_				:							Tuple						 =       max_position_embeddings
								snake_case_				:							Optional[int]						 =       position_buckets
								snake_case_				:							Union[str, Any]						 =       share_att_key
								snake_case_				:							Optional[int]						 =       relative_attention
								snake_case_				:							List[str]						 =       norm_rel_ebd
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       hidden_act
								snake_case_				:							Any						 =       num_attention_heads
								snake_case_				:							Dict						 =       hidden_dropout
								snake_case_				:							int						 =       attention_dropout
								snake_case_				:							Any						 =       activation_dropout
								snake_case_				:							Optional[Any]						 =       feat_proj_dropout
								snake_case_				:							Tuple						 =       final_dropout
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							Tuple						 =       feature_layer_norm_eps
								snake_case_				:							Any						 =       initializer_range
								snake_case_				:							Optional[int]						 =       vocab_size

								if (
								    (len(self.conv_stride				) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel				) != self.num_feat_extract_layers)
								    or (len(self.conv_dim				) != self.num_feat_extract_layers)
								):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f'''but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)'''
										    f'''= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'''				)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								snake_case_				:							List[str]						 =       apply_spec_augment
								snake_case_				:							List[Any]						 =       mask_time_prob
								snake_case_				:							str						 =       mask_time_length
								snake_case_				:							Optional[Any]						 =       mask_time_min_masks
								snake_case_				:							Optional[Any]						 =       mask_feature_prob
								snake_case_				:							str						 =       mask_feature_length
								snake_case_				:							Optional[int]						 =       mask_feature_min_masks

								# ctc loss
								snake_case_				:							Any						 =       ctc_loss_reduction
								snake_case_				:							Tuple						 =       ctc_zero_infinity

								# sequence classification
								snake_case_				:							int						 =       use_weighted_layer_sum
								snake_case_				:							List[Any]						 =       classifier_proj_size






						@property
						def       _lowerCAmelCase       (	self				) ->				Dict:
								return functools.reduce(operator.mul		,				self.conv_stride		,				1				)
