



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()







class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					PriorTransformer
			UpperCamelCase        =					'''hidden_states'''



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       4
						_UpperCAmelCase			=       8
						_UpperCAmelCase			=       7

						_UpperCAmelCase			=       floats_tensor((batch_size, embedding_dim)).to(A)

						_UpperCAmelCase			=       floats_tensor((batch_size, embedding_dim)).to(A)
						_UpperCAmelCase			=       floats_tensor((batch_size, num_embeddings, embedding_dim)).to(A)

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Union[str, Any]=0)       ->   List[Any]:



						"""simple docstring"""






						torch.manual_seed(A)
						_UpperCAmelCase			=       4
						_UpperCAmelCase			=       8
						_UpperCAmelCase			=       7

						_UpperCAmelCase			=       torch.randn((batch_size, embedding_dim)).to(A)

						_UpperCAmelCase			=       torch.randn((batch_size, embedding_dim)).to(A)
						_UpperCAmelCase			=       torch.randn((batch_size, num_embeddings, embedding_dim)).to(A)

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






						return (4, 8)



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Union[str, Any]:



						"""simple docstring"""






						return (4, 8)



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       {
						    'num_attention_heads': 2,
						    'attention_head_dim': 4,
						    'num_layers': 2,
						    'embedding_dim': 8,
						    'num_embeddings': 7,
						    'additional_embeddings': 4,
						}
						_UpperCAmelCase			=       self.dummy_input
						return init_dict, inputs_dict



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       PriorTransformer.from_pretrained(
						    'hf-internal-testing/prior-dummy'			,		output_loading_info=A)
						self.assertIsNotNone(A)
						self.assertEqual(len(loading_info['missing_keys'])			,		0)

						model.to(A)
						_UpperCAmelCase			=       model(**self.dummy_input)[0]

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



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.prepare_init_args_and_inputs_for_common()

						_UpperCAmelCase			=       self.model_class(**A)
						_UpperCAmelCase			=       inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_UpperCAmelCase			=       [*signature.parameters.keys()]

						_UpperCAmelCase			=       ['hidden_states', 'timestep']
						self.assertListEqual(arg_names[:2]			,		A)



			def    _lowerCamelCase	(						self       :  Any)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy')
						_UpperCAmelCase			=       model.to(A)

						if hasattr(A			,		'set_default_attn_processor'):
									model.set_default_attn_processor()

						_UpperCAmelCase			=       self.get_dummy_seed_input()

						with torch.no_grad():
									_UpperCAmelCase			=       model(**A)[0]

						_UpperCAmelCase			=       output[0, :5].flatten().cpu()
						print(A)

						# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
						# the expected output slices are not the same for CPU and GPU.
						_UpperCAmelCase			=       torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9])
						self.assertTrue(torch_all_close(A			,		A			,		rtol=1E-2))







@slow
class       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  List[Any]=1			,		A       :  Union[str, Any]=7_68			,		A       :  Any=77			,		A       :  Union[str, Any]=0)       ->   Any:



						"""simple docstring"""






						torch.manual_seed(A)
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       embedding_dim
						_UpperCAmelCase			=       num_embeddings

						_UpperCAmelCase			=       torch.randn((batch_size, embedding_dim)).to(A)

						_UpperCAmelCase			=       torch.randn((batch_size, embedding_dim)).to(A)
						_UpperCAmelCase			=       torch.randn((batch_size, num_embeddings, embedding_dim)).to(A)

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
			        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
			        # fmt: on
			    ])
			def    _lowerCamelCase	(						self       :  str			,		A       :  Any			,		A       :  Tuple)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'			,		subfolder='prior')
						model.to(A)
						_UpperCAmelCase			=       self.get_dummy_seed_input(seed=A)

						with torch.no_grad():
									_UpperCAmelCase			=       model(**A)[0]

						assert list(sample.shape) == [1, 7_68]

						_UpperCAmelCase			=       sample[0, :8].flatten().cpu()
						print(A)
						_UpperCAmelCase			=       torch.tensor(A)

						assert torch_all_close(A			,		A			,		atol=1E-3)





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__       =	{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''xmod'''



			def __init__(						self       :  Optional[int]			,		A       :  int=3_05_22			,		A       :  Tuple=7_68			,		A       :  Optional[Any]=12			,		A       :  Tuple=12			,		A       :  str=30_72			,		A       :  List[str]="gelu"			,		A       :  Any=0.1			,		A       :  int=0.1			,		A       :  Dict=5_12			,		A       :  Optional[Any]=2			,		A       :  Optional[Any]=0.0_2			,		A       :  List[Any]=1E-12			,		A       :  int=1			,		A       :  Tuple=0			,		A       :  Optional[Any]=2			,		A       :  int="absolute"			,		A       :  Union[str, Any]=True			,		A       :  List[Any]=None			,		A       :  Optional[Any]=False			,		A       :  List[str]=2			,		A       :  int=False			,		A       :  str=True			,		A       :  Optional[Any]=True			,		A       :  Tuple=("en_XX",)			,		A       :  Optional[int]=None			,		**A       :  List[str]			,		)       ->   Dict:



						"""simple docstring"""






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

						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       layer_norm_eps
						_UpperCAmelCase			=       position_embedding_type
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       classifier_dropout
						_UpperCAmelCase			=       pre_norm
						_UpperCAmelCase			=       adapter_reduction_factor
						_UpperCAmelCase			=       adapter_layer_norm
						_UpperCAmelCase			=       adapter_reuse_layer_norm
						_UpperCAmelCase			=       ln_before_adapter
						_UpperCAmelCase			=       list(A)
						_UpperCAmelCase			=       default_language







class       __lowerCAmelCase   (      A						):



			@property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Mapping[str, Mapping[int, str]]:



						"""simple docstring"""






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