






"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =DiTPipeline
							_SCREAMING_SNAKE_CASE    =CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							_SCREAMING_SNAKE_CASE    =PipelineTesterMixin.required_optional_params - {
							    'latents',
							    'num_images_per_prompt',
							    'callback',
							    'callback_steps',
							}
							_SCREAMING_SNAKE_CASE    =CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												torch.manual_seed(0    )
												a__          =  TransformeraDModel(
												    sample_size=16				,		num_layers=2				,		patch_size=4				,		attention_head_dim=8				,		num_attention_heads=2				,		in_channels=4				,		out_channels=8				,		attention_bias=__A				,		activation_fn='''gelu-approximate'''				,		num_embeds_ada_norm=1000				,		norm_type='''ada_norm_zero'''				,		norm_elementwise_affine=__A				,		)
												a__          =  AutoencoderKL()
												a__          =  DDIMScheduler()
												a__          =  {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
												return components




							def  lowercase		(   self:     Tuple				,		__A:     List[str]				,		__A:     Any=0    ):

												'''simple docstring'''

												if str(__A    ).startswith('''mps'''    ):
																	a__          =  torch.manual_seed(__A    )
												else:
																	a__          =  torch.Generator(device=__A    ).manual_seed(__A    )
												a__          =  {
												    '''class_labels''': [1],
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}
												return inputs




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  '''cpu'''

												a__          =  self.get_dummy_components()
												a__          =  self.pipeline_class(**__A    )
												pipe.to(__A    )
												pipe.set_progress_bar_config(disable=__A    )

												a__          =  self.get_dummy_inputs(__A    )
												a__          =  pipe(**__A    ).images
												a__          =  image[0, -3:, -3:, -1]

												self.assertEqual(image.shape				,		(1, 16, 16, 3)    )
												a__          =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]    )
												a__          =  np.abs(image_slice.flatten() - expected_slice    ).max()
												self.assertLessEqual(__A				,		1e-3    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												self._test_inference_batch_single_identical(relax_max_difference=__A				,		expected_max_diff=1e-3    )





							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()				,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,		)
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







@require_torch_gpu
@slow
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  torch.manual_seed(0    )

												a__          =  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''    )
												pipe.to('''cuda'''    )

												a__          =  ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
												a__          =  pipe.get_label_ids(__A    )

												a__          =  pipe(__A				,		generator=__A				,		num_inference_steps=40				,		output_type='''np'''    ).images

												for word, image in zip(__A				,		__A    ):
																	a__          =  load_numpy(
																	    F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'    )
																	assert np.abs((expected_image - image).max()    ) < 1e-2





							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''    )
												a__          =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
												pipe.to('''cuda'''    )

												a__          =  ['''vase''', '''umbrella''']
												a__          =  pipe.get_label_ids(__A    )

												a__          =  torch.manual_seed(0    )
												a__          =  pipe(__A				,		generator=__A				,		num_inference_steps=25				,		output_type='''np'''    ).images

												for word, image in zip(__A				,		__A    ):
																	a__          =  load_numpy(
																	    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
																	    F'/dit/{word}_512.npy'    )

																	assert np.abs((expected_image - image).max()    ) < 1e-1










"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


__a       :	str             = logging.get_logger(__name__)

__a       :	Dict             = {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='xlm'
							_SCREAMING_SNAKE_CASE    ={
							    'hidden_size': 'emb_dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							    'n_words': 'vocab_size',  # For backward compatibility
							}




							def __init__(   self:     Optional[Any]				,		__A:     str=30145				,		__A:     Union[str, Any]=2048				,		__A:     Union[str, Any]=12				,		__A:     List[Any]=16				,		__A:     List[Any]=0.1				,		__A:     int=0.1				,		__A:     str=True				,		__A:     int=False				,		__A:     Optional[int]=False				,		__A:     Union[str, Any]=False				,		__A:     List[Any]=1				,		__A:     Dict=True				,		__A:     List[Any]=512				,		__A:     Optional[int]=2048**-0.5				,		__A:     Tuple=1e-12				,		__A:     Optional[int]=0.0_2				,		__A:     Optional[int]=0				,		__A:     Union[str, Any]=1				,		__A:     List[Any]=2				,		__A:     Union[str, Any]=3				,		__A:     Tuple=5				,		__A:     Tuple=True				,		__A:     Dict="first"				,		__A:     Optional[int]=True				,		__A:     int=None				,		__A:     Optional[Any]=True				,		__A:     Dict=0.1				,		__A:     Tuple=5				,		__A:     Union[str, Any]=5				,		__A:     Tuple=0				,		__A:     List[str]=0				,		__A:     int=2				,		__A:     Optional[int]=0				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  vocab_size
												a__          =  emb_dim
												a__          =  n_layers
												a__          =  n_heads
												a__          =  dropout
												a__          =  attention_dropout
												a__          =  gelu_activation
												a__          =  sinusoidal_embeddings
												a__          =  causal
												a__          =  asm
												a__          =  n_langs
												a__          =  use_lang_emb
												a__          =  layer_norm_eps
												a__          =  bos_index
												a__          =  eos_index
												a__          =  pad_index
												a__          =  unk_index
												a__          =  mask_index
												a__          =  is_encoder
												a__          =  max_position_embeddings
												a__          =  embed_init_std
												a__          =  init_std
												a__          =  summary_type
												a__          =  summary_use_proj
												a__          =  summary_activation
												a__          =  summary_proj_to_labels
												a__          =  summary_first_dropout
												a__          =  start_n_top
												a__          =  end_n_top
												a__          =  mask_token_id
												a__          =  lang_id

												if "n_words" in kwargs:
																	a__          =  kwargs['''n_words''']

												super().__init__(pad_token_id=__A				,		bos_token_id=__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							@property
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												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),
												    ]    )


