"""simple docstring"""
import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()



class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  StableUnCLIPPipeline
				_lowerCAmelCase       :      Dict     					=  TEXT_TO_IMAGE_PARAMS
				_lowerCAmelCase       :      Tuple     					=  TEXT_TO_IMAGE_BATCH_PARAMS
				_lowerCAmelCase       :      int     					=  TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowerCAmelCase       :      Dict     					=  TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				_lowerCAmelCase       :      Optional[int]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     32
										snake_case        =     embedder_hidden_size

										# prior components

										torch.manual_seed(0      )
										snake_case        =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

										torch.manual_seed(0      )
										snake_case        =     CLIPTextModelWithProjection(
										    CLIPTextConfig(
										        bos_token_id=0						,      eos_token_id=2						,      hidden_size=lowerCAmelCase						,      projection_dim=lowerCAmelCase						,      intermediate_size=37						,      layer_norm_eps=1E-05						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=10_00						,      )      )

										torch.manual_seed(0      )
										snake_case        =     PriorTransformer(
										    num_attention_heads=2						,      attention_head_dim=12						,      embedding_dim=lowerCAmelCase						,      num_layers=1						,      )

										torch.manual_seed(0      )
										snake_case        =     DDPMScheduler(
										    variance_type='fixed_small_log'						,      prediction_type='sample'						,      num_train_timesteps=10_00						,      clip_sample=lowerCAmelCase						,      clip_sample_range=5.0						,      beta_schedule='squaredcos_cap_v2'						,      )

										# regular denoising components

										torch.manual_seed(0      )
										snake_case        =     StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase      )
										snake_case        =     DDPMScheduler(beta_schedule='squaredcos_cap_v2'      )

										torch.manual_seed(0      )
										snake_case        =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

										torch.manual_seed(0      )
										snake_case        =     CLIPTextModel(
										    CLIPTextConfig(
										        bos_token_id=0						,      eos_token_id=2						,      hidden_size=lowerCAmelCase						,      projection_dim=32						,      intermediate_size=37						,      layer_norm_eps=1E-05						,      num_attention_heads=4						,      num_hidden_layers=5						,      pad_token_id=1						,      vocab_size=10_00						,      )      )

										torch.manual_seed(0      )
										snake_case        =     UNetaDConditionModel(
										    sample_size=32						,      in_channels=4						,      out_channels=4						,      down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')						,      up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')						,      block_out_channels=(32, 64)						,      attention_head_dim=(2, 4)						,      class_embed_type='projection'						,      projection_class_embeddings_input_dim=embedder_projection_dim * 2						,      cross_attention_dim=lowerCAmelCase						,      layers_per_block=1						,      upcast_attention=lowerCAmelCase						,      use_linear_projection=lowerCAmelCase						,      )

										torch.manual_seed(0      )
										snake_case        =     DDIMScheduler(
										    beta_schedule='scaled_linear'						,      beta_start=0.0_00_85						,      beta_end=0.0_12						,      prediction_type='v_prediction'						,      set_alpha_to_one=lowerCAmelCase						,      steps_offset=1						,      )

										torch.manual_seed(0      )
										snake_case        =     AutoencoderKL()

										snake_case        =     {
										    # prior components
										    'prior_tokenizer': prior_tokenizer,
										    'prior_text_encoder': prior_text_encoder,
										    'prior': prior,
										    'prior_scheduler': prior_scheduler,
										    # image noising components
										    'image_normalizer': image_normalizer,
										    'image_noising_scheduler': image_noising_scheduler,
										    # regular denoising components
										    'tokenizer': tokenizer,
										    'text_encoder': text_encoder,
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										}

										return components

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=0      ):






										"""simple docstring"""

										if str(lowerCAmelCase      ).startswith('mps'      ):
																snake_case        =     torch.manual_seed(lowerCAmelCase      )
										else:
																snake_case        =     torch.Generator(device=lowerCAmelCase      ).manual_seed(lowerCAmelCase      )
										snake_case        =     {
										    'prompt': 'A painting of a squirrel eating a burger',
										    'generator': generator,
										    'num_inference_steps': 2,
										    'prior_num_inference_steps': 2,
										    'output_type': 'numpy',
										}
										return inputs

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch_device == 'cpu'

										self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch_device in ['cpu', 'mps']

										self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase      )





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



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'      )

										snake_case        =     StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'						,      torch_dtype=torch.floataa      )
										pipe.to(lowerCAmelCase      )
										pipe.set_progress_bar_config(disable=lowerCAmelCase      )
										# stable unclip will oom when integration tests are run on a V100,
										# so turn on memory savings
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										snake_case        =     torch.Generator(device='cpu'      ).manual_seed(0      )
										snake_case        =     pipe('anime turle'						,      generator=lowerCAmelCase						,      output_type='np'      )

										snake_case        =     output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										snake_case        =     StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'						,      torch_dtype=torch.floataa      )
										snake_case        =     pipe.to(lowerCAmelCase      )
										pipe.set_progress_bar_config(disable=lowerCAmelCase      )
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										snake_case        =     pipe(
										    'anime turtle'						,      prior_num_inference_steps=2						,      num_inference_steps=2						,      output_type='np'						,      )

										snake_case        =     torch.cuda.max_memory_allocated()
										# make sure that less than 7 GB is allocated
										assert mem_bytes < 7 * 10**9



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


